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Sample records for clinical parameters predicting

  1. Prediction of labor induction outcome using different clinical parameters

    OpenAIRE

    Tatić-Stupar Žaklina; Novakov-Mikić Aleksandra; Bogavac Mirjana; Milatović Stevan; Sekulić Slobodan

    2013-01-01

    Introduction. Induction of labor is one of the most common obstetric interventions in contemporary obstetrics. Objective. The aim of the study was to evaluate the clinical and sonographic parameters in prediction of success of labor induction. Methods. The prospective study included 422 women in whom induction of labor was carried out at the Department of Obstetrics and Gynecology of Clinical Centre of Vojvodina. The role of body mass index and age of women...

  2. Prediction of labor induction outcome using different clinical parameters

    Directory of Open Access Journals (Sweden)

    Tatić-Stupar Žaklina

    2013-01-01

    Full Text Available Introduction. Induction of labor is one of the most common obstetric interventions in contemporary obstetrics. Objective. The aim of the study was to evaluate the clinical and sonographic parameters in prediction of success of labor induction. Methods. The prospective study included 422 women in whom induction of labor was carried out at the Department of Obstetrics and Gynecology of Clinical Centre of Vojvodina. The role of body mass index and age of women, parity Bishop score, cervical length measured by transvaginal ultrasound was evaluated in regard of the success of induction, which was considered successful if a vaginal delivery occurred within 24 hours after the onset of induction. Data were statistically analyzed by univariate statistical analysis and Pearson’s χ2 test. Results. Out of 422 women, induction of labor was successful in 356 (84.4%, and it failed in 66 (15.6% cases. The values of Bishop score and cervical length had positive correlation with the success of induction. Conclusion. Bishop score and transvaginal cervical length were both reliable predictors in determining the success of labor induction, as well as parity and BMI. These parameters are mostly complementary, not competitive in prediction of labor induction success.

  3. Clinical parameters predictive of malignancy of thyroid follicular neoplasms

    International Nuclear Information System (INIS)

    Needle aspiration biopsy is commonly employed in the evaluation of thyroid nodules. Unfortunately, the cytologic finding of a 'follicular neoplasm' does not distinguish between a thyroid adenoma and a follicular cancer. The purpose of this study was to identify clinical parameters that characterize patients with an increased risk of having a thyroid follicular cancer who preoperatively have a 'follicular neoplasm' identified by needle aspiration biopsy. A total of 395 patients initially treated at Vancouver General Hospital and the British Columbia Cancer Agency between the years of 1965 and 1985 were identified and their data were entered into a computer database. Patients with thyroid adenomas were compared to patients with follicular cancer using the chi-square test and Student's t-test. Statistically significant parameters that distinguished patients at risk of having a thyroid cancer (p less than 0.05) included age greater than 50 years, nodule size greater than 3 cm, and a history of neck irradiation. Sex, family history of goiter or neoplasm, alcohol and tobacco use, and use of exogenous estrogen were not significant parameters. Patients can be identified preoperatively to be at an increased risk of having a follicular cancer and accordingly appropriate surgical resection can be planned

  4. 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.)

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

    International Nuclear Information System (INIS)

    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.)

  6. Predicting transformers oil parameters

    OpenAIRE

    Shaban, K.; El-Hag, A.; Matveev, A.

    2009-01-01

    In this paper different configurations of artificial neural networks are applied to predict various transformers oil parameters. The prediction is performed through modeling the relationship between the transformer insulation resistance extracted from the Megger test and the breakdown strength, interfacial tension, acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using two different configurations of neural networks....

  7. The Usefulness of Clinical and Laboratory Parameters for Predicting Severity of Dehydration in Children with Acute Gastroenteritis

    OpenAIRE

    Hoxha, Teuta Faik; Azemi, Mehmedali; Avdiu, Muharrem; Ismaili-Jaha, Vlora; Grajqevci, Violeta; Petrela, Ela

    2014-01-01

    ABSTRACT Background: An accurate assessment of the degree of dehydration in infants and children is important for proper decision-making and treatment. This emphasizes the need for laboratory tests to improve the accuracy of clinical assessment of dehydration. The aim of this study was to assess the relationship between clinical and laboratory parameters in the assessment of dehydration. Methods: We evaluated prospectively 200 children aged 1 month to 5 years who presented with diarrhea, vomi...

  8. Neurologic dysfunction in patients with rheumatoid arthritis of the cervical spine. Predictive value of clinical, radiographic and MR imaging parameters

    Energy Technology Data Exchange (ETDEWEB)

    Reijnierse, M.; Kroon, H.M.; Holscher, H.C.; Bloem, J.L. [Dept. of Radiology, University Hospital Leiden (Netherlands); Dijkmans, B.A.C.; Breedveld, F.C. [Dept. of Rheumatology, University Hospital Leiden (Netherlands); Hansen, B. [Dept. of Medical Statistics, University Hospital Leiden (Netherlands); Pope, T.L. [Dept. of Diagnostic Radiology, Univ. of South Carolina (United States)

    2001-03-01

    The aim of this study was to evaluate if subjective symptoms, radiographic and especially MR parameters of cervical spine involvement, can predict neurologic dysfunction in patients with severe rheumatoid arthritis (RA). Sequential radiographs, MR imaging, and neurologic examination were performed yearly in 46 consecutive RA patients with symptoms indicative of cervical spine involvement. Radiographic parameters were erosions of the dens or intervertebral joints, disc-space narrowing, horizontal and vertical atlantoaxial subluxation, subluxations below C2, and the diameter of the spinal canal. The MR features evaluated were presence of dens and atlas erosion, brainstem compression, subarachnoid space encroachment, pannus around the dens, abnormal fat body caudal to the clivus, cervicomedullary angle, and distance of the dens to the line of McRae. Muscle weakness was associated with a tenfold increased risk of neurologic dysfunction. Radiographic parameters were not associated. On MR images atlas erosion and a decreased distance of the dens to the line of McRae showed a fivefold increased risk of neurologic dysfunction. Subarachnoid space encroachment was associated with a 12-fold increased risk. Rheumatoid arthritis patients with muscle weakness and subarachnoid space encroachment of the entire cervical spine have a highly increased risk of developing neurologic dysfunction. (orig.)

  9. Neurologic dysfunction in patients with rheumatoid arthritis of the cervical spine. Predictive value of clinical, radiographic and MR imaging parameters

    International Nuclear Information System (INIS)

    The aim of this study was to evaluate if subjective symptoms, radiographic and especially MR parameters of cervical spine involvement, can predict neurologic dysfunction in patients with severe rheumatoid arthritis (RA). Sequential radiographs, MR imaging, and neurologic examination were performed yearly in 46 consecutive RA patients with symptoms indicative of cervical spine involvement. Radiographic parameters were erosions of the dens or intervertebral joints, disc-space narrowing, horizontal and vertical atlantoaxial subluxation, subluxations below C2, and the diameter of the spinal canal. The MR features evaluated were presence of dens and atlas erosion, brainstem compression, subarachnoid space encroachment, pannus around the dens, abnormal fat body caudal to the clivus, cervicomedullary angle, and distance of the dens to the line of McRae. Muscle weakness was associated with a tenfold increased risk of neurologic dysfunction. Radiographic parameters were not associated. On MR images atlas erosion and a decreased distance of the dens to the line of McRae showed a fivefold increased risk of neurologic dysfunction. Subarachnoid space encroachment was associated with a 12-fold increased risk. Rheumatoid arthritis patients with muscle weakness and subarachnoid space encroachment of the entire cervical spine have a highly increased risk of developing neurologic dysfunction. (orig.)

  10. Predicting Parameters in Deep Learning

    OpenAIRE

    Denil, Misha; Shakibi, Babak; Dinh, Laurent; Ranzato, Marc'Aurelio; De Freitas, Nando

    2013-01-01

    We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that not only can the parameter values be predicted, but many of them need not be learned at all. We train several different architectures by learning only a small number of weights and predicting the rest. In the best case we are able to predict more than 95% of...

  11. 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.

  12. 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

    International Nuclear Information System (INIS)

    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/LVaxial, RV/LV4-CH, and RV/LVvolume). 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/LVvolume ratio > 1.43 showed the highest area under the curve (AUC) (0.65) for the prediction of adverse clinical events when compared to RV/LVaxial, RV/LV4Ch and troponin I. The AUC for the detection of RVD of RV/LVaxial, RV/LV4Ch, RV/LVvolume, and troponin I were 0.86, 0.86, 0.92, and 0.69, respectively. Combination of RV/LVaxial, RV/LV4Ch, RV/LVvolume 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.

  13. 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)....

  14. Combined score using clinical, electrocardiographic, and echocardiographic parameters to predict left ventricular remodeling in patients having had cardiac resynchronization therapy six months earlier.

    Science.gov (United States)

    Brunet-Bernard, Anne; Maréchaux, Sylvestre; Fauchier, Laurent; Guiot, Aurélie; Fournet, Maxime; Reynaud, Amélie; Schnell, Frédéric; Leclercq, Christophe; Mabo, Philippe; Donal, Erwan

    2014-06-15

    The aim of this study was to evaluate whether a scoring system integrating clinical, electrocardiographic, and echocardiographic measurements can predict left ventricular reverse remodeling after cardiac resynchronization therapy (CRT). The derivation cohort consisted of 162 patients with heart failure implanted with a CRT device. Baseline clinical, electrocardiographic, and echocardiographic characteristics were entered into univariate and multivariate models to predict reverse remodeling as defined by a ≥15% reduction in left ventricular end-systolic volume at 6 months (60%). Combinations of predictors were then tested under different scoring systems. A new 7-point CRT response score termed L2ANDS2: Left bundle branch block (2 points), Age >70 years, Nonischemic origin, left ventricular end-diastolic Diameter 5 had a high positive likelihood ratio (+LR = 5.64), whereas a score <2 had a high negative likelihood ratio (-LR = 0.19). In conclusion, this L2ANDS2 score provides an easy-to-use tool for the clinician to assess the pretest probability of a patient being a CRT responder. PMID:24793667

  15. Clinical importance of predicting radiosensitivity

    International Nuclear Information System (INIS)

    Full text: The optimal use of radiation therapy in cancer treatment is hampered by the application of normal tissue tolerance limits that are derived from population averages. Such limits do not reflect the considerable differences in susceptibility to radiation injury that exist among individuals. Development of assays that accurately predicted normal tissue tolerance in individual patients would permit real application of the concept of treatment to tolerance. By adjusting doses upwards or downwards to achieve a uniform probability of complication in each patient, the therapeutic ratio, i e., the probability of an uncomplicated cure, would be increased for the population as a whole. Although the pathogenesis of radiation injury is highly complex, clinical studies have demonstrated a significant correlation between the in vitro radiosensitivity of patients' fibroblasts and their risk of developing late connective tissue type complications of radiotherapy. While such assays lack the precision and practicality to be used clinically, they do establish the principle of prediction of normal tissue tolerance. Newer assays using surrogate endpoints for cell survival and incorporating insights into the effects of radiation on cellular growth, differentiation, senescence and cytokine production are being developed. Such assays may, in the future, be complemented or replaced by molecular and/or cytogenetic probes to derive robust estimates of individual tolerance. The goal of accurate prediction of individual tolerance for clinical use, while not imminent, does seem achievable

  16. Clinicopathologic and gene expression parameters predict liver cancer prognosis

    International Nuclear Information System (INIS)

    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. 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. 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. 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

  17. 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.

  18. 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.

  19. 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

    International Nuclear Information System (INIS)

    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.)

  20. 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.)

  1. How to Establish Clinical Prediction Models.

    Science.gov (United States)

    Lee, Yong Ho; Bang, Heejung; Kim, Dae Jung

    2016-03-01

    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. PMID:26996421

  2. Clinical studies of biomarkers in suicide prediction

    OpenAIRE

    Jokinen, Jussi

    2007-01-01

    Suicide is a major clinical problem in psychiatry and suicidal behaviours can be seen as a nosological entity per se. Predicting suicide is difficult due to its low base-rate and the limited specificity of clinical predictors. Prospective biological studies suggest that dysfunctions in the hypothalamo pituitary adrenal (HPA) axis and the serotonergic system have predictive power for suicide in mood disorders. Suicide attempt is the most robust clinical predictor making suici...

  3. Outcome Prediction in Clinical Treatment Processes.

    Science.gov (United States)

    Huang, Zhengxing; Dong, Wei; Ji, Lei; Duan, Huilong

    2016-01-01

    Clinical outcome prediction, as strong implications for health service delivery of clinical treatment processes (CTPs), is important for both patients and healthcare providers. Prior studies typically use a priori knowledge, such as demographics or patient physical factors, to estimate clinical outcomes at early stages of CTPs (e.g., admission). They lack the ability to deal with temporal evolution of CTPs. In addition, most of the existing studies employ data mining or machine learning methods to generate a prediction model for a specific type of clinical outcome, however, a mathematical model that predicts multiple clinical outcomes simultaneously, has not yet been established. In this study, a hybrid approach is proposed to provide a continuous predictive monitoring service on multiple clinical outcomes. More specifically, a probabilistic topic model is applied to discover underlying treatment patterns of CTPs from electronic medical records. Then, the learned treatment patterns, as low-dimensional features of CTPs, are exploited for clinical outcome prediction across various stages of CTPs based on multi-label classification. The proposal is evaluated to predict three typical classes of clinical outcomes, i.e., length of stay, readmission time, and the type of discharge, using 3492 pieces of patients' medical records of the unstable angina CTP, extracted from a Chinese hospital. The stable model was characterized by 84.9% accuracy and 6.4% hamming-loss with 3 latent treatment patterns discovered from data, which outperforms the benchmark multi-label classification algorithms for clinical outcome prediction. Our study indicates the proposed approach can potentially improve the quality of clinical outcome prediction, and assist physicians to understand the patient conditions, treatment inventions, and clinical outcomes in an integrated view. PMID:26573645

  4. Meta-analysis of clinical prediction models

    NARCIS (Netherlands)

    Debray, T.P.A.

    2013-01-01

    The past decades there has been a clear shift from implicit to explicit diagnosis and prognosis. This includes appreciation of clinical -diagnostic and prognostic- prediction models, which is likely to increase with the introduction of fully computerized patient records. Prediction models aim to pro

  5. Representative parameter of immunostimulatory ginseng polysaccharide to predict radioprotection

    International Nuclear Information System (INIS)

    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

  6. Radiogenomics: predicting clinical normal tissue radiosensitivity

    DEFF Research Database (Denmark)

    Alsner, Jan

    2006-01-01

    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...

  7. Mining Clinical Data using Minimal Predictive Rules

    OpenAIRE

    Batal, Iyad; Hauskrecht, Milos

    2010-01-01

    Modern hospitals and health-care institutes collect huge amounts of clinical data. Those who deal with such data know that there is a widening gap between data collection and data comprehension. Thus, it is very important to develop data mining techniques capable of automatically extracting useful knowledge to support clinical decision-making in various diagnostic and patient-management tasks. In this paper, we develop a new framework for rule mining based on minimal predictive rules (MPR). O...

  8. Singular Parameter Prediction Algorithm for Bistable Neural Systems.

    Science.gov (United States)

    Durand, Dominique M; Jahangiri, Anila

    2010-04-01

    An algorithm is presented to predict the intensity and timing of a singular single stimulus required to switch the state of a bistable system from repetitive activity to a stable point. The algorithm is first tested on a modified Hodgkin-Huxley model to predict the parameters of a stimulus capable of annihilating the spontaneously occurring repetitive action potentials. Elevation of the potassium equilibrium potential causes oscillations in the V, m, h and n parameters and generates periodic activity. Equations describing the time-varying behavior of these parameters can be used to predict the pulse width, coupling interval and intensity of a single anodic pulse applied between two consecutive action potentials to suppress the activity. The algorithm was then applied to predict the singular parameters of quasi-periodic epileptiform activity generated in the hippocampus slice preparation exposed to high-potassium concentrations. The results indicate that a stimulus with the estimated parameters was able to either completely annihilate the action potentials in the HH model or predict the region of unpredictable latencies. Therefore this algorithm is capable a predicting singular parameters accurately when the model is known. In the case of an experimental system where the equations of the system are not known, the algorithm predicted parameters in the range of those observed experimentally. Therefore, the algorithm could reduce significantly the amount of time required to find the singular parameters of experimental bistable systems normally obtained by a systematic exploration of the parameter space. In particular, this algorithm could be useful to predict the singular parameters of quasi periodic epileptiform activity leading to the suppression of this activity if the system is bistable. PMID:21866209

  9. Clinical prediction rule for nonmelanoma skin cancer

    Directory of Open Access Journals (Sweden)

    John Alexander Nova

    2015-01-01

    Full Text Available Background: Skin cancer is the most frequent neoplasia in the world. Even though ultraviolet radiation is the main cause, established prevention campaigns have not proved to be effective for controlling the incidence of this disease. Objective: To develop clinical prediction rules based on medical consultation and a questionnaire to estimate the risk of developing nonmelanoma skin cancer. Methods: This study was developed in several steps. They were: Identifying risk factors that could be possible predictors of nonmelanoma skin cancer; their clinical validation; developing a prediction rule using logistic regression; and collecting information from 962 patients in a case and control design (481 cases and 481 controls. We developed independent prediction rules for basal cell and squamous cell carcinomas. Finally, we evaluated reliability for each of the variables. Results: The variables that made up the final prediction rule were: Family history of skin cancer, history of outdoor work, age, phototypes 1-3 and the presence of poikiloderma of civatte, actinic keratosis and conjunctivitis in band. Prediction rules specificity was 87% for basal cell carcinomas and 92% for squamous cell carcinomas. Inter- and intra-observer reliability was good except for the conjunctivitis in band variable. Conclusions: The prediction rules let us calculate the individual risk of developing basal cell carcinoma and squamous cell carcinoma. This is an economic easy-to-apply tool that could be useful in primary and secondary prevention of skin cancer.

  10. 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.

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

    International Nuclear Information System (INIS)

    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. (research papers)

  12. 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.

  13. 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.

  14. On-line Parameter Tuning of Model Predictive Control

    Czech Academy of Sciences Publication Activity Database

    Belda, Květoslav

    Milano : IFAC - International Fedaration of Automatic Control, 2011 - (Bittanti; Cenedese; Zampieri), s. 5489-5494 ISBN 978-3-902661-93-7. [18th IFAC World Congress . Milano (IT), 28.08.2011-02.09.2011] R&D Projects: GA ČR(CZ) GAP102/11/0437 Institutional research plan: CEZ:AV0Z10750506 Keywords : Model Predictive Control * Parameter Tuning * LQ Control * Probality Calculus Subject RIV: BC - Control Systems Theory http:// library .utia.cas.cz/separaty/2011/AS/belda-on-line parameter tuning of model predictive control.pdf

  15. Reliable prediction of complex thermal hydraulic parameters by ANN

    International Nuclear Information System (INIS)

    Thermal hydraulic data-base is very useful in the design and analysis of the proposed Advanced Heavy Water Reactor which relies on natural circulation for normal core cooling. Compilation of the thermal hydraulic data-base is in progress. Artificial Neural Networks (ANNs), have been applied to analyse the consistency and accuracy of the data-base. The ANN predictions are more accurate and cover wider range of parameters compared to model based predictions

  16. PREDICTION OF LEAF SPRING PARAMETERS USING ARTIFICIAL NEURAL NETWORKS

    OpenAIRE

    Dr.D.V.V.KRISHNA PRASAD; J.P.KARTHIK

    2013-01-01

    In this paper an attempt is made to predict the optimum design parameters using artificial neural networks. For this static and dynamic analysis on various leaf spring configuration is carried out by ANSYS and is used as training data for neural network. Training data includes cross section of the leaf, load on the leaf spring, stresses, displacement and natural frequencies. By creating a network using thickness and width of the leaf, load on the leaf spring as input parameters and stresses, ...

  17. 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.

  18. 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.

  19. Nondestructive prediction of pork freshness parameters using multispectral scattering images

    Science.gov (United States)

    Tang, Xiuying; Li, Cuiling; Peng, Yankun; Chao, Kuanglin; Wang, Mingwu

    2012-05-01

    Optical technology is an important and immerging technology for non-destructive and rapid detection of pork freshness. This paper studied on the possibility of using multispectral imaging technique and scattering characteristics to predict the freshness parameters of pork meat. The pork freshness parameters selected for prediction included total volatile basic nitrogen (TVB-N), color parameters (L *, a *, b *), and pH value. Multispectral scattering images were obtained from pork sample surface by a multispectral imaging system developed by ourselves; they were acquired at the selected narrow wavebands whose center wavelengths were 517,550, 560, 580, 600, 760, 810 and 910nm. In order to extract scattering characteristics from multispectral images at multiple wavelengths, a Lorentzian distribution (LD) function with four parameters (a: scattering asymptotic value; b: scattering peak; c: scattering width; d: scattering slope) was used to fit the scattering curves at the selected wavelengths. The results show that the multispectral imaging technique combined with scattering characteristics is promising for predicting the freshness parameters of pork meat.

  20. Predicting groundwater arsenic contamination in Southeast Asia from surface parameters

    Science.gov (United States)

    Winkel, Lenny; Berg, Michael; Amini, Manouchehr; Hug, Stephan J.; Annette Johnson, C.

    2008-08-01

    Arsenic contamination of groundwater resources threatens the health of millions of people worldwide, particularly in the densely populated river deltas of Southeast Asia. Although many arsenic-affected areas have been identified in recent years, a systematic evaluation of vulnerable areas remains to be carried out. Here we present maps pinpointing areas at risk of groundwater arsenic concentrations exceeding 10μgl-1. These maps were produced by combining geological and surface soil parameters in a logistic regression model, calibrated with 1,756 aggregated and geo-referenced groundwater data points from the Bengal, Red River and Mekong deltas. We show that Holocene deltaic and organic-rich surface sediments are key indicators for arsenic risk areas and that the combination of surface parameters is a successful approach to predict groundwater arsenic contamination. Predictions are in good agreement with the known spatial distribution of arsenic contamination, and further indicate elevated risks in Sumatra and Myanmar, where no groundwater studies exist.

  1. 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.

  2. 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

  3. Is There Any Parameter Helpful for Predicting a Suitable Candidate for Mite Immunotherapy?

    Science.gov (United States)

    Karaman, Sait; Can, Demet; Erdem, Semiha Bahçeci; Nacaroğlu, Hikmet Tekin; Karkıner, Canan Şule; Günay, İlker

    2016-04-01

    Few biomarkers that can predict the clinical response to allergen immunotherapy (AIT) have been identified. The aim of the present study was to investigate parameters that could be used "in predicting the clinical response to AIT" in children with asthma caused by house dust mites. We evaluated 107 children with mild persistent asthma who were sensitised only to mite aeroallergens. The study group included 47 patients who underwent a 4-to-5-year course of subcutaneous immunotherapy with standardised mite allergenic extract. Sixty patients who had not undergone AIT but were allergic to house mites were included in the control group. The clinical features and laboratory parameters of patients who did and did not sustain remission were compared. Remission was achieved in 74.5% of the 47 patients in the study group and in 20% of those in the control group. In the study group, one parameter predictive of a clinical response to AIT was identified by multivariate logistic analysis. This parameter was the serum total IgE level (tIgE) at the time of diagnosis (OR 131.64 and CI 0.858-20193; p = 0.032). Serum tIgE levels ≤ 339 kU/L at diagnosis were associated with an effective clinical response to AIT, with a sensitivity of 64.5% and specificity of 88.9%. We conclude that measurement of the serum tIgE level can be used as a predictive test prior to AIT in patients sensitized to mite aeroallergens. PMID:27090363

  4. 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. PMID:25899615

  5. 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

  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. Using neural networks for prediction of nuclear parameters

    International Nuclear Information System (INIS)

    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)

  8. Airborne fungal spores of Alternaria, meteorological parameters and predicting variables

    Science.gov (United States)

    Filali Ben Sidel, Farah; Bouziane, Hassan; del Mar Trigo, Maria; El Haskouri, Fatima; Bardei, Fadoua; Redouane, Abdelbari; Kadiri, Mohamed; Riadi, Hassane; Kazzaz, Mohamed

    2015-03-01

    Alternaria is frequently found as airborne fungal spores and is recognized as an important cause of respiratory allergies. The aerobiological monitoring of fungal spores was performed using a Burkard volumetric spore traps. To establish predicting variables for daily and weakly spore counts, a stepwise multiple regression between spore concentrations and independent variables (meteorological parameters and lagged values from the series of spore concentrations: previous day or week concentration (Alt t - 1) and mean concentration of the same day or week in other years ( C mean)) was made with data obtained during 2009-2011. Alternaria conidia are present throughout the year in the atmosphere of Tetouan, although they show important seasonal fluctuations. The highest levels of Alternaria spores were recorded during the spring and summer or autumn. Alternaria showed maximum daily values in April, May or October depending on year. When the spore variables of Alternaria, namely C mean and Alt t - 1, and meteorological parameters were included in the equation, the resulting R 2 satisfactorily predict future concentrations for 55.5 to 81.6 % during the main spore season and the pre-peak 2. In the predictive model using weekly values, the adjusted R 2 varied from 0.655 to 0.676. The Wilcoxon test was used to compare the results from the expected values and the pre-peak spore data or weekly values for 2012, indicating that there were no significant differences between series compared. This test showed the C mean, Alt t - 1, frequency of the wind third quadrant, maximum wind speed and minimum relative humidity as the most efficient independent variables to forecast the overall trend of this spore in the air.

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

    OpenAIRE

    O Hendy; M Abou Salem; G Abdel Rasoul; D Rohlman; A. Ismail

    2010-01-01

    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; ex...

  10. Assessment of uncertainty in parameter evaluation and prediction.

    Science.gov (United States)

    Meinrath, G; Ekberg, C; Landgren, A; Liljenzin, J O

    2000-02-01

    Like in all experimental science, chemical data is affected by the limited precision of the measurement process. Quality control and traceability of experimental data require suitable approaches to express properly the degree of uncertainty. Noise and bias are nuisance effects reducing the information extractable from experimental data. However, because of the complexity of the numerical data evaluation in many chemical fields, often mean values from data analysis, e.g. multi-parametric curve fitting, are reported only. Relevant information on the interpretation limits, e.g. standard deviations or confidence limits, are either omitted or estimated. Modern techniques for handling of uncertainty in both parameter evaluation and prediction are strongly based on the calculation power of computers. Advantageously, computer-intensive methods like Monte Carlo resampling and Latin Hypercube sampling do not require sophisticated and often unavailable mathematical treatment. The statistical concepts are introduced. Applications of some computer-intensive statistical techniques to chemical problems are demonstrated. PMID:18967855

  11. Systematic review of dose-volume parameters in the prediction of esophagitis in thoracic radiotherapy

    International Nuclear Information System (INIS)

    Purpose: With dose escalation and increasing use of concurrent chemoradiotherapy, radiation esophagitis (RE) remains a common treatment-limiting acute side effect in the treatment of thoracic malignancies. The advent of 3DCT planning has enabled investigators to study esophageal dose-volume histogram (DVH) parameters as predictors of RE. The purpose of this study was to assess published dosimetric parameters and toxicity data systematically in order to define reproducible predictors of RE, both for potential clinical use, and to provide recommendations for future research in the field. Materials and methods: We performed a systematic literature review of published studies addressing RE in the treatment of lung cancer and thymoma. Our search strategy included a variety of electronic medical databases, textbooks and bibliographies. Both prospective and retrospective clinical studies were included. Information relating to the relationship among measured dosimetric parameters, patient demographics, tumor characteristics, chemotherapy and RE was extracted and analyzed. Results: Eighteen published studies were suitable for analysis. Eleven of these assessed acute RE, while the remainder assessed both acute and chronic RE together. Heterogeneity of esophageal contouring practices, individual differences in information reporting and variability of RE outcome definitions were assessed. Well-described clinical and logistic modeling directly related V35Gy, V60Gy and SA55Gy to clinically significant RE. Conclusions: Several reproducible dosimetric parameters exist in the literature, and these may be potentially relevant in the prediction of RE in the radiotherapy of thoracic malignancies. Further clarification of the predictive relationship between such standardized dosimetric parameters and observed RE outcomes is essential to develop efficient radiation treatment planning in locally advanced NSCLC in the modern concurrent chemotherapy and image-guided IMRT era.

  12. 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. PMID:27089522

  13. 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.

  14. Toward the Real-Time Tsunami Parameters Prediction

    Science.gov (United States)

    Lavrentyev, Mikhail; Romanenko, Alexey; Marchuk, Andrey

    2013-04-01

    Today, a wide well-developed system of deep ocean tsunami detectors operates over the Pacific. Direct measurements of tsunami-wave time series are available. However, tsunami-warning systems fail to predict basic parameters of tsunami waves on time. Dozens examples could be provided. In our view, the lack of computational power is the main reason of these failures. At the same time, modern computer technologies such as, GPU (graphic processing unit) and FPGA (field programmable gates array), can dramatically improve data processing performance, which may enhance timely tsunami-warning prediction. Thus, it is possible to address the challenge of real-time tsunami forecasting for selected geo regions. We propose to use three new techniques in the existing tsunami warning systems to achieve real-time calculation of tsunami wave parameters. First of all, measurement system (DART buoys location, e.g.) should be optimized (both in terms of wave arriving time and amplitude parameter). The corresponding software application exists today and is ready for use [1]. We consider the example of the coastal line of Japan. Numerical tests show that optimal installation of only 4 DART buoys (accounting the existing sea bed cable) will reduce the tsunami wave detection time to only 10 min after an underwater earthquake. Secondly, as was shown by this paper authors, the use of GPU/FPGA technologies accelerates the execution of the MOST (method of splitting tsunami) code by 100 times [2]. Therefore, tsunami wave propagation over the ocean area 2000*2000 km (wave propagation simulation: time step 10 sec, recording each 4th spatial point and 4th time step) could be calculated at: 3 sec with 4' mesh 50 sec with 1' mesh 5 min with 0.5' mesh The algorithm to switch from coarse mesh to the fine grain one is also available. Finally, we propose the new algorithm for tsunami source parameters determination by real-time processing the time series, obtained at DART. It is possible to approximate

  15. Predicting Fundamental Stellar Parameters From Photometric Light Curves

    Science.gov (United States)

    Miller, Adam; Richards, J.; Bloom, J. S.; a larger Team

    2014-01-01

    We present a new machine-learning-based framework for the prediction of the fundamental stellar parameters, Teff, log g, and [Fe/H], based on the photometric light curves of variable stellar sources. The method was developed following a systematic spectroscopic survey of stellar variability. Variable sources were selected from repeated Sloan Digital Sky Survey (SDSS) observations of Stripe 82, and spectroscopic observations were obtained with Hectospec on the 6.5-m Multi-Mirror Telescope. In sum, spectra were obtained for ~9000 stellar variables (including ~3000 from the SDSS archive), for which we measured Teff, log g, and [Fe/H] using the Segue Stellar Parameters Pipeline (SSPP). Examining the full sample of ~67k variables in Stripe 82, we show that the vast majority of photometric variables are consistent with main-sequence stars, even after restricting the search to high galactic latitudes. From the spectroscopic sample we confirm that most of these stellar variables are G and K dwarfs, though there is a bias in the output of the SSPP that prevents the identification of M type variables. We are unable to identify the dominant source of variability for these stars, but eclipsing systems and/or star spots are the most likely explanation. We develop a machine-learning model that can determine Teff, log g, and [Fe/H] without obtaining a spectrum. Instead, the random-forest-regression model uses SDSS color information and light-curve features to infer stellar properties. We detail how the feature set is pruned and the model is optimized to produce final predictions of Teff, log g, and [Fe/H] with a typical scatter of 165 K, 0.42 dex, and 0.33 dex, respectively. We further show that for the subset of variables with at least 50 observations in the g band the typical scatter reduces to 75 K, 0.19 dex, and 0.16 dex, respectively. We consider these results an important step on the path to the efficient and optimal extraction of information from future time

  16. Pharmacogenetics : the science of predictive clinical pharmacology

    OpenAIRE

    Fenech, Anthony G; Grech, Godfrey

    2014-01-01

    The study of pharmacogenetics has expanded from what were initially casual family-based clinical drug response observations, to a fully-fledged science with direct therapeutic applications, all within a time-span of less than 60 years. A wide spectrum of polymorphisms, located within several genes, are now recognised to influence the pharmacokinetics and pharmacodynamics of the majority of drugs within our therapeutic armamentarium. This information forms the basis for the new development of ...

  17. 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.

  18. Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor.

    Science.gov (United States)

    Sengupta, Dipankar; Sood, Meemansa; Vijayvargia, Poorvika; Hota, Sunil; Naik, Pradeep K

    2013-01-01

    Healthcare sector is generating a large amount of information corresponding to diagnosis, disease identification and treatment of an individual. Mining knowledge and providing scientific decision-making for the diagnosis & treatment of disease from the clinical dataset is therefore increasingly becoming necessary. Aim of this study was to assess the applicability of knowledge discovery in brain tumor data warehouse, applying data mining techniques for investigation of clinical parameters that can be associated with occurrence of brain tumor. In this study, a brain tumor warehouse was developed comprising of clinical data for 550 patients. Apriori association rule algorithm was applied to discover associative rules among the clinical parameters. The rules discovered in the study suggests - high values of Creatinine, Blood Urea Nitrogen (BUN), SGOT & SGPT to be directly associated with tumor occurrence for patients in the primary stage with atleast 85% confidence and more than 50% support. A normalized regression model is proposed based on these parameters along with Haemoglobin content, Alkaline Phosphatase and Serum Bilirubin for prediction of occurrence of STATE (brain tumor) as 0 (absent) or 1 (present). The results indicate that the methodology followed will be of good value for the diagnostic procedure of brain tumor, especially when large data volumes are involved and screening based on discovered parameters would allow clinicians to detect tumors at an early stage of development. PMID:23888095

  19. Reporting and Methods in Clinical Prediction Research: A Systematic Review

    OpenAIRE

    Bouwmeester, W; Zuithoff, NP; Mallett, S.; Geerlings, MI; Vergouwe, Y.; Steyerberg, EW; Altman, DG; Moons, KG

    2012-01-01

    Editors' Summary Background There are often times in our lives when we would like to be able to predict the future. Is the stock market going to go up, for example, or will it rain tomorrow? Being able predict future health is also important, both to patients and to physicians, and there is an increasing body of published clinicalprediction research.” Diagnostic prediction research investigates the ability of variables or test results to predict the presence or absence of a specific diagnos...

  20. Clinical Prediction Rules for Physical Therapy Interventions: A Systematic Review

    OpenAIRE

    Beneciuk, Jason M.; Bishop, Mark D; George, Steven Z.

    2009-01-01

    Background and Purpose: Clinical prediction rules (CPRs) involving physical therapy interventions have been published recently. The quality of the studies used to develop the CPRs was not previously considered, a fact that has potential implications for clinical applications and future research. The purpose of this systematic review was to determine the quality of published CPRs developed for physical therapy interventions.

  1. Evaluation of measurement parameters in clinical cerebral MR angiography

    International Nuclear Information System (INIS)

    A three-dimensional MR angiography technique was developed in patients and volunteers that can produce clinically useful studies of the cerebral vasculature with multiple views possible from a single data set acquisition. A low-flip angle, three-dimensional fast imaging with steady precessing sequence was found to provide the optimal vessel/soft-tissue contrast related to the tissues' relaxation parameters and inflow. The inflowing unsaturated spins and vessel orientation made an axial acquisition most effective. The flow-related phase dispersion could be largelly corrected with the appropriate orientation, strength, order, and combination of refocusing gradients with the shortest possible echo times. Reduced voxel size also affected the phase dispersion due to motion and field inhomogeneities. Postprocessing yielded projection images of vessels throughout the three-dimensional volume

  2. Predictive data mining in clinical medicine: Current issues and guidelines

    OpenAIRE

    Bellazzi, Riccado; Zupan, Blaz

    2008-01-01

    BACKGROUND: The widespread availability of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, the collection of methods known as 'data mining' offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these met...

  3. 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.

  4. Using Kalman Filtering to Predict Time-Varying Parameters in a Model Predicting Baroreflex Regulation During Head-Up Tilt.

    Science.gov (United States)

    Matzuka, Brett; Mehlsen, Jesper; Tran, Hien; Olufsen, Mette Sofie

    2015-08-01

    The cardiovascular control system is continuously engaged to maintain homeostasis, but it is known to fail in a large cohort of patients suffering from orthostatic intolerance. Numerous clinical studies have been put forward to understand how the system fails, yet noninvasive clinical data are sparse, typical studies only include measurements of heart rate and blood pressure, as a result it is difficult to determine what mechanisms that are impaired. It is known, that blood pressure regulation is mediated by changes in heart rate, vascular resistance, cardiac contractility, and a number of other factors. Given that numerous factors contribute to changing these quantities, it is difficult to devise a physiological model describing how they change in time. One way is to build a model that allows these controlled quantities to change and to compare dynamics between subject groups. To do so, it requires more knowledge of how these quantities change for healthy subjects. This study compares two methods predicting time-varying changes in cardiac contractility and vascular resistance during head-up tilt. Similar to the study by Williams et al. [51], the first method uses piecewise linear splines, while the second uses the ensemble transform Kalman filter (ETKF) [1], [11], [12], [33]. 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 with the ETKF. While the spline method is easier to set up, this study shows that the ETKF has a significantly shorter computational time. Moreover, while uncertainty of predictions can be augmented to spline predictions using DRAM, these are readily available with the ETKF. PMID:25769142

  5. Internal derangement of the temporomandibular joint: correlation of magnetic resonance imaging and clinical parameters

    International Nuclear Information System (INIS)

    Purpose: To evaluate the value of magnetic resonance imaging (MRI) in symptomatic patients with different degrees of internal derangement. Material and methods: We prospectively investigated 117 temporomandibular points (TMJ) of 59 symptomatic patients and 31 asymptomatic volunteers and correlated this with clinical parameters. Results: There was a positive correlation between the degree of internal derangement and deformity of the disc, maximal mouth opening, signal intensity of the posterior band, thickness of the bilaminar zone, proliferative bony changes, size of the condyle and reduced translatory movement of the condyle, which in addition moved upward and backward. Patients most often complained of pain which was dependent on the degree of disc displacement and condylar changes. Clinical parameters were found to be inaccurate in predicting disc displacement of the temporormandibular joint may be asymptomatic. Patients history may give the only pointer to the disorder. (orig.)

  6. 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.

  7. Clinical Nomogram for Predicting Survival of Esophageal Cancer Patients after Esophagectomy

    OpenAIRE

    Jinlin Cao; Ping Yuan; Luming Wang; Yiqing Wang; Honghai Ma; Xiaoshuai Yuan; Wang Lv; Jian Hu

    2016-01-01

    The aim of this study was to construct an effective clinical nomogram for predicting the survival of esophageal cancer patients after esophagectomy. We identified esophageal cancer patients (n = 4,281) who underwent esophagectomy between 1988 and 2007 from the Surveillance, Epidemiology, and End Results (SEER) 18 registries database. Clinically significant parameters for survival were used to construct a nomogram based on Cox regression analyses. The model was validated using bootstrap resamp...

  8. 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...

  9. Predicting accurate line shape parameters for CO2 transitions

    International Nuclear Information System (INIS)

    The vibrational dependence of CO2 half-widths and line shifts are given by a modification of the model proposed by Gamache and Hartmann [Gamache R, Hartmann J-M. J Quant Spectrosc Radiat Transfer 2004;83:119]. This model allows the half-widths and line shifts for a ro-vibrational transition to be expressed in terms of the number of vibrational quanta exchanged in the transition raised to a power and a reference ro-vibrational transition. Calculations were made for 24 bands for lower rotational quantum numbers from 0 to 160 for N2-, O2-, air-, and self-collisions with CO2. These data were extrapolated to J″=200 to accommodate several databases. Comparison of the CRB calculations with measurement gives very high confidence in the data. In the model a Quantum Coordinate is defined by (c1 |Δν1|+c2 |Δν2|+c3|Δν3|)p. The power p is adjusted and a linear least-squares fit to the data by the model expression is made. The procedure is iterated on the correlation coefficient, R, until [|R|−1] is less than a threshold. The results demonstrate the appropriateness of the model. The model allows the determination of the slope and intercept as a function of rotational transition, broadening gas, and temperature. From the data of the fits, the half-width, line shift, and the temperature dependence of the half-width can be estimated for any ro-vibrational transition, allowing spectroscopic CO2 databases to have complete information for the line shape parameters. -- Highlights: • Development of a quantum coordinate model for the half-width and line shift. • Calculations of γ and δ for N2-, O2-, air-, and CO2–CO2 systems for 24 bands. • J″=0–160, bands up to Δν1=3, Δν2=5, Δν3=9, 9 temperatures from 200–2000 K. • γ, n, δ, prediction routines for all ro-vibrational transitions up to J″=200

  10. 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

  11. Prediction of meteorological parameters - 3: Rainfall and droughts

    International Nuclear Information System (INIS)

    We describe two new methods by which rainfall and hence meteorological droughts at any location on the earth may be predicted. The first method is based upon well supported observations that rainfall distribution at a given location during any local sunspot-related temperature/heat cycle is approximately similar to the distribution during another cycle associated with approximately similar sunspot cycle provided that the two temperature/heat cycles involved are immediately preceded by approximately similar sunspot cycles. The second method is based upon the fact that rainfall belts or patterns seem to be closely related to certain spatial and time-dependent temperature/heat patterns in the earth-atmosphere system. Reasonable predictions of these temperature/heat patterns may be made, and hence the associated rainfall patterns or belts may correspondingly be predicted. Specific examples are given to illustrate the two prediction methods. (author). 12 refs, 11 figs, 1 tab

  12. 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.

  13. Prediction of time series of NPP operating parameters using dynamic model based on BP neural network

    International Nuclear Information System (INIS)

    Highlights: • A dynamic prediction model for NPP operating parameters was proposed. • The structure of continuous dynamic prediction system was designed. • Multi-threading technology was used in the system. • The system can predict the fluctuating data with high accuracy. - Abstract: A dynamic model was developed using two back-propagation neural networks of the same structure, one for online training and the other for prediction, and proposed for continuous dynamic prediction of the time series of NPP operating parameters. The proposed prediction model was validated by predicting such time series of NPP operating parameters as coolant void fraction, water level in SG and pressurizer. Validation results indicated the proposed model could be used to achieve a stable prediction effect with high prediction accuracy for the prediction of fluctuating data

  14. On-time clinical phenotype prediction based on narrative reports

    OpenAIRE

    Bejan, Cosmin A.; Vanderwende, Lucy; Evans, Heather L.; Wurfel, Mark M.; Yetisgen-Yildiz, Meliha

    2013-01-01

    In this paper we describe a natural language processing system which is able to predict whether or not a patient exhibits a specific phenotype using the information extracted from the narrative reports associated with the patient. Furthermore, the phenotypic annotations from our report dataset were performed at the report level which allows us to perform the prediction of the clinical phenotype at any point in time during the patient hospitalization period. Our experiments indicate that an im...

  15. BAYESIAN PREDICTION FOR THE TWO-PARAMETER EXPONENTIAL DISTRIBUTION BASED ON TYPE Ⅱ DOUBLY CENSORING

    Institute of Scientific and Technical Information of China (English)

    LiYanling; ZhaoXuanmin; XieWenxian

    2005-01-01

    The two-parameter exponential distribution is proposed to be an underlying model, and prediction bounds for future observations are obtained by using Bayesian approach. Prediction intervals are derived for unobserved lifetimes in one-sample prediction and twosample prediction based on type Ⅱ doubly censored samples. A numerical example is given to illustrate the procedures,prediction intervals are investigated via Monte Carlo method,and the accuracy of prediction intervals is presented.

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

    Science.gov (United States)

    Choi, Youn-Kyung; Kim, Jinmi; Maki, Koutaro; Ko, Ching-Chang

    2016-01-01

    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. PMID:27340668

  17. 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...

  18. Evaluation of predicted and operated parameters of photovoltaic power - station

    International Nuclear Information System (INIS)

    This report is concerned with comparison of amount of power produced by photovoltaic power-station determined before construction by simulation of the given system with actually measured values during the full operation of the power-station. Our objective was to compare and evaluate the results of prediction against actual situation. The solution result is the justification of deviations measured. (authors)

  19. On-time clinical phenotype prediction based on narrative reports

    Science.gov (United States)

    Bejan, Cosmin A.; Vanderwende, Lucy; Evans, Heather L.; Wurfel, Mark M.; Yetisgen-Yildiz, Meliha

    2013-01-01

    In this paper we describe a natural language processing system which is able to predict whether or not a patient exhibits a specific phenotype using the information extracted from the narrative reports associated with the patient. Furthermore, the phenotypic annotations from our report dataset were performed at the report level which allows us to perform the prediction of the clinical phenotype at any point in time during the patient hospitalization period. Our experiments indicate that an important factor in achieving better results for this problem is to determine how much information to extract from the patient reports in the time interval between the patient admission time and the current prediction time. PMID:24551325

  20. Predicting Performance on a Firefighter's Ability Test from Fitness Parameters

    Science.gov (United States)

    Michaelides, Marcos A.; Parpa, Koulla M.; Thompson, Jerald; Brown, Barry

    2008-01-01

    The purpose of this project was to identify the relationships between various fitness parameters such as upper body muscular endurance, upper and lower body strength, flexibility, body composition and performance on an ability test (AT) that included simulated firefighting tasks. A second intent was to create a regression model that would predict…

  1. 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.

  2. Computer programme for prediction steel parameters after heat treatment

    OpenAIRE

    J. Trzaska; L.A Dobrzanski; A. Jagiełło

    2007-01-01

    Purpose: The purpose of this paper is presentation of the computer program for calculating the Continuous Cooling Transformation diagrams for constructional and engineering steels.Design/methodology/approach: The computer program uses the artificial neural networks for prediction steel properties after heat treatment. Input data are chemical composition and austenitizing temperature. Results of calculation consist of temperature of the beginning and the end of transformation...

  3. Order parameter prediction from molecular dynamics simulations in proteins

    CERN Document Server

    Perilla, Juan R

    2011-01-01

    A molecular understanding of how protein function is related to protein structure will require an ability to understand large conformational changes between multiple states. Unfortunately these states are often separated by high free energy barriers and within a complex energy landscape. This makes it very difficult to reliably connect, for example by all-atom molecular dynamics calculations, the states, their energies and the pathways between them. A major issue needed to improve sampling on the intermediate states is an order parameter -- a reduced descriptor for the major subset of degrees of freedom -- that can be used to aid sampling for the large conformational change. We present a novel way to combine information from molecular dynamics using non-linear time series and dimensionality reduction, in order to quantitatively determine an order parameter connecting two large-scale conformationally distinct protein states. This new method suggests an implementation for molecular dynamics calculations that ma...

  4. Clinical Prediction Rule of Drug Resistant Epilepsy in Children

    Science.gov (United States)

    Boonluksiri, Pairoj; Visuthibhan, Anannit; Katanyuwong, Kamornwan

    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 epilepsy were recruited. Primary outcome was the incidence of DRE. Independent determinants were patient characteristics, clinical manifestations and electroencephalography. CPR was performed based on multiple logistic regression. Results: The incidence of DRE was 42%. Risk factors were age onset, prior neurological deficits, and abnormal EEG. CPR can be established and stratified the prediction using scores into 3 levels such as low risk (score12) with positive likelihood ratio of 0.5, 1.8 and 12.5 respectively. Conclusions: CPR with scoring risks were stratified into 3 levels. The strongest risk is prior global neurological deficits. PMID:26819940

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

    OpenAIRE

    Hoda Hosny Abuzied; Mohamed Ahmed Awad; Hesham Aly Senbel

    2012-01-01

    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 numerou...

  6. Different Vocal Parameters Predict Perceptions of Dominance and Attractiveness

    OpenAIRE

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

    2010-01-01

    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 fert...

  7. 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.

  8. 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.

  9. Computer programme for prediction steel parameters after heat treatment

    Directory of Open Access Journals (Sweden)

    J. Trzaska

    2007-10-01

    Full Text Available Purpose: The purpose of this paper is presentation of the computer program for calculating the Continuous Cooling Transformation diagrams for constructional and engineering steels.Design/methodology/approach: The computer program uses the artificial neural networks for prediction steel properties after heat treatment. Input data are chemical composition and austenitizing temperature. Results of calculation consist of temperature of the beginning and the end of transformation in the cooling rate function, the volume fraction of structural components and hardness of steel cooled from austenitizing temperature with a fixed rate.Findings: The algorithm can be use in designing new chemical compositions of steels with assumed hardness after heat treatment.Research limitations/implications: The created method for designing chemical compositions is limited by ranges of mass concentrations of elements. The methodology demonstrated in the paper makes possibility to add new steels to the system.Practical implications: The method may be used in computer steel selection systems for machines parts manufactured from constructional or engineering steels subjected to heat treatment.Originality/value: The presented computer program can be used for selecting steel with required structure after heat treatment.

  10. 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.

  11. 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…

  12. Clinical and blood gasometric parameters during Vaquejada competition

    OpenAIRE

    Silvana S.B. Arruda; Lucio N. Huaixan; André R.C. Barreto-Vianna; Roberta F. Godoy; Eduardo M.M. Lima

    2015-01-01

    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 checkin...

  13. Outcome Prediction in Pneumonia Induced ALI/ARDS by Clinical Features and Peptide Patterns of BALF Determined by Mass Spectrometry

    OpenAIRE

    Frenzel, Jochen; Gessner, Christian; Sandvoss, Torsten; Hammerschmidt, Stefan; Schellenberger, Wolfgang; Sack, Ulrich; Eschrich, Klaus; Wirtz, Hubert

    2011-01-01

    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 sc...

  14. A Clinical Prediction Formula for Apnea-Hypopnea Index

    OpenAIRE

    Mustafa Sahin; Cem Bilgen; M. Sezai Tasbakan; Rasit Midilli; Basoglu, Ozen K.

    2014-01-01

    Objectives. There are many studies regarding unnecessary polysomnography (PSG) when obstructive sleep apnea syndrome (OSAS) is suspected. In order to reduce unnecessary PSG, this study aims to predict the apnea-hypopnea index (AHI) via simple clinical data for patients who complain of OSAS symptoms. Method. Demographic, anthropometric, physical examination and laboratory data of a total of 390 patients (290 men, average age 50 ± 11) who were subject to diagnostic PSG were obtained and evaluat...

  15. Cytokines and signaling molecules predict clinical outcomes in sepsis.

    Directory of Open Access Journals (Sweden)

    Christopher D Fjell

    Full Text Available INTRODUCTION: Inflammatory response during sepsis is incompletely understood due to small sample sizes and variable timing of measurements following the onset of symptoms. The vasopressin in septic shock trial (VASST compared the addition of vasopressin to norepinephrine alone in patients with septic shock. During this study plasma was collected and 39 cytokines measured in a 363 patients at both baseline (before treatment and 24 hours. Clinical features relating to both underlying health and the acute organ dysfunction induced by the severe infection were collected during the first 28 days of admission. HYPOTHESIS: Cluster analysis of cytokines identifies subgroups of patients at differing risk of death and organ failure. METHODS: Circulating cytokines and other signaling molecules were measured using a Luminex multi-bead analyte detection system. Hierarchical clustering was performed on plasma values to create patient subgroups. Enrichment analysis identified clinical outcomes significantly different according to these chemically defined patient subgroups. Logistic regression was performed to assess the importance of cytokines for predicting patient subgroups. RESULTS: Plasma levels at baseline produced three subgroups of patients, while 24 hour levels produced two subgroups. Using baseline cytokine data, one subgroup of 47 patients showed a high level of enrichment for severe septic shock, coagulopathy, renal failure, and risk of death. Using data at 24 hours, a larger subgroup of 81 patients that largely encompassed the 47 baseline subgroup patients had a similar enrichment profile. Measurement of two cytokines, IL2 and CSF2 and their product were sufficient to classify patients into these subgroups that defined clinical risks. CONCLUSIONS: A distinct pattern of cytokine levels measured early in the course of sepsis predicts disease outcome. Subpopulations of patients have differing clinical outcomes that can be predicted accurately from

  16. Perfusion CT in acute stroke: prediction of vessel recanalization and clinical outcome in intravenous thrombolytic therapy

    International Nuclear Information System (INIS)

    This study evaluated perfusion computed tomography (PCT) for the prediction of vessel recanalization and clinical outcome in patients undergoing intravenous thrombolysis. Thirty-nine patients with acute ischemic stroke of the middle cerebral artery territory underwent intravenous thrombolysis within 3 h of symptom onset. They all had non-enhanced CT (NECT), PCT, and CT angiography (CTA) before treatment. The Alberta Stroke Program Early Computed Tomography (ASPECT) score was applied to NECT and PCT maps to assess the extent of ischemia. CTA was assessed for the site of vessel occlusion. The National Institute of Health Stroke Scale (NIHSS) score was used for initial clinical assessment. Three-month clinical outcome was assessed using the modified Rankin scale. Vessel recanalization was determined by follow-up ultrasound. Of the PCT maps, a cerebral blood volume (CBV) ASPECT score of >6 versus ≤6 was the best predictor for clinical outcome (odds ratio, 31.43; 95% confidence interval, 3.41-289.58; P < 0.002), and was superior to NIHSS, NECT and CTA. No significant differences in ASPECT scores were found for the prediction of vessel recanalization. ASPECT score applied to PCT maps in acute stroke patients predicts the clinical outcome of intravenous thrombolysis and is superior to both early NECT and clinical parameters. (orig.)

  17. 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.

  18. Development of an algorithm to predict comfort of wheelchair fit based on clinical measures.

    Science.gov (United States)

    Kon, Keisuke; Hayakawa, Yasuyuki; Shimizu, Shingo; Nosaka, Toshiya; Tsuruga, Takeshi; Matsubara, Hiroyuki; Nomura, Tomohiro; Murahara, Shin; Haruna, Hirokazu; Ino, Takumi; Inagaki, Jun; Kobayashi, Toshiki

    2015-09-01

    [Purpose] The purpose of this study was to develop an algorithm to predict the comfort of a subject seated in a wheelchair, based on common clinical measurements and without depending on verbal communication. [Subjects] Twenty healthy males (mean age: 21.5 ± 2 years; height: 171 ± 4.3 cm; weight: 56 ± 12.3 kg) participated in this study. [Methods] Each experimental session lasted for 60 min. The clinical measurements were obtained under 4 conditions (good posture, with and without a cushion; bad posture, with and without a cushion). Multiple regression analysis was performed to determine the relationship between a visual analogue scale and exercise physiology parameters (respiratory and metabolism), autonomic nervous parameters (heart rate, blood pressure, and salivary amylase level), and 3D-coordinate posture parameters (good or bad posture). [Results] For the equation (algorithm) to predict the visual analogue scale score, the adjusted multiple correlation coefficient was 0.72, the residual standard deviation was 1.2, and the prediction error was 12%. [Conclusion] The algorithm developed in this study could predict the comfort of healthy male seated in a wheelchair with 72% accuracy. PMID:26504299

  19. 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.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Sung Woo Cho

    2016-01-01

    Full Text Available Abstract Background: Hemorheological and glycemic parameters and high density lipoprotein (HDL cholesterol are used as biomarkers of atherosclerosis and thrombosis. Objective: 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. Methods: 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. Results: 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. Conclusion: 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.

  2. Effects of soil hydraulic and transport parameter uncertainty on predictions of solute transport in large lysimeters

    Science.gov (United States)

    Advanced numerical simulation models can potentially help improve guidelines for irrigation and salinity management. Many simulation model parameters have considerable uncertainty, and ideally that uncertainty should be reflected in model predictions and recommendations. In this work, we investiga...

  3. Parameter optimization using GA in SVM to predict damage level of non-reshaped berm breakwater.

    Digital Repository Service at National Institute of Oceanography (India)

    Harish, N.; Lokesha.; Mandal, S.; Rao, S.; Patil, S.G.

    In the present study, Support Vector Machines (SVM) and hybrid of Genetic Algorithm (GA) with SVM models are developed to predict the damage level of non-reshaped berm breakwaters. Optimal kernel parameters of SVM are determined by using GA...

  4. Clinical implementation of dose-volume histogram predictions for organs-at-risk in IMRT planning

    International Nuclear Information System (INIS)

    True quality control (QC) of the planning process requires quantitative assessments of treatment plan quality itself, and QC in IMRT has been stymied by intra-patient anatomical variability and inherently complex three-dimensional dose distributions. In this work we describe the development of an automated system to reduce clinical IMRT planning variability and improve plan quality using mathematical models that predict achievable OAR DVHs based on individual patient anatomy. These models rely on the correlation of expected dose to the minimum distance from a voxel to the PTV surface, whereby a three-parameter probability distribution function (PDF) was used to model iso-distance OAR subvolume dose distributions. DVH models were obtained by fitting the evolution of the PDF with distance. Initial validation on clinical cohorts of 40 prostate and 24 head-and-neck plans demonstrated highly accurate model-based predictions for achievable DVHs in rectum, bladder, and parotid glands. By quantifying the integrated difference between candidate DVHs and predicted DVHs, the models correctly identified plans with under-spared OARs, validated by replanning all cases and correlating any realized improvements against the predicted gains. Clinical implementation of these predictive models was demonstrated in the PINNACLE treatment planning system by use of existing margin expansion utilities and the scripting functionality inherent to the system. To maintain independence from specific planning software, a system was developed in MATLAB to directly process DICOM-RT data. Both model training and patient-specific analyses were demonstrated with significant computational accelerations from parallelization.

  5. Clinical implementation of dose-volume histogram predictions for organs-at-risk in IMRT planning

    Science.gov (United States)

    Moore, K. L.; Appenzoller, L. M.; Tan, J.; Michalski, J. M.; Thorstad, W. L.; Mutic, S.

    2014-03-01

    True quality control (QC) of the planning process requires quantitative assessments of treatment plan quality itself, and QC in IMRT has been stymied by intra-patient anatomical variability and inherently complex three-dimensional dose distributions. In this work we describe the development of an automated system to reduce clinical IMRT planning variability and improve plan quality using mathematical models that predict achievable OAR DVHs based on individual patient anatomy. These models rely on the correlation of expected dose to the minimum distance from a voxel to the PTV surface, whereby a three-parameter probability distribution function (PDF) was used to model iso-distance OAR subvolume dose distributions. DVH models were obtained by fitting the evolution of the PDF with distance. Initial validation on clinical cohorts of 40 prostate and 24 head-and-neck plans demonstrated highly accurate model-based predictions for achievable DVHs in rectum, bladder, and parotid glands. By quantifying the integrated difference between candidate DVHs and predicted DVHs, the models correctly identified plans with under-spared OARs, validated by replanning all cases and correlating any realized improvements against the predicted gains. Clinical implementation of these predictive models was demonstrated in the PINNACLE treatment planning system by use of existing margin expansion utilities and the scripting functionality inherent to the system. To maintain independence from specific planning software, a system was developed in MATLAB to directly process DICOM-RT data. Both model training and patient-specific analyses were demonstrated with significant computational accelerations from parallelization.

  6. 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.

  7. 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.

  8. 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.

  9. Asymptotic Prediction Mean Squared Error for Strongly Dependent Processes with Estimated Parameters

    OpenAIRE

    Naoya Katayama

    2004-01-01

    In this paper we deal with the prediction theory of long memory processes. After investigating the general theory relating to convergence of moments of the nonlinear least squares estimators, we evaluate the asymptotic prediction mean squared error of two predictors. One is defined by using the estimator of the differencing parameter and the other is defined by using a fixed, known differencing parameter, which is, in other words, one parametric predictor of the seasonally integrated autoregr...

  10. Uncertainty and Sensitivity Analyses of a Two-Parameter Impedance Prediction Model

    Science.gov (United States)

    Jones, M. G.; Parrott, T. L.; Watson, W. R.

    2008-01-01

    This paper presents comparisons of predicted impedance uncertainty limits derived from Monte-Carlo-type simulations with a Two-Parameter (TP) impedance prediction model and measured impedance uncertainty limits based on multiple tests acquired in NASA Langley test rigs. These predicted and measured impedance uncertainty limits are used to evaluate the effects of simultaneous randomization of each input parameter for the impedance prediction and measurement processes. A sensitivity analysis is then used to further evaluate the TP prediction model by varying its input parameters on an individual basis. The variation imposed on the input parameters is based on measurements conducted with multiple tests in the NASA Langley normal incidence and grazing incidence impedance tubes; thus, the input parameters are assigned uncertainties commensurate with those of the measured data. These same measured data are used with the NASA Langley impedance measurement (eduction) processes to determine the corresponding measured impedance uncertainty limits, such that the predicted and measured impedance uncertainty limits (95% confidence intervals) can be compared. The measured reactance 95% confidence intervals encompass the corresponding predicted reactance confidence intervals over the frequency range of interest. The same is true for the confidence intervals of the measured and predicted resistance at near-resonance frequencies, but the predicted resistance confidence intervals are lower than the measured resistance confidence intervals (no overlap) at frequencies away from resonance. A sensitivity analysis indicates the discharge coefficient uncertainty is the major contributor to uncertainty in the predicted impedances for the perforate-over-honeycomb liner used in this study. This insight regarding the relative importance of each input parameter will be used to guide the design of experiments with test rigs currently being brought on-line at NASA Langley.

  11. Parameter sensitivity analysis: application to model predictions of transport parameters governing arterial wall uptake of 14C-4 cholesterol

    International Nuclear Information System (INIS)

    The authors have applied nonlinear (Marquardt) regression techniques to concentration profiles of 14C-4 cholesterol within the arterial wall of excised canine carotids perfused for two hours under various hemodynamic conditions. The resulting estimates of the transport parameters of Peclet number (Pe), effective diffusivity (D), endothelial vesicular Biot number (Be), and endothelial phenomenological rejection coefficient (Re) serve as input to a sensitivity analysis of the inter-dependence of model parameters. Here the best-fit value of each parameter estimate is perturbed by 10% and the resulting change in the predicted concentration profile used to calculate individual parameter sensitivities according to algorithms defined in the text authored by P.M. Frank. Their results indicate that the parameters Be and Re are indistinguishable at all locations within the wall substance (x/L) except the endothelial cell border (x/L=0). A similar finding is true for the parameter pair Pe-D when x/L > 0.4, i.e. beyond the medial-adventitial junction. The implications are that the present mathematical model be modified to exclude an assessment of Be, especially when the anatomic integrity of the endothelial layer is compromised, and that perfusion times be extended to allow advancement of convective and diffusive fronts into the adventitia

  12. 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.

  13. 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...

  14. 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.

  15. 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.

  16. Fatigue life prediction under variable amplitude axial–torsion loading using maximum damage parameter range method

    International Nuclear Information System (INIS)

    This article deals with the problem of multiaxial fatigue life assessment under variable amplitude axial–torsion loading. A maximum damage parameter range (MDPR) reversal counting method is proposed to predict fatigue life under variable amplitude multiaxial loading. First, a multiaxial fatigue damage parameter is selected for a given multiaxial loading time history. Then, a damage parameter range time history can be calculated. Finally, based on the MDPR method, fatigue life can be predicted by correlating with multiaxial fatigue damage model and the Miner–Palmgren damage rule. The proposed method is evaluated with experimental data of the 7050-T7451 aluminum alloy and En15R steel under variable amplitude multiaxial loading. The results demonstrated that the proposed method can provide satisfactory prediction. -- Highlights: • A maximum damage parameter range (MDPR) reversal counting method is proposed. • Fatigue damage parameter will be directly defined as cycle counting parameter. • Based on MDPR method, a fatigue life prediction procedure is proposed. • The detailed algorithm is proposed. • The proposed method can provide satisfactory prediction

  17. Comparison between turbinoplasty and endoscopic turbinectomy: Efficacy and clinical parameters

    Directory of Open Access Journals (Sweden)

    Rodrigues, Marcos Marques

    2011-10-01

    Full Text Available Introduction: Nasal Obstruction is a common symptom and affects 25% of the population. The inferior turbinate hypertrophy is the main cause of nasal obstruction. In the failure of clinical control, a surgical procedure to reduce the size of the inferior turbinate is indicated. Objective: Compare the improvement of life quality in late postoperative of Turbinectomy and turbinoplasty. Method: Study of a retrospective case series. 24 patients were submitted to a nasal surgery of turbinectomy or turbinoplasty in 2007. The patients were invited to an interview in august of 2008. The patients were evaluated in the following items: Postoperative NOSE scale, morbidity in postoperative, bleeding and quantity of crusts in postoperative. Results: 24 patients attended for the evaluation. The main variable analyzed was the difference between NOSE scales in late pre and post operative. There was no statistically significant by the test in the variables studied. Discussion: In the evaluation of the various types of surgical treatment of the inferior turbinate, literature shows similar results to our study, finding similar results between many surgical techniques in the improvement of the nasal obstruction and in mucociliary activity. Conclusion: There are no evidence in the literature and in our sample of the superiority of a technique of surgical treatment in the inferior turbinate under other treatments.

  18. Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation

    Science.gov (United States)

    Zhou, Ligang; Keung Lai, Kin; Yen, Jerome

    2014-03-01

    Due to the economic significance of bankruptcy prediction of companies for financial institutions, investors and governments, many quantitative methods have been used to develop effective prediction models. Support vector machine (SVM), a powerful classification method, has been used for this task; however, the performance of SVM is sensitive to model form, parameter setting and features selection. In this study, a new approach based on direct search and features ranking technology is proposed to optimise features selection and parameter setting for 1-norm and least-squares SVM models for bankruptcy prediction. This approach is also compared to the SVM models with parameter optimisation and features selection by the popular genetic algorithm technique. The experimental results on a data set with 2010 instances show that the proposed models are good alternatives for bankruptcy prediction.

  19. Prediction model of atmospheric refractive index structure parameter in coastal area

    Science.gov (United States)

    Wang, Hongxing; Li, Bifeng; Wu, Xiaojun; Liu, Chuanhui; Hu, Zhihui; Xu, Pengfei

    2015-09-01

    In this paper, we focus on the prediction of atmospheric refractive index structure parameter (?) in coastal area using the routine meteorological parameters. Based on the micrometeorology, macrometeorology and Monin-Obukhov similarity theory, three modified prediction models of ? are presented in combination with the long-term observation data of ? and meteorological parameters in coastal city, respectively. For different weather, the applicable cases of three ? prediction models are comparatively analysed and their applicable effects are comprehensively evaluated. The results indicate that the modified micrometeorology model of ? shows better applicability for overcast sky, the offshore macrometeorology model of ? displays better predictability for sunny day and the offshore Thiermann model provides better availability for overcast sky, cloudy day, overcast to sunny or sunny to overcast day.

  20. Development of computer code for determining prediction parameters of radionuclide migration in soil layer

    International Nuclear Information System (INIS)

    A computer code (MIGSTEM-FIT) has been developed to determine the prediction parameters, retardation factor, water flow velocity, dispersion coefficient, etc., of radionuclide migration in soil layer from the concentration distribution of radionuclide in soil layer or in effluent. In this code, the solution of the predicting equation for radionuclide migration is compared with the concentration distribution measured, and the most adequate values of parameter can be determined by the flexible tolerance method. The validity of finite differential method, which was one of the method to solve the predicting equation, was confirmed by comparison with the analytical solution, and also the validity of fitting method was confirmed by the fitting of the concentration distribution calculated from known parameters. From the examination about the error, it was found that the error of the parameter obtained by using this code was smaller than that of the concentration distribution measured. (author)

  1. Artificial neural networks based early clinical prediction of mortality after spontaneous intracerebral hemorrhage.

    Science.gov (United States)

    Lukić, Stevo; Ćojbasić, Žarko; Perić, Zoran; Milošević, Zoran; Spasić, Mirjana; Pavlović, Vukašin; Milojević, Andrija

    2012-12-01

    Numerous outcome prediction models have been developed for mortality and functional outcome after spontaneous intracerebral haemorrhage (ICH). However, no outcome prediction model for ICH has considered the impact of care restriction. To develop and compare results of the artificial neural networks (ANN) and logistic regression (LR) models, based on initial clinical parameters, for prediction of mortality after spontaneous ICH. Analysis has been conducted on consecutive dataset of patients with spontaneous ICH, over 5-year period in tertiary care academic hospital. Patients older than 18 years were eligible for inclusion if they had been presented within 6 h from the start of symptoms and had evidence of spontaneous supratentorial ICH on initial brain computed tomography within 24 h. Initial clinical parameters have been used to develop LR and ANN prediction models for hospital mortality as outcome measure. Models have been accessed for discrimination and calibration abilities. We have analyzed 411 patients (199 males and 212 females) with spontaneous ICH, medically treated and not withdrawn from therapy, with average age of 67.35 years. From them, 256 (62.29%) patients died during hospital treatment and 155 (37.71%) patients survived. In the observed dataset, ANN model overall correctly classified outcome in 93.55% of patients, compared with 79.32% of correct classification for the LR model. Discrimination and calibration parameters indicate that both models show an adequate fit of expected and observed values, with superiority of ANN model. Our results favour the ANN model for prediction of mortality after spontaneous ICH. Further studies of the strengths and limitations of this method are needed with larger prospective samples. PMID:22674031

  2. Extended cross-section adjustment method to improve the prediction accuracy of core parameters

    International Nuclear Information System (INIS)

    An extended cross-section adjustment method has been developed to improve the prediction accuracy of target core parameters. The present method is on the basis of a cross-section adjustment method which minimizes the uncertainties of target core parameters under the conditions that integral experimental data are given. The present method enables us to enhance the prediction accuracy better than the conventional cross-section adjustment method by taking into account the target core parameters, as well as the extended bias factor method. In addition, it is proved that the present method is equivalent to the extended bias factor method when only one target core parameter is taken into account. The present method is implemented in an existing cross-section adjustment solver. Numerical calculations verify the derived formulation and demonstrate an applicability of an adjusted cross-section set which is specialized for the target core parameters. (author)

  3. Radiation-induced liver disease after stereotactic body radiotherapy for small hepatocellular carcinoma: clinical and dose-volumetric parameters

    International Nuclear Information System (INIS)

    To investigate the clinical and dose–volumetric parameters that predict the risk of radiation-induced liver disease (RILD) for patients with small, unresectable hepatocellular carcinoma (HCC) treated with stereotactic body radiotherapy (SBRT). Between March 2007 and December 2009, 92 patients with HCC treated with SBRT were reviewed for RILD within 3 months of completing treatment. RILD was evaluated according to the Common Terminology Criteria for Adverse Events, version 3.0. A dose of 10–20 Gy (median, 15 Gy) per fraction was given over 3–4 consecutive days for a total dose of 30–60 Gy (median, 45 Gy). The following clinical and dose–volumetric parameters were examined: age, gender, Child-Pugh class, presence of hepatitis B virus, gross tumor volume, normal liver volume, radiation dose, fraction size, mean dose to the normal liver, and normal liver volumes receiving from < 5 Gy to < 60 Gy (in increments of 5 Gy). Seventeen (18.5%) of the 92 patients developed grade 2 or worse RILD after SBRT (49 patients in grade 1, 11 in grade 2, and 6 in ≥ grade 3). On univariate analysis, Child-Pugh class was identified as a significant clinical parameter, while normal liver volume and normal liver volumes receiving from < 15 Gy to < 60 Gy were the significant dose–volumetric parameters. Upon multivariate analysis, only Child-Pugh class was a significant parameter for predicting grade 2 or worse RILD. The Child-Pugh B cirrhosis was found to have a significantly greater susceptibility to the development of grade 2 or worse RILD after SBRT in patients with small, unresectable HCC. Additional efforts aimed at testing other models to predict the risk of RILD in a large series of HCC patients treated with SBRT are needed

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

    OpenAIRE

    Simon van Mourik; Cajo ter Braak; Hans Stigter; Jaap Molenaar

    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 this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic...

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

    OpenAIRE

    Lo, Benjamin W. Y.; Hitoshi Fukuda; Yusuke Nishimura; Forough Farrokhyar; Lehana Thabane; Mitchell A. H. Levine

    2015-01-01

    Background: Clinical prediction tools assist in clinical outcome prediction. They quantify the relative contributions of certain variables and condense information that identifies important indicators or predictors to a targeted condition. This systematic review synthesizes and critically appraises the methodologic quality of studies that derive both clinical predictors and clinical predictor tools used to determine outcome prognosis in patients suffering from aneurysmal subarachnoid hemorrha...

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

    International Nuclear Information System (INIS)

    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.

  7. 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.

  8. 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.

  9. Differential diagnosis between Crohn’s disease and intestinal tuberculosis using integrated parameters including clinical manifestations, T-SPOT, endoscopy and CT enterography

    Science.gov (United States)

    Zhang, Tianyu; Fan, Rong; Wang, Zhengting; Hu, Shurong; Zhang, Maochen; Lin, Yun; Tang, Yonghua; Zhong, Jie

    2015-01-01

    Background: The aim of the study was to evaluate clinical manifestations, T-SPOT, endoscopy and CT enterography to differentiate Crohn’s disease (CD) from intestinal tuberculosis (ITB). Methods: 128 in patients with suspected CD and ITB were prospectively enrolled in the study. Demographic, clinical, laboratory, endoscopic and CT enterographic data were collected. After treatment for 6 months, when a definite diagnosis was reached, the differential diagnostic value of each parameter was analyzed. Multivariable logistic regression was used to analyze further, parameters of statistical significance to establish a mathematical regression equation. Receiver operating characteristic curves were plotted. Results: Clinical parameters helpful in differentiating CD from ITB included diarrhea, night sweat and perianal disease. Endoscopic parameters were useful in differentiating CD from ITB including transverse ulcers, longitudinal ulcers, rodent-like ulcers and patulous ileocecal valve. CT enterographic parameters aided the identification of the two conditions. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of a mathematical regression model established for 6 parameters of clinical endoscopy and CT enterography were 97.8%, 96.8%, 97.6%, 98.9% and 93.7% respectively, whereas those for T-SPOT were 96.8%, 91.3%, 92.7%, 78.9% and 98.8% respectively. Conclusions: T-SPOT is useful to exclude a diagnosis of ITB. Differentiating CD from ITB is a difficult clinical problem that requires a consideration of clinical, T-SPOT, endoscopic and CT enterographic parameters for accurate diagnosis. PMID:26770348

  10. 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; Jauberta, Jean-Noël; Gani, Rafiqul

    2015-01-01

    evidenced and fixed. A new class of purely predictive alpha functions was derived by applying group-contribution (GC) methods to the prediction of alpha function parameters. The interest of such an approach is discussed and compared to another predictive approach (use of generalized alpha functions coupled......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 and...... compared regarding their ability to reproduce vapor pressure, heat of vaporization, liquid heat capacity, liquid density and second virial coefficient data. To reach this objective, extensive databanks of alpha function parameters were created. In particular, pitfalls of Twu-type alpha functions were...

  11. 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.

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

    OpenAIRE

    Rucha Shah; Dhoom Singh Mehta

    2016-01-01

    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 in...

  13. Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth.

    Science.gov (United States)

    Frazier, Thomas W; Youngstrom, Eric A; Fristad, Mary A; Demeter, Christine; Birmaher, Boris; Kowatch, Robert A; Arnold, L Eugene; Axelson, David; Gill, Mary K; Horwitz, Sarah M; Findling, Robert L

    2014-01-01

    This report evaluates whether classification tree algorithms (CTA) may improve the identification of individuals at risk for bipolar spectrum disorders (BPSD). Analyses used the Longitudinal Assessment of Manic Symptoms (LAMS) cohort (629 youth, 148 with BPSD and 481 without BPSD). Parent ratings of mania symptoms, stressful life events, parenting stress, and parental history of mania were included as risk factors. Comparable overall accuracy was observed for CTA (75.4%) relative to logistic regression (77.6%). However, CTA showed increased sensitivity (0.28 vs. 0.18) at the expense of slightly decreased specificity and positive predictive power. The advantage of CTA algorithms for clinical decision making is demonstrated by the combinations of predictors most useful for altering the probability of BPSD. The 24% sample probability of BPSD was substantially decreased in youth with low screening and baseline parent ratings of mania, negative parental history of mania, and low levels of stressful life events (2%). High screening plus high baseline parent-rated mania nearly doubled the BPSD probability (46%). Future work will benefit from examining additional, powerful predictors, such as alternative data sources (e.g., clinician ratings, neurocognitive test data); these may increase the clinical utility of CTA models further. PMID:25143826

  14. Improving Clinical Prediction of Bipolar Spectrum Disorders in Youth

    Directory of Open Access Journals (Sweden)

    Thomas W. Frazier

    2014-03-01

    Full Text Available This report evaluates whether classification tree algorithms (CTA may improve the identification of individuals at risk for bipolar spectrum disorders (BPSD. Analyses used the Longitudinal Assessment of Manic Symptoms (LAMS cohort (629 youth, 148 with BPSD and 481 without BPSD. Parent ratings of mania symptoms, stressful life events, parenting stress, and parental history of mania were included as risk factors. Comparable overall accuracy was observed for CTA (75.4% relative to logistic regression (77.6%. However, CTA showed increased sensitivity (0.28 vs. 0.18 at the expense of slightly decreased specificity and positive predictive power. The advantage of CTA algorithms for clinical decision making is demonstrated by the combinations of predictors most useful for altering the probability of BPSD. The 24% sample probability of BPSD was substantially decreased in youth with low screening and baseline parent ratings of mania, negative parental history of mania, and low levels of stressful life events (2%. High screening plus high baseline parent-rated mania nearly doubled the BPSD probability (46%. Future work will benefit from examining additional, powerful predictors, such as alternative data sources (e.g., clinician ratings, neurocognitive test data; these may increase the clinical utility of CTA models further.

  15. 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)

  16. 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.

  17. Parameter estimation techniques and uncertainty in ground water flow model predictions

    International Nuclear Information System (INIS)

    Quantification of uncertainty in predictions of nuclear waste repository performance is a requirement of Nuclear Regulatory Commission regulations governing the licensing of proposed geologic repositories for high-level radioactive waste disposal. One of the major uncertainties in these predictions is in estimating the ground-water travel time of radionuclides migrating from the repository to the accessible environment. The cause of much of this uncertainty has been attributed to a lack of knowledge about the hydrogeologic properties that control the movement of radionuclides through the aquifers. A major reason for this lack of knowledge is the paucity of data that is typically available for characterizing complex ground-water flow systems. Because of this, considerable effort has been put into developing parameter estimation techniques that infer property values in regions where no measurements exist. Currently, no single technique has been shown to be superior or even consistently conservative with respect to predictions of ground-water travel time. This work was undertaken to compare a number of parameter estimation techniques and to evaluate how differences in the parameter estimates and the estimation errors are reflected in the behavior of the flow model predictions. That is, we wished to determine to what degree uncertainties in flow model predictions may be affected simply by the choice of parameter estimation technique used. 3 refs., 2 figs

  18. Application of ann for predicting water quality parameters in the mediterranean sea along gaza-palestine

    International Nuclear Information System (INIS)

    Seawater pollution problems are gaining interest world wide because of their health impacts and other environmental issues. Intense human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. This paper is concerned with the use of ANNs (Artificial Neural Networks) MLP ( Multilayer Perceptron) model for the prediction of pH and EC (Electrical Conductivity) in water quality parameters along Gaza city coast. MLP neural networks are trained and developed with reference to three major oceanographic parameters (water temperature, wind speed and turbidity) to predict the values of pH and EC; these parameters are considered as inputs of the neural network. The data collected comprised of four years and collected from nine locations along Gaza coastline. Results show that the model has high capability and accuracy in predicting both parameters. The network performance has been validated with different data sets and the results show satisfactory performance. Results of the developed model have been compared with multiple regression statistical models and found that MLP predictions are slightly better than the conventional methods. Prediction results prove that the proposed approach is suitable for modeling the water quality in the Mediterranean Sea along Gaza. (author)

  19. Application of GA–SVM method with parameter optimization for landslide development prediction

    OpenAIRE

    X. Z. LI; Kong, J. M.

    2014-01-01

    Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA–...

  20. Soil structure parameters in models of crop growth and yield prediction. physical submodels

    OpenAIRE

    Baranowski P.; Witkowska-Walczak B.; Walczak R.T.

    1997-01-01

    The role of chosen soil structure parameters in models of crop growth and yield prediction has been analysed on the base of the review of the latest literature. It was stated that the most frequently appearing soil parameters in the chosen models are soil water retention, rooting system, unsaturated and saturated hydraulic conductivity, bulk density or porosity. Comparison of submodels of physical processes in soil-plant-atmosphere continuum in chosen yield production models (CTSPAC, WOFOST, ...

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

    OpenAIRE

    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 val...

  2. Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation

    International Nuclear Information System (INIS)

    The filtering and predictive skill for turbulent signals is often limited by the lack of information about the true dynamics of the system and by our inability to resolve the assumed dynamics with sufficiently high resolution using the current computing power. The standard approach is to use a simple yet rich family of constant parameters to account for model errors through parameterization. This approach can have significant skill by fitting the parameters to some statistical feature of the true signal; however in the context of real-time prediction, such a strategy performs poorly when intermittent transitions to instability occur. Alternatively, we need a set of dynamic parameters. One strategy for estimating parameters on the fly is a stochastic parameter estimation through partial observations of the true signal. In this paper, we extend our newly developed stochastic parameter estimation strategy, the Stochastic Parameterization Extended Kalman Filter (SPEKF), to filtering sparsely observed spatially extended turbulent systems which exhibit abrupt stability transition from time to time despite a stable average behavior. For our primary numerical example, we consider a turbulent system of externally forced barotropic Rossby waves with instability introduced through intermittent negative damping. We find high filtering skill of SPEKF applied to this toy model even in the case of very sparse observations (with only 15 out of the 105 grid points observed) and with unspecified external forcing and damping. Additive and multiplicative bias corrections are used to learn the unknown features of the true dynamics from observations. We also present a comprehensive study of predictive skill in the one-mode context including the robustness toward variation of stochastic parameters, imperfect initial conditions and finite ensemble effect. Furthermore, the proposed stochastic parameter estimation scheme applied to the same spatially extended Rossby wave system demonstrates

  3. Capsaicin Cough Sensitivity and the Association with Clinical Parameters in Bronchiectasis

    OpenAIRE

    Guan, Wei-jie; Gao, Yong-hua; Xu, Gang; Lin, Zhi-ya; Tang, Yan; Li, Hui-min; Lin, Zhi-min; Zheng, Jin-ping; Chen, Rong-chang; Zhong, Nan-Shan

    2014-01-01

    Background Cough hypersensitivity has been common among respiratory diseases. Objective To determine associations of capsaicin cough sensitivity and clinical parameters in adults with clinically stable bronchiectasis. Methods We recruited 135 consecutive adult bronchiectasis patients and 22 healthy subjects. History inquiry, sputum culture, spirometry, chest high-resolution computed tomography (HRCT), Leicester Cough Questionnaire scoring, Bronchiectasis Severity Index (BSI) assessment and ca...

  4. Application of fuzzy inference system in the prediction of wave parameters

    Energy Technology Data Exchange (ETDEWEB)

    Kazeminezhad, M.H.; Etemad-Shahidi, A.; Mousavi, S.J. [Iran Univ. of Science and Technology, Structure and Hydrostructure Research Center, Tehran (Iran)

    2005-10-01

    Wave prediction is one of the most important issues in coastal and ocean engineering studies. In this study, the performance of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Coastal Engineering Manual (CEM) methods for predicting wave parameters were investigated. The data set used in this study comprises fetch-limited wave data and over water wind data gathered from deep-water location in Lake Ontario. The data set of year 2002 was used to develop the ANFIS models as wave predictor models. The data set of year 2003 was then used to test the developed ANFIS models and also the CEM method. Results indicate that ANFIS outperforms the CEM method in terms of prediction capability as the scatter index of predictions of ANFIS is less than that of CEM method. In particular, the CEM method overestimates the significant wave height and underestimates the peak spectral period, while ANFIS results in more accurate predictions. (Author)

  5. 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.

  6. 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...

  7. A systematic review of studies comparing diagnostic clinical prediction rules with clinical judgment.

    Directory of Open Access Journals (Sweden)

    Sharon Sanders

    Full Text Available Diagnostic clinical prediction rules (CPRs are developed to improve diagnosis or decrease diagnostic testing. Whether, and in what situations diagnostic CPRs improve upon clinical judgment is unclear.We searched MEDLINE, Embase and CINAHL, with supplementary citation and reference checking for studies comparing CPRs and clinical judgment against a current objective reference standard. We report 1 the proportion of study participants classified as not having disease who hence may avoid further testing and or treatment and 2 the proportion, among those classified as not having disease, who do (missed diagnoses by both approaches. 31 studies of 13 medical conditions were included, with 46 comparisons between CPRs and clinical judgment. In 2 comparisons (4%, CPRs reduced the proportion of missed diagnoses, but this was offset by classifying a larger proportion of study participants as having disease (more false positives. In 36 comparisons (78% the proportion of diagnoses missed by CPRs and clinical judgment was similar, and in 9 of these, the CPRs classified a larger proportion of participants as not having disease (fewer false positives. In 8 comparisons (17% the proportion of diagnoses missed by the CPRs was greater. This was offset by classifying a smaller proportion of participants as having the disease (fewer false positives in 2 comparisons. There were no comparisons where the CPR missed a smaller proportion of diagnoses than clinical judgment and classified more participants as not having the disease. The design of the included studies allows evaluation of CPRs when their results are applied independently of clinical judgment. The performance of CPRs, when implemented by clinicians as a support to their judgment may be different.In the limited studies to date, CPRs are rarely superior to clinical judgment and there is generally a trade-off between the proportion classified as not having disease and the proportion of missed diagnoses

  8. 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.)

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

    International Nuclear Information System (INIS)

    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.)

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

    Science.gov (United States)

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

    2015-11-01

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

  11. 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.

  12. Predictive maintenance of electrical components through computer-controlled acquisition and trending of standard circuit parameters

    International Nuclear Information System (INIS)

    Condition monitoring techniques are finding greater acceptance within many power plant maintenance organizations as plant operators develop predictive maintenance programs. This paper describes a proven condition monitoring technique and it's application in an electrical circuit predictive maintenance program. The technique performs a series of standard electrical measurements, under computer control, using a pre-defined test methodology. The test data is evaluated by utilizing statistical analysis methods and by comparisons through graphical representations. These methods allow comparisons with data from similar circuits or with baseline measurements. Circuit and component degradation appears as a change in one or more of the measured electrical parameters. Trending these parameters over time provides for early identification of circuit degradation. Incorporating this technique into a plant predictive maintenance program will switch maintenance personnel from a reactive to proactive position

  13. Pressurized water reactor core parameter prediction using an artificial neural network

    International Nuclear Information System (INIS)

    In pressurized water reactors, the fuel reloading problem has significant meaning in terms of both safety and economics. The local power peaking factor must be kept lower than a predetermined value during a cycle, and the effective multiplication factor must be maximized to extract the maximum energy. If these core parameters could be obtained in a very short time, the optimal fuel reloading patterns would be found more effectively and quickly. A very fast core parameter prediction system is developed using the back propagation neural network. This system predicts the core parameters several hundred times as fast as the reference numerical code, within an error of a few percent. The effects of the variation of the training rate coefficients, the momentum, and the hidden layer units are also discussed

  14. Correlations between Plasma Levels of Anionic Uremic Toxins and Clinical Parameters in Hemodialysis Patients.

    Science.gov (United States)

    Ichimura, Yuichi; Takamatsu, Hiroyuki; Ideuchi, Hideki; Oda, Masako; Takeda, Kiyotaka; Saitoh, Hiroshi

    2016-01-01

    When the kidney is seriously impaired, various uremic toxins (UTs) accumulate in the body, often exerting unfavorable effects on physiological functions and drug pharmacokinetics. To prevent this, it is important to determine plasma UT levels accurately in chronic kidney disease patients. Although attempts to predict plasma UT levels using biomarkers have been made, the correlation between UT levels and the markers is not yet fully understood. In this study, we assessed the correlations among plasma levels of indoxyl sulfate (IS), indoleacetic acid (IA), and 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF) in 20 hemodialysis patients and evaluated the relationship between the plasma levels of UTs and clinical parameters, such as serum creatinine (Scr), blood urea nitrogen, and estimated glomerular filtration rate (eGFR), with special focus on IS. There were no correlations among the plasma levels of the three UTs before and immediately after hemodialysis. However, a significant correlation was observed between plasma IS levels and Scr before hemodialysis (r=0.643, p=0.002), with the correlation becoming much stronger when using the data obtained immediately after hemodialysis (r=0.744, pScr values, although the precise mechanism behind the correlation remains to be clarified. PMID:27477735

  15. 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

  16. Interim-treatment quantitative PET parameters predict progression and death among patients with hodgkin's disease

    International Nuclear Information System (INIS)

    We hypothesized that quantitative PET parameters may have predictive value beyond that of traditional clinical factors such as the International Prognostic Score (IPS) among Hodgkin's disease (HD) patients. Thirty HD patients treated at presentation or relapse had staging and interim-treatment PET-CT scans. The majority of patients (53%) had stage III-IV disease and 67% had IPS ≥ 2. Interim-treatment scans were performed at a median of 55 days from the staging PET-CT. Chemotherapy regimens used: Stanford V (67%), ABVD (17%), VAMP (10%), or BEACOPP (7%). Hypermetabolic tumor regions were segmented semiautomatically and the metabolic tumor volume (MTV), mean standardized uptake value (SUVmean), maximum SUV (SUVmax) and integrated SUV (iSUV) were recorded. We analyzed whether IPS, absolute value PET parameters or the calculated ratio of interim- to pre-treatment PET parameters were associated with progression free survival (PFS) or overall survival (OS). Median follow-up of the study group was 50 months. Six of the 30 patients progressed clinically. Absolute value PET parameters from pre-treatment scans were not significant. Absolute value SUVmax from interim-treatment scans was associated with OS as determined by univariate analysis (p < 0.01). All four calculated PET parameters (interim/pre-treatment values) were associated with OS: MTVint/pre (p < 0.01), SUVmeanint/pre (p < 0.05), SUVmaxint/pre (p = 0.01), and iSUVint/pre (p < 0.01). Absolute value SUVmax from interim-treatment scans was associated with PFS (p = 0.01). Three calculated PET parameters (int/pre-treatment values) were associated with PFS: MTVint/pre (p = 0.01), SUVmaxint/pre (p = 0.02) and iSUVint/pre (p = 0.01). IPS was associated with PFS (p < 0.05) and OS (p < 0.01). Calculated PET metrics may provide predictive information beyond that of traditional clinical factors and may identify patients at high risk of treatment failure early for treatment intensification

  17. Parametric computation predicts a multiplicative interaction between synaptic strength parameters that controls properties of gamma oscillations

    Directory of Open Access Journals (Sweden)

    Jordan eChambers

    2012-07-01

    Full Text Available Gamma oscillations are thought to be critical for a number of behavioural functions, they occur in many regions of the brain and through a variety of mechanisms. Fast repetitive bursting (FRB neurons in layer 2 of the cortex are able to drive gamma oscillations over long periods of time. Even though the oscillation is driven by FRB neurons, strong feedback within the rest of the cortex must modulate properties of the oscillation such as frequency and power. We used a highly detailed model of the cortex to determine how a cohort of 33 parameters controlling synaptic drive might modulate gamma oscillation properties. We were interested in determining not just the effects of parameters individually, but we also wanted to reveal interactions between parameters beyond additive effects. To prevent a combinatorial explosion in parameter combinations that might need to be simulated, we used a fractional factorial design that estimated the effects of individual parameters and two parameter interactions. This experiment required only 4096 model runs. We found that the largest effects on both gamma power and frequency came from a complex interaction between efficacy of synaptic connections from layer 2 inhibitory neurons to layer 2 excitatory neurons and the parameter for the reciprocal connection. As well as the effect of the individual parameters determining synaptic efficacy, there was multiplicative an interaction between these parameters beyond the additive effects of the parameters alone. The magnitude of this effect was similar to that of the individual parameters, predicting that it is physiologically important in setting gamma oscillation properties.

  18. Clinical Nomogram for Predicting Survival of Esophageal Cancer Patients after Esophagectomy

    Science.gov (United States)

    Cao, Jinlin; Yuan, Ping; Wang, Luming; Wang, Yiqing; Ma, Honghai; Yuan, Xiaoshuai; Lv, Wang; Hu, Jian

    2016-01-01

    The aim of this study was to construct an effective clinical nomogram for predicting the survival of esophageal cancer patients after esophagectomy. We identified esophageal cancer patients (n = 4,281) who underwent esophagectomy between 1988 and 2007 from the Surveillance, Epidemiology, and End Results (SEER) 18 registries database. Clinically significant parameters for survival were used to construct a nomogram based on Cox regression analyses. The model was validated using bootstrap resampling and a Chinese cohort (n = 145). A total of 4,109 patients from the SEER database were included for analysis. The multivariate analyses showed that the factors of age, race, histology, tumor site, tumor size, grade and depth of invasion, and the numbers of metastases and retrieved nodes were independent prognostic factors. All of these factors were selected into the nomogram. The nomogram showed a clear prognostic superiority over the seventh AJCC-TNM classification (C-index: SEER cohort, 0.716 vs 0.693, respectively; P nomogram predicted the probabilities of 3- and 5-year survival, which corresponded closely with the actual survival rates. This novel prognostic model may improve clinicians’ abilities to predict individualized survival and to make treatment recommendations. PMID:27215834

  19. 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.

  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. An Approach for Improving Prediction in River System Models Using Bayesian Probabilities of Parameter Performance

    Science.gov (United States)

    Kim, S. S. H.; Hughes, J. D.; Chen, J.; Dutta, D.; Vaze, J.

    2014-12-01

    Achieving predictive success is a major challenge in hydrological modelling. Predictive metrics indicate whether models and parameters are appropriate for impact assessment, design, planning and management, forecasting and underpinning policy. It is often found that very different parameter sets and model structures are equally acceptable system representations (commonly described as equifinality). Furthermore, parameters that produce the best goodness of fit during a calibration period may often yield poor results outside of that period. A calibration method is presented that uses a recursive Bayesian filter to estimate the probability of consistent performance of parameter sets in different sub-periods. The result is a probability distribution for each specified performance interval. This generic method utilises more information within time-series data than what is typically used for calibrations, and could be adopted for different types of time-series modelling applications. Where conventional calibration methods implicitly identify the best performing parameterisations on average, the new method looks at the consistency of performance during sub-periods. The proposed calibration method, therefore, can be used to avoid heavy weighting toward rare periods of good agreement. The method is trialled in a conceptual river system model called the Australian Water Resources Assessments River (AWRA-R) model in the Murray-Darling Basin, Australia. The new method is tested via cross-validation and results are compared to a traditional split-sample calibration/validation to evaluate the new technique's ability to predict daily streamflow. The results showed that the new calibration method could produce parameterisations that performed better in validation periods than optimum calibration parameter sets. The method shows ability to improve on predictive performance and provide more realistic flux terms compared to traditional split-sample calibration methods.

  2. Clinical implications in the use of the PBC algorithm versus the AAA by comparison of different NTCP models/parameters

    International Nuclear Information System (INIS)

    Retrospective analysis of 3D clinical treatment plans to investigate qualitative, possible, clinical consequences of the use of PBC versus AAA. The 3D dose distributions of 80 treatment plans at four different tumour sites, produced using PBC algorithm, were recalculated using AAA and the same number of monitor units provided by PBC and clinically delivered to each patient; the consequences of the difference on the dose-effect relations for normal tissue injury were studied by comparing different NTCP model/parameters extracted from a review of published studies. In this study the AAA dose calculation is considered as benchmark data. The paired Student t-test was used for statistical comparison of all results obtained from the use of the two algorithms. In the prostate plans, the AAA predicted lower NTCP value (NTCPAAA) for the risk of late rectal bleeding for each of the seven combinations of NTCP parameters, the maximum mean decrease was 2.2%. In the head-and-neck treatments, each combination of parameters used for the risk of xerostemia from irradiation of the parotid glands involved lower NTCPAAA, that varied from 12.8% (sd=3.0%) to 57.5% (sd=4.0%), while when the PBC algorithm was used the NTCPPBC’s ranging was from 15.2% (sd=2.7%) to 63.8% (sd=3.8%), according the combination of parameters used; the differences were statistically significant. Also NTCPAAA regarding the risk of radiation pneumonitis in the lung treatments was found to be lower than NTCPPBC for each of the eight sets of NTCP parameters; the maximum mean decrease was 4.5%. A mean increase of 4.3% was found when the NTCPAAA was calculated by the parameters evaluated from dose distribution calculated by a convolution-superposition (CS) algorithm. A markedly different pattern was observed for the risk relating to the development of pneumonitis following breast treatments: the AAA predicted higher NTCP value. The mean NTCPAAA varied from 0.2% (sd = 0.1%) to 2.1% (sd = 0.3%), while the mean NTCPPBC

  3. Effects of time-averaging climate parameters on predicted multicompartmental fate of pesticides and POPs

    International Nuclear Information System (INIS)

    With the aim to investigate the justification of time-averaging of climate parameters in multicompartment modelling the effects of various climate parameters and different modes of entry on the predicted substances' total environmental burdens and the compartmental fractions were studied. A simple, non-steady state zero-dimensional (box) mass-balance model of intercompartmental mass exchange which comprises four compartments was used for this purpose. Each two runs were performed, one temporally unresolved (time-averaged conditions) and a time-resolved (hourly or higher) control run. In many cases significant discrepancies are predicted, depending on the substance and on the parameter. We find discrepancies exceeding 10% relative to the control run and up to an order of magnitude for prediction of the total environmental burden from neglecting seasonalities of the soil and ocean temperatures and the hydroxyl radical concentration in the atmosphere and diurnalities of atmospheric mixing depth and the hydroxyl radical concentration in the atmosphere. Under some conditions it was indicated that substance sensitivity could be explained by the magnitude of the sink terms in the compartment(s) with parameters varying. In general, however, any key for understanding substance sensitivity seems not be linked in an easy manner to the properties of the substance, to the fractions of its burden or to the sink terms in either of the compartments with parameters varying. Averaging of diurnal variability was found to cause errors of total environmental residence time of different sign for different substances. The effects of time-averaging of several parameters are in general not additive but synergistic as well as compensatory effects occur. An implication of these findings is that the ranking of substances according to persistence is sensitive to time resolution on the scale of hours to months. As a conclusion it is recommended to use high temporal resolution in multi

  4. Design Characteristics Influence Performance of Clinical Prediction Rules in Validation: A Meta-Epidemiological Study

    OpenAIRE

    Ban, J-W.; Emparanza, J I; Urreta, I.; Burls, A

    2016-01-01

    BACKGROUND: Many new clinical prediction rules are derived and validated. But the design and reporting quality of clinical prediction research has been less than optimal. We aimed to assess whether design characteristics of validation studies were associated with the overestimation of clinical prediction rules' performance. We also aimed to evaluate whether validation studies clearly reported important methodological characteristics. METHODS: Electronic databases were searched for system...

  5. 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.

  6. 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

  7. Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor

    OpenAIRE

    Dipankar SENGUPTA; Sood, Meemansa; Vijayvargia, Poorvika; Hota, Sunil; Naik, Pradeep K

    2013-01-01

    Healthcare sector is generating a large amount of information corresponding to diagnosis, disease identification and treatment of an individual. Mining knowledge and providing scientific decision-making for the diagnosis & treatment of disease from the clinical dataset is therefore increasingly becoming necessary. Aim of this study was to assess the applicability of knowledge discovery in brain tumor data warehouse, applying data mining techniques for investigation of clinical parameters that...

  8. 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)

  9. Endometrial thickness, Caucasian ethnicity, and age predict clinical pregnancy following fresh blastocyst embryo transfer: a retrospective cohort

    Directory of Open Access Journals (Sweden)

    Santoro Nanette

    2009-04-01

    Full Text Available Abstract Background In-vitro fertilization (IVF with blastocyst as opposed to cleavage stage embryos has been advocated to improve success rates. Limited information exists on which to predict which patients undergoing blastocyst embryo transfer (BET will achieve pregnancy. This study's objective was to evaluate the predictive value of patient and cycle characteristics for clinical pregnancy following fresh BET. Methods This was a retrospective cohort study from 2003–2007 at an academic assisted reproductive program. 114 women with infertility underwent fresh IVF with embryo transfer. We studied patients undergoing transfer of embryos at the blastocyst stage of development. Our main outcome of interest was clinical pregnancy. Clinical pregnancy and its associations with patient characteristics (age, body mass index, FSH, ethnicity and cycle parameters (thickness of endometrial stripe, number eggs, available cleaving embryos, number blastocysts available, transferred, and cryopreserved, and embryo quality were examined using Student's T test and Mann-Whitney-U tests as appropriate. Multivariable logistic regression models were created to determine independent predictors of CP following BET. Receiver Operating Characteristic analyses were used to determine the optimal thickness of endometrial stripe for predicting clinical pregnancy. Results Patients achieving clinical pregnancy demonstrated a thicker endometrial stripe and were younger preceding embryo transfer. On multivariable logistic regression analyses, Caucasian ethnicity (OR 2.641, 95% CI 1.054–6.617, thickness of endometrial stripe, (OR 1.185, 95% CI 1.006–1.396 and age (OR 0.879, 95% CI 0.789–0.980 predicted clinical pregnancy. By receiver operating characteristic analysis, endometrial stripe ≥ 9.4 mm demonstrated a sensitivity of 83% for predicting clinical pregnancy following BET. Conclusion In a cohort of patients undergoing fresh BET, thicker endometrial stripe, Caucasian

  10. Cardiac magnetic resonance imaging parameters as surrogate endpoints in clinical trials of acute myocardial infarction

    OpenAIRE

    Gutberlet Matthias; Lurz Philipp; Fuernau Georg; de Waha Suzanne; Eitel Ingo; Desch Steffen; Schuler Gerhard; Thiele Holger

    2011-01-01

    Abstract Cardiac magnetic resonance (CMR) offers a variety of parameters potentially suited as surrogate endpoints in clinical trials of acute myocardial infarction such as infarct size, myocardial salvage, microvascular obstruction or left ventricular volumes and ejection fraction. The present article reviews each of these parameters with regard to the pathophysiological basis, practical aspects, validity, reliability and its relative value (strengths and limitations) as compared to competit...

  11. 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.

  12. Prediction of Parameters Distribution of Upward Boiling Two-Phase Flow With Two-Fluid Models

    International Nuclear Information System (INIS)

    In this paper, a multidimensional two-fluid model with additional turbulence k - ε equations is used to predict the two-phase parameters distribution in freon R12 boiling flow. The 3D module of the CATHARE code is used for numerical calculation. The DEBORA experiment has been chosen to evaluate our models. The radial profiles of the outlet parameters were measured by means of an optical probe. The comparison of the radial profiles of void fraction, liquid temperature, gas velocity and volumetric interfacial area at the end of the heated section shows that the multidimensional two-fluid model with proper constitutive relations can yield reasonably predicted results in boiling conditions. Sensitivity tests show that the turbulent dispersion force, which involves the void fraction gradient, plays an important role in determining the void fraction distribution; and the turbulence eddy viscosity is a significant factor to influence the liquid temperature distribution. (authors)

  13. 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.

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

    OpenAIRE

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

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

    OpenAIRE

    Bambang Wahono; Kristian Ismail; Harutoshi Ogai

    2015-01-01

    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 t...

  16. Predictive analysis of combined burner parameter effects on oxy-fuel flames

    OpenAIRE

    Boushaki, T.; Guessasma, S.; Sautet, J.C.

    2010-01-01

    Abstract The present paper aims at studying the influence of burner parameters with a separated jet configuration, namely nozzles diameters and separation distance between the jets, on the flame characteristics (lift-off positions of flame and flame length). The experimental layout considers the use of OH-chemilumenescence to measure the flame characteristics for different combinations of processing conditions. The predictive analysis is based on a neural computation that considers...

  17. Predicting the Flory-Huggins χ Parameter for Polymers with Stiffness Mismatch from Molecular Dynamics Simulations

    OpenAIRE

    Daniel J. Kozuch; Wenlin Zhang; Milner, Scott T.

    2016-01-01

    The Flory–Huggins χ parameter describes the excess free energy of mixing and governs phase behavior for polymer blends and block copolymers. For chemically-distinct nonpolar polymers, the value of χ is dominated by the mismatch in cohesive energy densities of the monomers. For blends of chemically-similar polymers, the entropic portion of χ, arising from non-ideal local packing, becomes more significant. Using polymer field theory, Fredrickson et al. predicted that a difference in backbone st...

  18. Predicting Nitrate Concentration in Groundwater Resources using Lumped-Parameter Model: Case Study in Qazvin Plain

    OpenAIRE

    R.S Hajimirmohammad Ali; H Karyab

    2016-01-01

    Background and Objective: The concentration of nitrate, factors affecting the balance sheet, and the changes in an aquifer is of utmost importance. Because modeling is an efficient method to predict the concentration of ions in water resources, in this study using lumped-parameter model and Monte Carlo simulation model, the nitrate concentrations in groundwater resources of Qazvin Plain were estimated and analyzed. Materials and Methods: A total of 19 wells in different climates of saline...

  19. A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

    OpenAIRE

    Dangdang Du; Xiaoliang Jia; Chaobo Hao

    2016-01-01

    Aiming at the nonlinearity, chaos, and small-sample of aero engine performance parameters data, a new ensemble model, named the least squares support vector machine (LSSVM) ensemble model with phase space reconstruction (PSR) and particle swarm optimization (PSO), is presented. First, to guarantee the diversity of individual members, different single kernel LSSVMs are selected as base predictors, and they also output the primary prediction results independently. Then, all the primary predicti...

  20. 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.

  1. A simple methodology for predicting laser-weld properties from material and laser parameters

    International Nuclear Information System (INIS)

    In laser material processing, understanding the laser interaction and the effect of processing parameters on this interaction is fundamental to any process if the system is to be optimized. Expanding this to different materials or other laser systems with different beam characteristics makes this interaction more complex and difficult to resolve. This work presents a relatively simple physical model to understand these interactions in terms of mean surface enthalpy values derived from both material parameters and laser parameters. From these fundamental properties the melt depth and width for any material can be predicted using a simple theory. By considering the mean enthalpy of the surface, the transition from conduction limited melting to keyholing can also be accurately predicted. The theory is compared to experimental results and the predicted and observed data are shown to correspond well for these experimental results as well as for published results for stainless steel and for a range of metals. The results suggest that it is important to keep the Fourier number of the weld much smaller than one to make it efficient. It is also discussed that the surface enthalpy could be used to prodict other effects in the weld such as porosity and material expulsion.

  2. Safety analysis methodology with assessment of the impact of the prediction errors of relevant parameters

    International Nuclear Information System (INIS)

    The best estimate plus uncertainty approach (BEAU) requires the use of extensive resources and therefore it is usually applied for cases in which the available safety margin obtained with a conservative methodology can be questioned. Outside the BEAU methodology, there is not a clear approach on how to deal with the issue of considering the uncertainties resulting from prediction errors in the safety analyses performed for licensing submissions. However, the regulatory document RD-310 mentions that the analysis method shall account for uncertainties in the analysis data and models. A possible approach is presented, that is simple and reasonable, representing just the author's views, to take into account the impact of prediction errors and other uncertainties when performing safety analysis in line with regulatory requirements. The approach proposes taking into account the prediction error of relevant parameters. Relevant parameters would be those plant parameters that are surveyed and are used to initiate the action of a mitigating system or those that are representative of the most challenging phenomena for the integrity of a fission barrier. Examples of the application of the methodology are presented involving a comparison between the results with the new approach and a best estimate calculation during the blowdown phase for two small breaks in a generic CANDU 6 station. The calculations are performed with the CATHENA computer code. (author)

  3. Cardiac magnetic resonance imaging parameters as surrogate endpoints in clinical trials of acute myocardial infarction

    Directory of Open Access Journals (Sweden)

    Gutberlet Matthias

    2011-09-01

    Full Text Available Abstract Cardiac magnetic resonance (CMR offers a variety of parameters potentially suited as surrogate endpoints in clinical trials of acute myocardial infarction such as infarct size, myocardial salvage, microvascular obstruction or left ventricular volumes and ejection fraction. The present article reviews each of these parameters with regard to the pathophysiological basis, practical aspects, validity, reliability and its relative value (strengths and limitations as compared to competitive modalities. Randomized controlled trials of acute myocardial infarction which have used CMR parameters as a primary endpoint are presented.

  4. Analysis Of Clinical, Haematological And Biochemical Parameters In Patients With Infectious Mononucleosis

    Directory of Open Access Journals (Sweden)

    Canović Petar

    2015-12-01

    Full Text Available Primary infection with Epstein-Barr virus (EBV usually occurs in early childhood and often does not present clinical symptoms. More than 90% of adults are infected with this virus. A primary infection that occurs in adolescence or adulthood is usually clinically presented as infectious mononucleosis with a triad of symptoms: fever, lymphadenopathy and pharyngitis. Our retrospective study included 51 patients with a median age of 17 (9-23 years and serologically confirmed infectious mononucleosis. All patients with infectious mononucleosis were treated at the Clinic for Infectious Diseases at the Clinical Center in Kragujevac during 2013. We analysed the clinical, haematological and laboratory parameters of patients. The aspartate-aminotransferase levels were increased in 40 patients, with a mean value of 116.24 (±93.22; the alanine-aminotransferase levels were increased in 44 patients, with a mean value of 189.24 (±196.69. Lymphadenopathy was the most common clinical feature upon admission in 49 patients (96%; 38 patients (74.5% had splenomegaly, and 20 (39% had hepatomegaly. Twenty-six patients (51% had leukocytosis with lymphocytosis, while 15 (75% of the 20 who had a normal leukocyte count also had lymphocytosis. In the present study, we updated the clinical, haematological and laboratory parameters, which may lead to the establishment of an accurate diagnosis and promote further treatment of the patients.

  5. 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; Arendt-Nielsen, Lars

    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 was...... used to investigate differences in pain sensitivity between low-back pain (LBP) sub-groups and was correlated with important clinical parameters.......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 was...

  6. Prediction of Aqueous Solubility for 209 Polychlorinated Diphenyl Ethers from Molecular Structural Parameters by DFT Method

    Institute of Scientific and Technical Information of China (English)

    XIE Ya-Jie; LIU Hong-Xia; WANG Zun-Yao; ZHU Li-Dan

    2008-01-01

    Optimized calculations of 209 polychlorinated diphenyl ethers (PCDEs) and diphenyl ethers were carried out at the B3LYP/6-31G* level with the Gaussian 98 program. Based on the theoretical linear solvation energy relationship (TLSER) model, the obtained structural parameters were taken as theoretical descriptors to establish the novel QSPR model for predicting aqueous solubility (-lgSw) of PCDEs. The model obtained in this work contains two variables: mean molecular polarizability (α) and the most positive partial charge on a hydrogen atom (qH+), of which R2 = 0.9606 and SD = 0.32. And the results of cross-validation test also show that the model exhibits optimum stability and better predictive power. Moreover, the predictive power of the new model is better than that of MCIs method.

  7. A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

    Directory of Open Access Journals (Sweden)

    Dangdang Du

    2016-01-01

    Full Text Available Aiming at the nonlinearity, chaos, and small-sample of aero engine performance parameters data, a new ensemble model, named the least squares support vector machine (LSSVM ensemble model with phase space reconstruction (PSR and particle swarm optimization (PSO, is presented. First, to guarantee the diversity of individual members, different single kernel LSSVMs are selected as base predictors, and they also output the primary prediction results independently. Then, all the primary prediction results are integrated to produce the most appropriate prediction results by another particular LSSVM—a multiple kernel LSSVM, which reduces the dependence of modeling accuracy on kernel function and parameters. Phase space reconstruction theory is applied to extract the chaotic characteristic of input data source and reconstruct the data sample, and particle swarm optimization algorithm is used to obtain the best LSSVM individual members. A case study is employed to verify the effectiveness of presented model with real operation data of aero engine. The results show that prediction accuracy of the proposed model improves obviously compared with other three models.

  8. An investigation of engine performance parameters and artificial intelligent emission prediction of hydrogen powered car

    International Nuclear Information System (INIS)

    With the depletion of fossil fuel resources and the potential consequences of climate change due to fossil fuel use, much effort has been put into the search for alternative fuels for transportation. Although there are several potential alternative fuels, which have low impact on the environment, none of these fuels have the ability to be used as the sole 'fuel of the future'. One fuel which is likely to become a part of the over all solution to the transportation fuel dilemma is hydrogen. In this paper, The Toyota Corolla four cylinder, 1.8 l engine running on petrol is systematically converted to run on hydrogen. Several ancillary instruments for measuring various engine operating parameters and emissions are fitted to appraise the performance of the hydrogen car. The effect of hydrogen as a fuel compares with gasoline on engine operating parameters and effect of engine operating parameters on emission characteristics is discussed. Based on the experimental setup, a suite of neural network models were tested to accurately predict the effect of major engine operating conditions on the hydrogen car emissions. Predictions were found to be ±4% to the experimental values. This work provided better understanding of the effect of engine process parameters on emissions. (author)

  9. Development of Health Parameter Model for Risk Prediction of CVD Using SVM.

    Science.gov (United States)

    Unnikrishnan, P; Kumar, D K; Poosapadi Arjunan, S; Kumar, H; Mitchell, P; Kawasaki, R

    2016-01-01

    Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD). The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model. PMID:27594895

  10. Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

    Science.gov (United States)

    Kaklamanos, James; Baise, Laurie G.; Boore, David M.

    2011-01-01

    The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

  11. Lipocalin-2 as an Infection-Related Biomarker to Predict Clinical Outcome in Ischemic Stroke

    Science.gov (United States)

    Hochmeister, Sonja; Engel, Odilo; Adzemovic, Milena Z.; Pekar, Thomas; Kendlbacher, Paul; Zeitelhofer, Manuel; Haindl, Michaela; Meisel, Andreas; Fazekas, Franz; Seifert-Held, Thomas

    2016-01-01

    Objectives From previous data in animal models of cerebral ischemia, lipocalin-2 (LCN2), a protein related to neutrophil function and cellular iron homeostasis, is supposed to have a value as a biomarker in ischemic stroke patients. Therefore, we examined LCN2 expression in the ischemic brain in an animal model and measured plasma levels of LCN2 in ischemic stroke patients. Methods In the mouse model of transient middle cerebral artery occlusion (tMCAO), LCN2 expression in the brain was analyzed by immunohistochemistry and correlated to cellular nonheme iron deposition up to 42 days after tMCAO. In human stroke patients, plasma levels of LCN2 were determined one week after ischemic stroke. In addition to established predictive parameters such as age, National Institutes of Health Stroke Scale and thrombolytic therapy, LCN2 was included into linear logistic regression modeling to predict clinical outcome at 90 days after stroke. Results Immunohistochemistry revealed expression of LCN2 in the mouse brain already at one day following tMCAO, and the amount of LCN2 subsequently increased with a maximum at 2 weeks after tMCAO. Accumulation of cellular nonheme iron was detectable one week post tMCAO and continued to increase. In ischemic stroke patients, higher plasma levels of LCN2 were associated with a worse clinical outcome at 90 days and with the occurrence of post-stroke infections. Conclusions LCN2 is expressed in the ischemic brain after temporary experimental ischemia and paralleled by the accumulation of cellular nonheme iron. Plasma levels of LCN2 measured in patients one week after ischemic stroke contribute to the prediction of clinical outcome at 90 days and reflect the systemic response to post-stroke infections. PMID:27152948

  12. CLINICAL DATABASE ANALYSIS USING DMDT BASED PREDICTIVE MODELLING

    Directory of Open Access Journals (Sweden)

    Srilakshmi Indrasenan

    2013-04-01

    Full Text Available In recent years, predictive data mining techniques play a vital role in the field of medical informatics. These techniques help the medical practitioners in predicting various classes which is useful in prediction treatment. One of such major difficulty is prediction of survival rate in breast cancer patients. Breast cancer is a common disease these days and fighting against it is a tough battle for both the surgeons and the patients. To predict the survivability rate in breast cancer patients which helps the medical practitioner to select the type of treatment a predictive data mining technique called Diversified Multiple Decision Tree (DMDT classification is used. Additionally, to avoid difficulties from the outlier and skewed data, it is also proposed to perform the improvement of training space by outlier filtering and over sampling. As a result, this novel approach gives the survivability rate of the cancer patients based on which the medical practitioners can choose the type of treatment.

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

    International Nuclear Information System (INIS)

    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.

  14. 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.

  15. 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.

  16. The use of blood gas parameters to predict ascites susceptibility in juvenile broilers.

    Science.gov (United States)

    van As, P; Elferink, M G; Closter, A M; Vereijken, A; Bovenhuis, H; Crooijmans, R P M A; Decuypere, E; Groenen, M A M

    2010-08-01

    Ascites syndrome is a metabolic disorder found in modern broilers that have insufficient pulmonary vascular capacity. Commercial breeding programs have heavily focused on high growth rate, which led to fast-growing chickens, but as a negative consequence, the incidence of ascites syndrome increased. However, not all birds with a high growth rate will suffer from ascites syndrome, which might indicate a genetic susceptibility to ascites. Information on blood gas parameters measured early in life and their relation to ascites susceptibility is expected to contribute to identification on the cause of ascites syndrome. In this study, several physiological parameters, such as blood gas parameters [pH, partial pressure of CO(2) in venous blood (pvCO(2)), and partial pressure of O(2) in venous blood], hematocrit, electrolytes (Na(+), Ca(2+), and K(+)), metabolites (lactate and glucose), were measured at d 11 to 12 of age from 100 female and 100 male broilers. From d 14 onward, the birds were challenged to provoke the development of ascites syndrome. Our results showed that high pvCO(2) values together with low pH values (males) or high pH values (females) in the venous blood of juvenile broilers coincided with ascites. Therefore, blood pvCO(2) and pH in both juvenile male and female broilers seem to be critical factors in ascites pathophysiology and can be used as phenotypic traits to predict ascites susceptibility in juvenile broilers at d 11 to 12. A prediction model was built on a subpopulation of the broilers without any loss in sensitivity (0.52) and specificity (0.78) when applied to the validation population. The parameter sex was included in the prediction model because levels of pvCO(2) and pH that associated with ascites susceptibility are different between males and females. Commercial breeders can include these phenotypic traits in their genetic selection programs to reduce the incidence of ascites syndrome. PMID:20634524

  17. Prediction of biomechanical parameters in the lumbar spine during static sagittal plane lifting.

    Science.gov (United States)

    Kong, W Z; Goel, V K; Gilbertson, L G

    1998-04-01

    A combined approach involving optimization and the finite element technique was used to predict biomechanical parameters in the lumbar spine during static lifting in the sagittal plane. Forces in muscle fascicles of the lumbar region were first predicted using an optimization-based force model including the entire lumbar spine. These muscle forces as well as the distributed upper body weight and the lifted load were then applied to a three-dimensional finite element model of the thoracolumbar spine and rib cage to predict deformation, the intradiskal pressure, strains, stresses, and load transfer paths in the spine. The predicted intradiskal pressures in the L3-4 disk at the most deviated from the in vivo measurements by 8.2 percent for the four lifting cases analyzed. The lumbosacral joint flexed, while the other lumbar joints extended for all of the four lifting cases studied (rotation of a joint is the relative rotation between its two vertebral bodies). High stresses were predicted in the posterolateral regions of the endplates and at the junctions of the pedicles and vertebral bodies. High interlaminar shear stresses were found in the posterolateral regions of the lumbar disks. While the facet joints of the upper two lumbar segments did not transmit any load, the facet joints of the lower two lumbar segments experienced significant loads. The ligaments of all lumbar motion segments except the lumbosacral junction provided only marginal moments. The limitations of the current model and possible improvements are discussed. PMID:10412390

  18. Incidence and clinical vital parameters in primary ketosis of Murrah buffaloes

    Directory of Open Access Journals (Sweden)

    Ankit Kumar

    2015-09-01

    Full Text Available Aim: The present study was undertaken to ascertain the incidence and clinical vital parameters in cases of primary ketosis in Murrah buffaloes brought to teaching veterinary clinical complex, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar and from adjoining villages of the district Hisar, Haryana, India. Materials and Methods: The investigation was conducted on 24 clinical cases (out of total 145 screened of primary ketosis. The diagnosis was confirmed on the basis of clinical signs and significantly positive two tests for ketone bodies in urine (Rothera’s and Keto-Diastix strip test. Data collected were statistically analyzed using independent Student’s t-test. Results: Overall incidence of disease in these areas was found to be 16.55% and all the animals were recently parturited (mean: 1.42±0.14 month, on an average in their third lactation (mean: 2.38±0.30 and exhibited clinical signs such as selective anorexia (refusal to feed on concentrate diet, drastic reduction in milk yield (mean: 64.4±5.35%, ketotic odor from urine, breath, and milk and rapid loss of body condition. All the clinical vital parameters in ketotic buffaloes (body temperature, heart rate, respiration rate, and rumen movements were within normal range. Conclusion: Primary ketosis in Murrah buffaloes was the most common seen in the third lactation, within the first 2 months after parturition with characteristics clinical signs and no variability in vital parameters. The disease has severe effect on the production status of affected animal.

  19. 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.

  20. 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.

  1. Dynamic interactions between hydrogeological and exposure parameters in daily dose prediction under uncertainty and temporal variability

    International Nuclear Information System (INIS)

    Highlights: • Dynamic parametric interaction in daily dose prediction under uncertainty. • Importance of temporal dynamics associated with the dose. • Different dose experienced by different population cohorts as a function of time. • Relevance of uncertainty reduction in the input parameters shows temporal dynamism. -- Abstract: We study the time dependent interaction between hydrogeological and exposure parameters in daily dose predictions due to exposure of humans to groundwater contamination. Dose predictions are treated stochastically to account for an incomplete hydrogeological and geochemical field characterization, and an incomplete knowledge of the physiological response. We used a nested Monte Carlo framework to account for uncertainty and variability arising from both hydrogeological and exposure variables. Our interest is in the temporal dynamics of the total dose and their effects on parametric uncertainty reduction. We illustrate the approach to a HCH (lindane) pollution problem at the Ebro River, Spain. The temporal distribution of lindane in the river water can have a strong impact in the evaluation of risk. The total dose displays a non-linear effect on different population cohorts, indicating the need to account for population variability. We then expand the concept of Comparative Information Yield Curves developed earlier (see de Barros et al. [29]) to evaluate parametric uncertainty reduction under temporally variable exposure dose. Results show that the importance of parametric uncertainty reduction varies according to the temporal dynamics of the lindane plume. The approach could be used for any chemical to aid decision makers to better allocate resources towards reducing uncertainty

  2. 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.

  3. A predictive thermal dynamic model for parameter generation in the laser assisted direct write process

    International Nuclear Information System (INIS)

    The laser assisted direct write (LADW) method can be used to generate electrical circuitry on a substrate by depositing metallic ink and curing the ink thermally by a laser. Laser curing has emerged over recent years as a novel yet efficient alternative to oven curing. This method can be used in situ, over complicated 3D contours of large parts (e.g. aircraft wings) and selectively cure over heat sensitive substrates, with little or no thermal damage. In previous studies, empirical methods have been used to generate processing windows for this technique, relating to the several interdependent processing parameters on which the curing quality and efficiency strongly depend. Incorrect parameters can result in a track that is cured in some areas and uncured in others, or in damaged substrates. This paper addresses the strong need for a quantitative model which can systematically output the processing conditions for a given combination of ink, substrate and laser source; transforming the LADW technique from a purely empirical approach, to a simple, repeatable, mathematically sound, efficient and predictable process. The method comprises a novel and generic finite element model (FEM) that for the first time predicts the evolution of the thermal profile of the ink track during laser curing and thus generates a parametric map which indicates the most suitable combination of parameters for process optimization. Experimental data are compared with simulation results to verify the accuracy of the model.

  4. Integrative neural networks model for prediction of sediment rating curve parameters for ungauged basins

    Science.gov (United States)

    Atieh, M.; Mehltretter, S. L.; Gharabaghi, B.; Rudra, R.

    2015-12-01

    One of the most uncertain modeling tasks in hydrology is the prediction of ungauged stream sediment load and concentration statistics. This study presents integrated artificial neural networks (ANN) models for prediction of sediment rating curve parameters (rating curve coefficient α and rating curve exponent β) for ungauged basins. The ANN models integrate a comprehensive list of input parameters to improve the accuracy achieved; the input parameters used include: soil, land use, topographic, climatic, and hydrometric data sets. The ANN models were trained on the randomly selected 2/3 of the dataset of 94 gauged streams in Ontario, Canada and validated on the remaining 1/3. The developed models have high correlation coefficients of 0.92 and 0.86 for α and β, respectively. The ANN model for the rating coefficient α is directly proportional to rainfall erosivity factor, soil erodibility factor, and apportionment entropy disorder index, whereas it is inversely proportional to vegetation cover and mean annual snowfall. The ANN model for the rating exponent β is directly proportional to mean annual precipitation, the apportionment entropy disorder index, main channel slope, standard deviation of daily discharge, and inversely proportional to the fraction of basin area covered by wetlands and swamps. Sediment rating curves are essential tools for the calculation of sediment load, concentration-duration curve (CDC), and concentration-duration-frequency (CDF) analysis for more accurate assessment of water quality for ungauged basins.

  5. Correlation of alkaline phosphatase activity to clinical parameters of inflammation in smokers suffering from chronic periodontitis

    Directory of Open Access Journals (Sweden)

    Vishakha Grover

    2016-01-01

    Full Text Available Context: Current clinical periodontal diagnostic techniques emphasize the assessment of clinical and radiographic signs of periodontal diseases which can provide a measure of history of disease. Hence, new methodologies for early identification and determination of periodontal disease activity need to be explored which will eventually result in expedited treatment. Aim: To evaluate the correlation of alkaline phosphatase (ALP activity in gingival crevicular fluid (GCF to clinical parameters of periodontal inflammation in smokers with chronic periodontitis. Materials and Methods: Study population included 15 smoker male patients in the age group of 35–55 years suffering from moderate generalized chronic periodontitis with history of smoking present. Following parameters were evaluated at baseline, 1 month and 3 months after scaling and root planing: plaque index, bleeding index, probing pocket depth (PD, relative attachment level (RAL, and GCF ALP activity. Statistical Analysis Used: Independent variables for measurements over time were analyzed by using Wilcoxon signed rank test. Results: A statistically significant reduction in all the clinical parameters and GCF ALP activity was observed from baseline to 1 month and 3 months. A correlation was observed between change in GCF ALP activity and PD reduction as well as gain in RAL at 3 months. Conclusion: The present study emphasizes that total ALP activity could be used as a marker for periodontal disease activity in smokers. Estimation of changes in the levels of this enzyme has a potential to aid in the detection of progression of periodontal disease and monitoring the response to periodontal therapy.

  6. Correlation of alkaline phosphatase activity to clinical parameters of inflammation in smokers suffering from chronic periodontitis

    Science.gov (United States)

    Grover, Vishakha; Malhotra, Ranjan; Kapoor, Anoop; Bither, Rupika; Sachdeva, Sonia

    2016-01-01

    Context: Current clinical periodontal diagnostic techniques emphasize the assessment of clinical and radiographic signs of periodontal diseases which can provide a measure of history of disease. Hence, new methodologies for early identification and determination of periodontal disease activity need to be explored which will eventually result in expedited treatment. Aim: To evaluate the correlation of alkaline phosphatase (ALP) activity in gingival crevicular fluid (GCF) to clinical parameters of periodontal inflammation in smokers with chronic periodontitis. Materials and Methods: Study population included 15 smoker male patients in the age group of 35–55 years suffering from moderate generalized chronic periodontitis with history of smoking present. Following parameters were evaluated at baseline, 1 month and 3 months after scaling and root planing: plaque index, bleeding index, probing pocket depth (PD), relative attachment level (RAL), and GCF ALP activity. Statistical Analysis Used: Independent variables for measurements over time were analyzed by using Wilcoxon signed rank test. Results: A statistically significant reduction in all the clinical parameters and GCF ALP activity was observed from baseline to 1 month and 3 months. A correlation was observed between change in GCF ALP activity and PD reduction as well as gain in RAL at 3 months. Conclusion: The present study emphasizes that total ALP activity could be used as a marker for periodontal disease activity in smokers. Estimation of changes in the levels of this enzyme has a potential to aid in the detection of progression of periodontal disease and monitoring the response to periodontal therapy. PMID:27563197

  7. Integrating knowledge-driven and data-driven approaches in the derivation of clinical prediction rules

    OpenAIRE

    Kwiatkowska, Bogumila

    2006-01-01

    Clinical prediction rules play an important role in medical practice. They expedite diagnosis and treatment for the serious cases and limit unnecessary tests for low-probability cases. However, the creation process for prediction rules is costly, lengthy, and involves several steps: initial clinical trials, rule generation and refinement, validation, and evaluation in clinical settings. With the current development of efficient data mining algorithms and growing accessibility to a vast amount...

  8. Islet Oxygen Consumption Rate (OCR) Dose Predicts Insulin Independence in Clinical Islet Autotransplantation

    OpenAIRE

    Papas, Klearchos K.; Bellin, Melena D.; Sutherland, David E.R.; Suszynski, Thomas M.; Kitzmann, Jennifer P; Avgoustiniatos, Efstathios S.; Gruessner, Angelika C.; Mueller, Kathryn R; Beilman, Gregory J.; Balamurugan, Appakalai N.; Loganathan, Gopalakrishnan; Colton, Clark K.; Koulmanda, Maria; Weir, Gordon C; Wilhelm, Josh J.

    2015-01-01

    Background 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 confoundin...

  9. Islet Oxygen Consumption Rate (OCR) Dose Predicts Insulin Independence in Clinical Islet Autotransplantation

    OpenAIRE

    Papas, Klearchos K; Bellin, Melena D.; Sutherland, David E. R.; Suszynski, Thomas M.; Kitzmann, Jennifer P.; Avgoustiniatos, Efstathios S.; Gruessner, Angelika C.; Mueller, Kathryn R.; Beilman, Gregory J.; Balamurugan, Appakalai N.; Gopalakrishnan Loganathan; Colton, Clark K.; Maria Koulmanda; Weir, Gordon C.; Josh J Wilhelm

    2015-01-01

    Background: 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 ...

  10. 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. PMID:27174829

  11. Predicting Nitrate Concentration in Groundwater Resources using Lumped-Parameter Model: Case Study in Qazvin Plain

    Directory of Open Access Journals (Sweden)

    R.S Hajimirmohammad Ali

    2016-03-01

    Full Text Available Background and Objective: The concentration of nitrate, factors affecting the balance sheet, and the changes in an aquifer is of utmost importance. Because modeling is an efficient method to predict the concentration of ions in water resources, in this study using lumped-parameter model and Monte Carlo simulation model, the nitrate concentrations in groundwater resources of Qazvin Plain were estimated and analyzed. Materials and Methods: A total of 19 wells in different climates of saline watershed in Qazvin Plain were selected and entry and exit routes of nitrate to these sources were analyzed using lumped-parameter model.  Finally, Monte Carlo simulation was used to determine the probability of the estimated nitrate concentration in aquifer. Results: Application of lumped-parameter model for a part of a part of groundwater resources in Qazvin Plain watershed predicted the nitrate concentration in the range of 8.12 to 15.94 mg/l.   The maximum concentration was estimated in cold-dry climate with 12.8±0.04 mg/L. Moreover, it was found that the difference between the estimated nitrate concentration and factors affecting its concentration in different climates was significant (p<0.05. Conclusion: Despite the predicted concentrations of nitrate in the study area were in accordance with the Iran national standard for drinking purposes, the cumulative probability of Monte Carlo simulation showed that the possible violation of nitrate from the safe limit of 10 mg/l in the study area is 90% (p = 0.005.

  12. 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 of...... European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding of molecular mechanisms driving resistance to anti-angiogenesis agents, the current limitations of laboratory and clinical trial strategies and...... how the PREDICT consortium will endeavour to identify a new generation of predictive biomarkers....

  13. 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…

  14. 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.

  15. 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.

  16. An Effective Approach Based on Response Surface Methodology for Predicting Friction Welding Parameters

    Science.gov (United States)

    Celik, Sare; Deniz Karaoglan, Aslan; Ersozlu, Ismail

    2016-03-01

    The joining of dissimilar metals is one of the most essential necessities of industries. Manufacturing by the joint of alloy steel and normal carbon steel is used in production, because it decreases raw material cost. The friction welding process parameters such as friction pressure, friction time, upset pressure, upset time and rotating speed play the major roles in determining the strength and microstructure of the joints. In this study, response surface methodology (RSM), which is a well-known design of experiments approach, is used for modeling the mathematical relation between the responses (tensile strength and maximum temperature), and the friction welding parameters with minimum number of experiments. The results show that RSM is an effective method for this type of problems for developing models and prediction.

  17. Comparison of two non-linear prediction techniques for estimation of some intact rock parameters

    Science.gov (United States)

    Yagiz, Saffet; Sezer, Ebru; Gokceoglu, Candan

    2010-05-01

    Traditionally, some regression techniques have been used for prediction of some rock properties using their physical and index parameters. For this purpose, numerous models and empirical equations have been proposed in the literature to predict the uniaxial compressive strength (UCS) and the elasticity modules (E) of intact rocks. Two of the powerful modeling techniques for this purpose is that the non-linear multivariable regression (NLMR) and the artificial neural networks (ANN). The aim of the study is to develop some models to predict the UCS and E of rocks using predictive tools. Further, to investigate whether two-cycle or four-cycle slake durability index as an input parameter into the models demonstrates better characterization capacity for carbonate rocks, and also, to introduce two new performance ranking approaches via performance index and degree of consistency to select the best predictor among the developed models, complex and their rank cannot be solved by using a simple ranking approach introduced previously in the literature. To obtain these purposes, seven type of carbonate rocks was collected from quarries in the southwestern Turkey and their properties including the uniaxial compressive strength, the Schmidt hammer, effective porosity, dry unit weight, P-wave velocity, the modulus of elasticity, and both two and four-cycle of slake durability indices were determined for establishing a dataset used for construction of the models. As a result of this study, it is found that four-cycle slake durability index exhibits more characterization capacity for carbonate rock in the models in comparison with two-cycle slake durability index. Also, the ANN models having two outputs (UCS and E) exhibit more accurate estimation capacity than the NLMR models. In addition, newly introduced performance ranking index and degree of consistency may be accepted as useful indicators to be considered to obtain the performance ranking of complex models. Consequently

  18. Diffusion kurtosis imaging. Optimization of parameters for clinical use. President award proceedings

    International Nuclear Information System (INIS)

    Diffusion kurtosis imaging (DKI) is a new technique based on non-Gaussian water diffusion analysis. Because water diffusion in the brain is restricted (non-Gaussian), DKI provides more precise diffusional information derived from the tissue microstructure than does conventional diffusion analysis, such as diffusion tensor imaging (DTI, assuming Gaussian). However, our original protocol for DKI required 10 additional minutes for scanning, which seemed excessive for daily clinical use, so we examined b-value, number of motion-probing gradient (MPG) directions, and diffusion time to find more suitable parameters for DKI in the clinical setting. (author)

  19. A Novel Scale Up Model for Prediction of Pharmaceutical Film Coating Process Parameters.

    Science.gov (United States)

    Suzuki, Yasuhiro; Suzuki, Tatsuya; Minami, Hidemi; Terada, Katsuhide

    2016-01-01

    In the pharmaceutical tablet film coating process, we clarified that a difference in exhaust air relative humidity can be used to detect differences in process parameters values, the relative humidity of exhaust air was different under different atmospheric air humidity conditions even though all setting values of the manufacturing process parameters were the same, and the water content of tablets was correlated with the exhaust air relative humidity. Based on this experimental data, the exhaust air relative humidity index (EHI), which is an empirical equation that includes as functional parameters the pan coater type, heated air flow rate, spray rate of coating suspension, saturated water vapor pressure at heated air temperature, and partial water vapor pressure at atmospheric air pressure, was developed. The predictive values of exhaust relative humidity using EHI were in good correlation with the experimental data (correlation coefficient of 0.966) in all datasets. EHI was verified using the date of seven different drug products of different manufacturing scales. The EHI model will support formulation researchers by enabling them to set film coating process parameters when the batch size or pan coater type changes, and without the time and expense of further extensive testing. PMID:26936048

  20. Analysis of clinical and morphological parameters in patients with vulvar melanoma

    Directory of Open Access Journals (Sweden)

    E. V. Korzhevskaya

    2011-01-01

    Full Text Available The purpose of this study was to estimate the value of and to reveal the specific features of clinical and morphological parameters in pa- tients with vulvar melanoma. The study was based on the data obtained from the analysis of 40 vulvar melanoma patients treated at the N.N. Blokhin Russian Cancer Research Institute in the period January 1980 to December 2010.

  1. 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

  2. The Prediction of Academic and Clinical Performance in Medical School

    Science.gov (United States)

    Gough, Harrison G.; Hall, Wallace B.

    1975-01-01

    A study of medical student performance showed the clinical performance factor more or less unpredictable from aptitude and premedical academic achievement indices while the academic performance factor was forecast with acceptable accuracy by equations based on the Medical College Admissions Test and premedical grade point average. (JT)

  3. Prediction of Radionuclide transfer based on soil parameters: application to vulnerability studies

    International Nuclear Information System (INIS)

    The multi factorial character of the radiocaesium and radiostrontium soil-to-plan transfer, which depends on the radionuclide level in the soil solution amplified by a plant factor, prevents from establishing univariate relationships between transfer factors and soil and/or plant parameters. The plant factor is inversely proportional to the level of competitive species in the soil solution (Ca and Mg, for radiostrontium, and K and NH4 for radiocaesium). Radionuclide level in soil solution depends on the radionuclide available fraction and its distribution coefficient. For radiostrontium, this may be obtained from the Cationic Exchange Capacity (CEC), whereas for radiocaesium the Specific Interception Potential should be calculate, both corrected by the concentrations of the competitive species and selectivity coefficients. Therefore, the transfer factor eventually depends on soil solution composition, the available fraction and the number of sorption sites, as well as on the plant factor. For a given plant, a relative sequence of transfer can be set up based solely on soil parameters, since the plant factor is cancelled. This prediction model has been compared with transfer data from experiments with Mediterranean, mineral soils, contaminated with a thermo generated aerosol, and with podzolic and organic soils, contaminated by the Chernobyl fallout. These studies revealed that it was possible to predict a relative scale of transfer for any type of soil, also allowing a scale of soil vulnerability to radiostrontium and radiocaesium contamination to be set up. (Author)

  4. 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.

  5. Significance of Geological Parameters for Predicting Water Inflow in Hard Rock Tunnels

    Science.gov (United States)

    Holmøy, K. H.; Nilsen, B.

    2014-05-01

    One of the most challenging aspects of tunnelling is prognostication of water inflows. More reliable prediction of groundwater inflow may give considerable economical saving for future tunnel projects and may also prevent damage of environment and installations on the surface. This paper is discussing the significance of eight hypotheses regarding geological parameters for predicting water inflow in tunnels. The respective hypotheses have been tested as part of a recent research project in Norway. Six Norwegian tunnels with different geological conditions were selected for the research; the Romeriksporten, Frøya, T-baneringen, Lunner, Skaugum, and Storsand tunnels. Based on detailed study of these tunnels, the hypotheses are tested by comparing water inflow with geological parameters and factors such as Q value, faulting, rock stress orientation, rock cover, thickness of permeable soil or depth of lake/sea above the tunnel, rock type, and width of weakness zones. It is found that four out of the eight tested hypotheses are supported, two have low to medium support and two are not supported. One unexpected result is that for the tunnels covered by this study, the water inflow was found to increase with rock cover.

  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. Forecasting of Real Thunderstorms based on Electric Parameters Calculations in Numerical Weather Prediction Models

    Science.gov (United States)

    Dementyeva, Svetlana; Ilin, Nikolay; Shatalina, Maria; Mareev, Evgeny

    2016-04-01

    Now-casting and long-term forecasting of lightning flashes occurrence are urgent problems from different points of view. There are several approaches to predicting lightning activity using indirect non-electrical parameters based on the relationship of lightning flashes with vertical fluxes of solid-phased hydrometeors but for more explicit forecasting of the lightning flashes occurrence electric processes should be considered. In addition, a factor playing a key role for now-casting of lightning activity is the earliness. We have proposed an algorithm, which makes the process of thunderstorms prediction automatic (due to automatic start of the electric parameters calculation) and quick (due to the use of simplified methods). Our forecasting was based on the use of Weather Research and Forecasting (WRF) model, which does not include the electrification processes, but it was supplemented with two modules. The first is an algorithm, which allows us to select thunderstorm events indirectly. It is based on such characteristics of thunderclouds and thunderstorms as radar reflectivity, duration and area and provides us with information about an approximate beginning and duration of the thunderstorm. The second module is a method for electric parameters calculations, which we have proposed before. It was suggested that the non-inductive mechanism of charge generation and separation plays a key role in the thundercloud electrification processes. Also charge densities of solid-phased hydrometeors are assumed to be proportional to their mass in elementary air volume. According to the models by Saunders and Takahashi, particles change the sign of charge while getting into the lower part of thundercloud from the upper and vice versa. Electric neutrality in the vertical air column was supposed in the course of vertical charge separation due to collisions between falling graupels and carried upward ice crystals. Electric potential (and consequently electric field) can be found

  8. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks

    OpenAIRE

    Gevaert, Olivier; De Smet, Frank; Timmerman, Dirk; Moreau, Yves; De Moor, Bart

    2006-01-01

    MOTIVATION: Clinical data, such as patient history, laboratory analysis, ultrasound parameters--which are the basis of day-to-day clinical decision support--are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows t...

  9. Predicting Cereal Root Disease in Western Australia Using Soil DNA and Environmental Parameters.

    Science.gov (United States)

    Poole, Grant J; Harries, Martin; Hüberli, D; Miyan, S; MacLeod, W J; Lawes, Roger; McKay, A

    2015-08-01

    Root diseases have long been prevalent in Australian grain-growing regions, and most management decisions to reduce the risk of yield loss need to be implemented before the crop is sown. The levels of pathogens that cause the major root diseases can be measured using DNA-based services such as PreDicta B. Although these pathogens are often studied individually, in the field they often occur as mixed populations and their combined effect on crop production is likely to vary across diverse cropping environments. A 3-year survey was conducted covering most cropping regions in Western Australia, utilizing PreDicta B to determine soilborne pathogen levels and visual assessments to score root health and incidence of individual crop root diseases caused by the major root pathogens, including Rhizoctonia solani (anastomosis group [AG]-8), Gaeumannomyces graminis var. tritici (take-all), Fusarium pseudograminearum, and Pratylenchus spp. (root-lesion nematodes) on wheat roots for 115, 50, and 94 fields during 2010, 2011, and 2012, respectively. A predictive model was developed for root health utilizing autumn and summer rainfall and soil temperature parameters. The model showed that pathogen DNA explained 16, 5, and 2% of the variation in root health whereas environmental parameters explained 22, 11, and 1% of the variation in 2010, 2011, and 2012, respectively. Results showed that R. solani AG-8 soil pathogen DNA, environmental soil temperature, and rainfall parameters explained most of the variation in the root health. This research shows that interactions between environment and pathogen levels before seeding can be utilized in predictive models to improve assessment of risk from root diseases to assist growers to plan more profitable cropping programs. PMID:25822184

  10. 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 %.

  11. 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.

  12. Prediction and optimization of friction welding parameters for super duplex stainless steel (UNS S32760) joints

    International Nuclear Information System (INIS)

    Highlights: • Corrosion resistance and impact strength – predicted by response surface methodology. • Burn off length has highest significance on corrosion resistance. • Friction force is a strong determinant in changing impact strength. • Pareto front points generated by genetic algorithm aid to fix input control variable. • Pareto front will be a trade-off between corrosion resistance and impact strength. - Abstract: Friction welding finds widespread industrial use as a mass production process for joining materials. Friction welding process allows welding of several materials that are extremely difficult to fusion weld. Friction welding process parameters play a significant role in making good quality joints. To produce a good quality joint it is important to set up proper welding process parameters. This can be done by employing optimization techniques. This paper presents a multi objective optimization method for optimizing the process parameters during friction welding process. The proposed method combines the response surface methodology (RSM) with an intelligent optimization algorithm, i.e. genetic algorithm (GA). Corrosion resistance and impact strength of friction welded super duplex stainless steel (SDSS) (UNS S32760) joints were investigated considering three process parameters: friction force (F), upset force (U) and burn off length (B). Mathematical models were developed and the responses were adequately predicted. Direct and interaction effects of process parameters on responses were studied by plotting graphs. Burn off length has high significance on corrosion current followed by upset force and friction force. In the case of impact strength, friction force has high significance followed by upset force and burn off length. Multi objective optimization for maximizing the impact strength and minimizing the corrosion current (maximizing corrosion resistance) was carried out using GA with the RSM model. The optimization procedure resulted in

  13. 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.

  14. Genetic Algorithms for Estimating Effective Parameters in a Lumped Reactor Model for Reactivity Predictions

    International Nuclear Information System (INIS)

    The control system of a reactor should be able to predict, in real time, the amount of reactivity to be inserted (e.g., by control rod movements and boron injection and dilution) to respond to a given electrical load demand or to undesired, accidental transients. The real-time constraint renders impractical the use of a large, detailed dynamic reactor code. One has, then, to resort to simplified analytical models with lumped effective parameters suitably estimated from the reactor data.The simple and well-known Chernick model for describing the reactor power evolution in the presence of xenon is considered and the feasibility of using genetic algorithms for estimating the effective nuclear parameters involved and the initial nonmeasurable xenon and iodine conditions is investigated. This approach has the advantage of counterbalancing the inherent model simplicity with the periodic reestimation of the effective parameter values pertaining to each reactor on the basis of its recent history. By so doing, other effects, such as burnup, are automatically taken into account

  15. 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.

  16. Do clinical prediction models improve concordance of treatment decisions in reproductive medicine?

    NARCIS (Netherlands)

    J.W. van der Steeg; P. Steures; M.J.C. Eijkemans; J.D.F. Habbema; P.M.M. Bossuyt; P.G.A. Hompes; F. van der Veen; B.W.J. Mol

    2006-01-01

    Objective To assess whether the use of clinical prediction models improves concordance between gynaecologists with respect to treatment decisions in reproductive medicine. Design We constructed 16 vignettes of subfertile couples by varying fertility history, postcoital test, sperm motility, follicle

  17. 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.

  18. 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.

    . Multiple regression model of P. indica abundance on the parameters: temperature, salinity, dissolved oxygen and phosphate-phosphorus could explain more than 85% of the variation in the predicted abundance, while those of 8 species obtained from...

  19. 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...

  20. 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...

  1. Model Predictive Optimal Control of a Time-Delay Distributed-Parameter Systems

    Science.gov (United States)

    Nguyen, Nhan

    2006-01-01

    This paper presents an optimal control method for a class of distributed-parameter systems governed by first order, quasilinear hyperbolic partial differential equations that arise in many physical systems. Such systems are characterized by time delays since information is transported from one state to another by wave propagation. A general closed-loop hyperbolic transport model is controlled by a boundary control embedded in a periodic boundary condition. The boundary control is subject to a nonlinear differential equation constraint that models actuator dynamics of the system. The hyperbolic equation is thus coupled with the ordinary differential equation via the boundary condition. Optimality of this coupled system is investigated using variational principles to seek an adjoint formulation of the optimal control problem. The results are then applied to implement a model predictive control design for a wind tunnel to eliminate a transport delay effect that causes a poor Mach number regulation.

  2. 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.

  3. 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.

  4. 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.

  5. Predicting Relapse in Patients With Medulloblastoma by Integrating Evidence From Clinical and Genomic Features

    NARCIS (Netherlands)

    P. Tamayo; Y.J. Cho; A. Tsherniak; H. Greulich; L. Ambrogio; N. Schouten-van Meeteren; T. Zhou; A. Buxton; M. Kool; M. Meyerson; S.L. Pomeroy; J.P. Mesirov

    2011-01-01

    Purpose Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accurac

  6. Dose-volumetric parameters for predicting hypothyroidism after radiotherapy for head and neck cancer

    International Nuclear Information System (INIS)

    To investigate predictors affecting the development of hypothyroidism after radiotherapy for head and neck cancer, focusing on radiation dose-volumetric parameters, and to determine the appropriate radiation dose-volumetric threshold of radiation-induced hypothyroidism. A total of 114 patients with head and neck cancer whose radiotherapy fields included the thyroid gland were analysed. The purpose of the radiotherapy was either definitive (n=81) or post-operative (n=33). Thyroid function was monitored before starting radiotherapy and after completion of radiotherapy at 1 month, 6 months, 1 year and 2 years. A diagnosis of hypothyroidism was based on a thyroid stimulating hormone value greater than the maximum value of laboratory range, regardless of symptoms. In all patients, dose volumetric parameters were analysed. Median follow-up duration was 25 months (range; 6-38). Forty-six percent of the patients were diagnosed as hypothyroidism after a median time of 8 months (range; 1-24). There were no significant differences in the distribution of age, gender, surgery, radiotherapy technique and chemotherapy between the euthyroid group and the hypothyroid group. In univariate analysis, the mean dose and V35-V50 results were significantly associated with hypothyroidism. The V45 is the only variable that independently contributes to the prediction of hypothyroidism in multivariate analysis and V45 of 50% was a threshold value. If V45 was <50%, the cumulative incidence of hypothyroidism at 1 year was 22.8%, whereas the incidence was 56.1% if V45 was ≥50%. (P=0.034). The V45 may predict risk of developing hypothyroidism after radiotherapy for head and neck cancer, and a V45 of 50% can be a useful dose-volumetric threshold of radiation-induced hypothyroidism. (author)

  7. Prediction of ground motion parameters for the volcanic area of Mount Etna

    Science.gov (United States)

    Tusa, Giuseppina; Langer, Horst

    2016-01-01

    Ground motion prediction equations (GMPEs) have been derived for peak ground acceleration (PGA), velocity (PGV), and 5 % damped spectral acceleration (PSA) at frequencies between 0.1 and 10 Hz for the volcanic area of Mt. Etna. The dataset consists of 91 earthquakes with epicentral distances between 0.5 and 100 km. Given the specific characteristics of the area, we divided our data set into two groups: shallow events (SE, focal depth 5 km). The range of magnitude covered by the SE and the DE is 3.0 ≤ M L ≤ 4.3 and 3.0 ≤ M L ≤ 4.8, respectively. Signals of DE typically have more high frequencies than those of SE. These differences are clearly reflected in the empirical GMPEs of the two event groups. Empirical GMPEs were estimated considering several functional forms: Sabetta and Pugliese (Bull Seism Soc Am 77:1491-1513, 1987) (SP87), Ambraseys et al. (Earth Eng Struct Dyn 25:371-400, 1996) (AMB96), and Boore and Atkinson (Earth Spectra 24:99-138, 2008) (BA2008). From ANOVA, we learn that most of the errors in our GMPEs can be attributed to unmodeled site effects, whereas errors related to event parameters are limited. For DE, BA2008 outperforms the simpler models SP87 or AMB96. For SE, the simple SP87 is preferable considering the Bayesian Information Criterion since it proves more stable with respect to confidence and gives very similar or even lower prediction errors during cross-validation than the BA2008 model. We compared our results to relationships derived for Italy (ITA10, Bindi et al. Bull Earth Eng 99:2471-2488, 2011). For SE, the main differences are observed for distances greater than about 5 km for both horizontal and vertical PGAs. Conversely, for DE the ITA10 heavily overestimates the peak ground parameters for short distances.

  8. Validity of Sildenafil Test in Patients with Pulmonary Arterial Hypertension Associated with Congenital Heart Disease According to Clinical and Echocardiographic Parameters

    OpenAIRE

    Akbar Shahmohammadi; Paridokht Nakhostin Davari; Mohammad Yusof Aarabi Mogaddam; Akbar Molaei; Mahmood Meraji

    2009-01-01

    Background: Pulmonary arterial hypertension is a complication of most congenital heart diseases. We sought to assess the effect of sildenafil on patients suffering from pulmonary arterial hypertension in association with congenital heart disease on the basis of clinical and echocardiographic parameters and compare the catheterization and treatment results so as to evaluate the predictive value of sildenafil on the operability of patients. Methods: After primary echocardiography, 21 patients w...

  9. Predictive biomarkers for personalised anti-cancer drug use: discovery to clinical implementation.

    Science.gov (United States)

    Alymani, Nayef A; Smith, Murray D; Williams, David J; Petty, Russell D

    2010-03-01

    A priority translational research objective in cancer medicine is the discovery of novel therapeutic targets for solid tumours. Ideally, co-discovery of predictive biomarkers occurs in parallel to facilitate clinical development of agents and ultimately personalise clinical use. However, the identification of clinically useful predictive biomarkers for solid tumours has proven challenging with many initially promising biomarkers failing to translate into clinically useful applications. In particular, the 'failure' of a predictive biomarker has often only become apparent at a relatively late stage in investigation. Recently, the field has recognised the need to develop a robust clinical biomarker development methodology to facilitate the process. This review discusses the recent progress in this area focusing on the key stages in the biomarker development process: discovery, validation, qualification and implementation. Concentrating on predictive biomarkers for selecting systemic therapies for individual patients in the clinic, the advances and progress in each of these stages in biomarker development are outlined and the key remaining challenges are discussed. Specific examples are discussed to illustrate the challenges identified and how they have been addressed. Overall, we find that significant progress has been made towards a formalised biomarker developmental process. This holds considerable promise for facilitating the translation of predictive biomarkers from discovery to clinical implementation. Further enhancements could eventually be found through alignment with regulatory processes. PMID:20138504

  10. 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.

  11. Prognostic and Predictive Biomarkers in Colorectal Cancer. From the Preclinical Setting to Clinical Practice.

    Science.gov (United States)

    Maurel, Joan; Postigo, Antonio

    2015-01-01

    Colorectal cancer (CRC) is the second largest cause of cancer mortality in Western countries, mostly due to metastasis. Understanding the natural history and prognostic factors in patients with metastatic CRC (mCRC) is essential for the optimal design of clinical trials. The main prognostic factors currently used in clinical practice are related to tumor behavior (e.g., white blood counts, levels of lactate dehydrogenase, levels of alkaline phosphatase) disease extension (e.g., presence of extrahepatic spread, number of organs affected) and general functional status (e.g., performance status as defined by the Eastern Cooperative Oncology Group). However, these parameters are not always sufficient to establish appropriate therapeutic strategies. First-line therapy in mCRC combines conventional chemotherapy (CHT) (e.g., FOLFOX, FOLFIRI, CAPOX) with a number of agents targeted to specific signaling pathways (TA) (e.g., panitumumab and cetuximab for cases KRAS/NRAS WT, and bevacizumab). Although the response rate to this combination regime exceeds 50%, progression of the disease is almost universal and only less than 10% of patients are free of disease at 2 years. Current clinical trials with second and third line therapy include new TA, such as tyrosin-kinase receptors inhibitors (MET, HER2, IGF-1R), inhibitors of BRAF, MEK, PI3K, AKT, mTORC, NOTCH and JAK1/JAK2, immunotherapy modulators and check point inhibitors (anti-PD-L1 and anti- PD1). Despite the identification of multiple prognostic and predictive biomarkers and signatures, it is still unclear how expression of many of these biomarkers is modulated by CHT and/or TA, thus potentially affecting response to treatment. In this review we analyzed how certain biomarkers in tumor cells and microenvironment influence the response to new TA and immune-therapies strategies in mCRC pre-treated patients. PMID:26452385

  12. Precision and Negative Predictive Value of Links between ClinicalTrials.gov and PubMed

    OpenAIRE

    Huser, Vojtech; Cimino, James J.

    2012-01-01

    One of the goals of translational science is to shorten the time from discovery to clinical use. Clinical trial registries were established to increase transparency in completed and ongoing clinical trials, and they support linking trials with resulting publications. We set out to investigate precision and negative predictive value (NPV) of links between ClinicalTrials.gov (CT.gov) and PubMed. CT.gov has been established to increase transparency in clinical trials and the link to PubMed is cr...

  13. Some haematological and clinical-chemical parameters of sight hounds (Afghan hound, saluki and whippet).

    Science.gov (United States)

    Hilppö, M

    1986-01-01

    Blood was collected from 111 clinically healthy dogs of sight hound breeds for haematological and clinical chemical analyses. The distributions according to age, breed and sex are tabulated. Reference values for haemoglobin were established as 172-240 g/l for adults and 136-217 g/l for growing dogs; the respective packed cell volume values were 50-69% and 41-64%. Ten serum parameters were determined: AP, ALAT, ASAT, GT, CK, urea, cholesterol, creatinine, total protein and albumin. The results are given as means +/- S.D., and a reference value for serum creatinine was established as less than or equal to 165 mumol/l for adults. PMID:3675721

  14. Sixty-Six Years of Research on the Clinical Versus Actuarial Prediction of Violence

    Science.gov (United States)

    Hilton, N. Zoe; Harris, Grant T.; Rice, Marnie E.

    2006-01-01

    In their meta-analysis of clinical versus statistical prediction models, Aegisdottir et al. (this issue) extended previous findings of statistical-method superiority across such variables as clinicians' experience and familiarity with data. In this reaction, the authors are particularly interested in violence prediction, which yields the greatest…

  15. Human intoxication with paralytic shellfish toxins: clinical parameters and toxin analysis in plasma and urine.

    Science.gov (United States)

    García, Carlos; Lagos, Marcelo; Truan, Dominique; Lattes, Karinna; Véjar, Omar; Chamorro, Beatriz; Iglesias, Verónica; Andrinolo, Darío; Lagos, Néstor

    2005-01-01

    This study reports the data recorded from four patients intoxicated with shellfish during the summer 2002, after consuming ribbed mussels (Aulacomya ater) with paralytic shellfish toxin contents of 8,066 +/- 61.37 microg/100 gr of tissue. Data associated with clinical variables and paralytic shellfish toxins analysis in plasma and urine of the intoxicated patients are shown. For this purpose, the evolution of respiratory frequency, arterial blood pressure and heart rate of the poisoned patients were followed and recorded. The clinical treatment to reach a clinically stable condition and return to normal physiological parameters was a combination of hydration with saline solution supplemented with Dobutamine (vasoactive drug), Furosemide (diuretic) and Ranitidine (inhibitor of acid secretion). The physiological condition of patients began to improve after four hours of clinical treatment, and a stable condition was reached between 12 to 24 hours. The HPLC-FLD analysis showed only the GTX3/GTX2 epimers in the blood and urine samples. Also, these epimers were the only paralytic shellfish toxins found in the shellfish extract sample. PMID:16238098

  16. The importance of the preoperative clinical parameters and the intraoperative electrophysiological monitoring in brachial plexus surgery

    Directory of Open Access Journals (Sweden)

    Leandro Pretto Flores

    2011-08-01

    Full Text Available OBJECTIVE: The study aims to demonstrate the impact of some preoperative clinical parameters on the functional outcome of patients sustaining brachial plexus injuries, and to trace some commentaries about the use of intraoperative monitoring techniques. METHOD: A retrospective study one hundred cases of brachial plexus surgery. The analysis regarding postoperative outcomes was performed by comparing the average of the final result of the surgery for each studied cohort. RESULTS: Direct electrical stimulation was used in all patients, EMG in 59%, SEPs in 37% and evoked NAPs in 19% of the cases. Patients in whom the motor function of the hand was totally or partially preserved before surgery, and those in whom surgery was delayed less than 6 months demonstrated significant (p<0.05 better outcomes. CONCLUSION: The preoperative parameters associated to favorable outcomes in reconstruction of the brachial plexus are a good post-traumatic status of the hand and a short interval between injury and surgery.

  17. 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.

  18. Evaluation of clinical parameters and lesions in pig organs during post-weaning period

    OpenAIRE

    Mejía Medina, Julián; Rincón Ruiz, Juan; Gutiérrez Vergara, Cristian; Correa Londoño, Guillermo; López Herrera, Albeiro; Parra Suescún, Jaime

    2012-01-01

    To evaluate the effect of early weaning on clinical parameters, development and occurrence of lesions in organs of systemic importance, and weight gain in pigs evaluations were carried out. The experiment was conducted in the San Pablo Production Research Center of the Universidad Nacional de Colombia (Medellín). We used 16 weaned pigs at 21 days of age. The animals were fed for 10 days with a basal diet (milk). Four pigs were slaughtered on days 1, 5, 7 and 10 post-weaning and samples of int...

  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. Use of clinical movement screening tests to predict injury in sport

    OpenAIRE

    Chimera, Nicole J; Warren, Meghan

    2016-01-01

    Clinical movement screening tests are gaining popularity as a means to determine injury risk and to implement training programs to prevent sport injury. While these screens are being used readily in the clinical field, it is only recently that some of these have started to gain attention from a research perspective. This limits applicability and poses questions to the validity, and in some cases the reliability, of the clinical movement tests as they relate to injury prediction, intervention,...

  1. Prediction of the date, magnitude and affected area of impending strong earthquakes using integration of multi precursors earthquake parameters

    OpenAIRE

    M. R. Saradjian; Akhoondzadeh, M

    2011-01-01

    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 (Ir...

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

    Science.gov (United States)

    Swanton, Charles; Larkin, James M; Gerlinger, Marco; Eklund, Aron C; Howell, Michael; Stamp, Gordon; Downward, Julian; Gore, Martin; Futreal, P Andrew; Escudier, Bernard; Andre, Fabrice; Albiges, Laurence; Beuselinck, Benoit; Oudard, Stephane; Hoffmann, Jens; Gyorffy, Balázs; Torrance, Chris J; Boehme, Karen A; Volkmer, Hansjuergen; Toschi, Luisella; Nicke, Barbara; Beck, Marlene; Szallasi, Zoltan

    2010-01-01

    The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway inhibitor. Through the analysis of tumor tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumor-derived genomic data with personalized tumor-derived small hairpin RNA and high-throughput small interfering 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 of chance associations that lead to overestimation of true clinical accuracy. These methods will identify molecular pathways important for survival and growth of RCC cells and particular targets suitable for therapeutic development. Importantly, our results may enable individualized treatment of RCC, 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 a European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding

  3. Spatial Prediction of Soil Classes by Using Soil Weathering Parameters Derived from vis-NIR Spectroscopy

    Science.gov (United States)

    Ramirez-Lopez, Leonardo; Alexandre Dematte, Jose

    2010-05-01

    There is consensus in the scientific community about the great need of spatial soil information. Conventional mapping methods are time consuming and involve high costs. Digital soil mapping has emerged as an area in which the soil mapping is optimized by the application of mathematical and statistical approaches, as well as the application of expert knowledge in pedology. In this sense, the objective of the study was to develop a methodology for the spatial prediction of soil classes by using soil spectroscopy methodologies related with fieldwork, spectral data from satellite image and terrain attributes in simultaneous. The studied area is located in São Paulo State, and comprised an area of 473 ha, which was covered by a regular grid (100 x 100 m). In each grid node was collected soil samples at two depths (layers A and B). There were extracted 206 samples from transect sections and submitted to soil analysis (clay, Al2O3, Fe2O3, SiO2 TiO2, and weathering index). The first analog soil class map (ASC-N) contains only soil information regarding from orders to subgroups of the USDA Soil Taxonomy System. The second (ASC-H) map contains some additional information related to some soil attributes like color, ferric levels and base sum. For the elaboration of the digital soil maps the data was divided into three groups: i) Predicted soil attributes of the layer B (related to the soil weathering) which were obtained by using a local soil spectral library; ii) Spectral bands data extracted from a Landsat image; and iii) Terrain parameters. This information was summarized by a principal component analysis (PCA) in each group. Digital soil maps were generated by supervised classification using a maximum likelihood method. The trainee information for this classification was extracted from five toposequences based on the analog soil class maps. The spectral models of weathering soil attributes shown a high predictive performance with low error (R2 0.71 to 0.90). The spatial

  4. Prognosis after Acute Myocardial Infarction as Predicted by C-reactive Protein and Clinical Variables

    Directory of Open Access Journals (Sweden)

    Angelo Modica MD, PhD

    2013-02-01

    Full Text Available Background:Raised concentrations of C-reactive protein (CRP have been reported to be strongly related to an adverse long term prognosis in patients with acute myocardial infarction (AMI. However, adjustments for clinical variables as well as interaction between variables have been incomplete. The aims of this study were to examine the predictive value of baseline concentrations of CRP for mortality after adjustment for important clinical variables and to compare the clinical usefulness of CRP with easily available clinical variables in the prediction of long term survival.Methods:Five hundred and thirty-one patients with AMI were included. A blood sample for CRP was obtained on admission. All patients were followed for a minimum of two years and death of any cause was recorded as the study end point.Results:In logistic regression analysis, the interaction term Age by Killip class > 1, the variable glomerular filtration rate as well as the interaction term Age by Atrial fibrillation were retained. The resulting model correctly predicted death or not in 81% of the patients. CRP did not contribute to the final model.Conclusions:CRP does not independently predict long-term mortality after an AMI after adjustments for clinical variables and interaction. CRP has no value beyond clinical variables in predicting death after AMI.

  5. The generation of experimental designs for uncertainty and sensitivity analysis of model predictions with emphasis on dependences between uncertain parameters

    International Nuclear Information System (INIS)

    One of the major steps of a probabilistic uncertainty and sensitivity analysis of model predictions is the generation of an experimental design, i.e. the selection of a multivariate sample of parameter values suitable to study the influence of parameter uncertainties on model predictions. In order to support the analyst in performing this task a computer program, named MEDUSA, has been written to generate the desired design after having received the necessary input data from the experts who are familiar with the various uncertain parameters. The paper describes the main features of this program. (author)

  6. Clinical significance of sleep bruxism on several occlusal and functional parameters.

    Science.gov (United States)

    Ommerborn, Michelle A; Giraki, Maria; Schneider, Christine; Fuck, Lars Michael; Zimmer, Stefan; Franz, Matthias; Raab, Wolfgang Hans-michael; Schaefer, Ralf

    2010-10-01

    The aim of this study was to evaluate the association between various functional and occlusal parameters and sleep bruxism. Thirty-nine (39) sleep bruxism patients and 30 controls participated in this investigation. The assessment of sleep bruxism was performed using the Bruxcore Bruxism-Monitoring Device (BBMD) combined with a new computer-based analyzing method. Sixteen functional and/or occlusal parameters were recorded. With a mean slide of 0.95 mm in the sleep bruxism group and a mean slide of 0.42 mm in the control group (Mann Whitney U test; p<0.003), results solely demonstrated a significant group difference regarding the length of a slide from centric occlusion to maximum intercuspation. The results suggest that the slightly pronounced slide could be of clinical importance in the development of increased wear facets in patients with current sleep bruxism activity. Following further evaluation including polysomnographic recordings, the BBMD combined with this new analyzing technique seems to be a clinically feasible instrument that allows the practitioner to quantify abrasion over a short period. PMID:21032978

  7. 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

  8. 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

  9. Genomic Copy Number Variations in the Genomes of Leukocytes Predict Prostate Cancer Clinical Outcomes.

    Directory of Open Access Journals (Sweden)

    Yan P Yu

    Full Text Available Accurate prediction of prostate cancer clinical courses remains elusive. In this study, we performed whole genome copy number analysis on leukocytes of 273 prostate cancer patients using Affymetrix SNP6.0 chip. Copy number variations (CNV were found across all chromosomes of the human genome. An average of 152 CNV fragments per genome was identified in the leukocytes from prostate cancer patients. The size distributions of CNV in the genome of leukocytes were highly correlative with prostate cancer aggressiveness. A prostate cancer outcome prediction model was developed based on large size ratio of CNV from the leukocyte genomes. This prediction model generated an average prediction rate of 75.2%, with sensitivity of 77.3% and specificity of 69.0% for prostate cancer recurrence. When combined with Nomogram and the status of fusion transcripts, the average prediction rate was improved to 82.5% with sensitivity of 84.8% and specificity of 78.2%. In addition, the leukocyte prediction model was 62.6% accurate in predicting short prostate specific antigen doubling time. When combined with Gleason's grade, Nomogram and the status of fusion transcripts, the prediction model generated a correct prediction rate of 77.5% with 73.7% sensitivity and 80.1% specificity. To our knowledge, this is the first study showing that CNVs in leukocyte genomes are predictive of clinical outcomes of a human malignancy.

  10. 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.

  11. 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.

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

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    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.

  13. Prediction of the thermal dynamic parameters fluctuation of coolant system of the IBR-2M reactor using neural networks

    International Nuclear Information System (INIS)

    This paper presents an artificial neural network method for long-term prediction of the thermal dynamic parameters of primary coolant circuit of the IBR-2M reactor. The main goal is to predict the temperature and liquid sodium flow rate through the core and thermal power. It is shown that the prediction can reduce three times the effects of slow reactivity fluctuations in power and decrease the requirements for the automatic power stabilization system. Nonlinear autoregressive neural network (NAR) with local feedback connection has been considered. The results of prediction error ~ 5% coincide with the experimental ones.

  14. Late gadolinium enhancement cardiovascular magnetic resonance predicts clinical worsening in patients with pulmonary hypertension

    Directory of Open Access Journals (Sweden)

    Freed Benjamin H

    2012-02-01

    Full Text Available Abstract Background Late gadolinium enhancement (LGE occurs at the right ventricular (RV insertion point (RVIP in patients with pulmonary hypertension (PH and has been shown to correlate with cardiovascular magnetic resonance (CMR derived RV indices. However, the prognostic role of RVIP-LGE and other CMR-derived parameters of RV function are not well established. Our aim was to evaluate the predictive value of contrast-enhanced CMR in patients with PH. Methods RV size, ejection fraction (RVEF, and the presence of RVIP-LGE were determined in 58 patients with PH referred for CMR. All patients underwent right heart catheterization, exercise testing, and N-terminal pro-brain natriuretic peptide (NT-proBNP evaluation; results of which were included in the final analysis if performed within 4 months of the CMR study. Patients were followed for the primary endpoint of time to clinical worsening (death, decompensated right ventricular heart failure, initiation of prostacyclin, or lung transplantation. Results Overall, 40/58 (69% of patients had RVIP-LGE. Patients with RVIP- LGE had larger right ventricular volume index, lower RVEF, and higher mean pulmonary artery pressure (mPAP, all p Conclusions The presence of RVIP-LGE in patients with PH is a marker for more advanced disease and poor prognosis. In addition, this study reveals for the first time that CMR-derived RVEF is an independent non-invasive imaging predictor of adverse outcomes in this patient population.

  15. Thrombin generation assay: a new tool to predict and optimize clinical outcome in cardiovascular patients?

    Science.gov (United States)

    Campo, Gianluca; Pavasini, Rita; Pollina, Alberto; Fileti, Luca; Marchesini, Jlenia; Tebaldi, Matteo; Ferrari, Roberto

    2012-12-01

    Antithrombotic therapy (including antiplatelet and anticoagulant drugs) is the cornerstone of the current medical treatment of patients with acute coronary syndromes (ACS). This therapy and particularly the new antiplatelet and anticoagulant drugs have significantly reduced the ischemic risk, but have increased bleeding complications. Recently, several studies have emphasized the negative prognostic impact on long-term mortality of these bleeding adverse events. Thus, new assays to estimate the bleeding risk and the efficacy of these antithrombotic drugs are clearly in demand. Regarding the anticoagulant drugs, new promising data have emerged about the thrombin generation assay (TGA). TGA measures the ability of plasma to generate thrombin. TGA may be used to check coagulation function, to value risk of thrombosis and to compare the efficacy of different anticoagulants employed in clinical management of patients with ACS. The TGA result is a curve which describes the variation of thrombin's amount during the activation of the coagulation cascade. All available anticoagulant drugs influence the principal parameters generated by TGA and so it is possible to evaluate the effects of the medical treatment. In this review we provide a brief description of the assay and we summarize the principals of previous studies by analyzing the relationship between anticoagulant drugs and TGA. Moreover, a brief summary of its ability to predict ischemic and bleeding risks has been provided. PMID:22688556

  16. Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters.

    Directory of Open Access Journals (Sweden)

    Radina P Soebiyanto

    Full Text Available BACKGROUND: Influenza transmission is often associated with climatic factors. As the epidemic pattern varies geographically, the roles of climatic factors may not be unique. Previous in vivo studies revealed the direct effect of winter-like humidity on air-borne influenza transmission that dominates in regions with temperate climate, while influenza in the tropics is more effectively transmitted through direct contact. METHODOLOGY/PRINCIPAL FINDINGS: Using time series model, we analyzed the role of climatic factors on the epidemiology of influenza transmission in two regions characterized by warm climate: Hong Kong (China and Maricopa County (Arizona, USA. These two regions have comparable temperature but distinctly different rainfall. Specifically we employed Autoregressive Integrated Moving Average (ARIMA model along with climatic parameters as measured from ground stations and NASA satellites. Our studies showed that including the climatic variables as input series result in models with better performance than the univariate model where the influenza cases depend only on its past values and error signal. The best model for Hong Kong influenza was obtained when Land Surface Temperature (LST, rainfall and relative humidity were included as input series. Meanwhile for Maricopa County we found that including either maximum atmospheric pressure or mean air temperature gave the most improvement in the model performances. CONCLUSIONS/SIGNIFICANCE: Our results showed that including the environmental variables generally increases the prediction capability. Therefore, for countries without advanced influenza surveillance systems, environmental variables can be used for estimating influenza transmission at present and in the near future.

  17. Parameters That May Be Used for Predicting Failure during Endoscopic Retrograde Cholangiopancreatography

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    Emre Balik

    2013-01-01

    Full Text Available Aim. Endoscopic retrograde cholangiopancreatography (ERCP is frequently used for the diagnosis and treatment of hepatic, biliary tract, and pancreatic disorders. However, failure during cannulation necessitates other interventions. The aim of this study was to establish parameters that can be used to predict failure during ERCP. Methods. A total of 5884 ERCP procedures performed on 5079 patients, between 1991 and 2006, were retrospectively evaluated. Results. Cannulation was possible in 4482 (88.2% patients. For each one-year increase in age, the cannulation failure rate increased by 1.01-fold (. A history of previous hepatic biliary tract surgery caused the cannulation failure rate to decrease by 0.487-fold (. A tumor infiltrating the ampulla, the presence of pathology obstructing the gastrointestinal passage, and peptic ulcer increased the failure rate by 78-, 28-, and 3.47-fold, respectively (. Conclusions.Patient gender and duodenal diverticula do not influence the success of cannulation during ERCP. Billroth II and Roux-en-Y gastrojejunostomy surgeries, a benign or malignant obstruction of the gastrointestinal system, and duodenal ulcers decrease the cannulation success rate, whereas a history of previous hepatic biliary tract surgery increases it. Although all endoscopists had equal levels of experience, statistically significant differences were detected among them.

  18. 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

  19. 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. PMID:27115488

  20. Early radiation-induced mucosal changes evaluated by proctoscopy: Predictive role of dosimetric parameters

    International Nuclear Information System (INIS)

    Background and purpose: Late rectal complications are assessed according to different scoring systems. Endoscopy can provide a more sensitive estimation of early radiation damage. The aim of this paper is to investigate the correlation between dosimetric parameters and rectal mucosal changes after radiotherapy (RT). Materials and methods: Patients with prostate adenocarcinoma treated with curative or adjuvant RT underwent endoscopy 1 year after RT. Receiver operating characteristics (ROC) analysis was performed to analyze the predictive capability of the dosimetric variables in determining mucosal changes classified by Vienna Rectoscopy Score (VRS). Results: The best dosimetric predictors of grade ⩾2 telangiectasia were rectal (r) V60Gy (p = 0.014), rV70Gy (p = 0.017) and rDmean (p = 0.018). Similar results were obtained for grade ⩾2 VRS. The set of rV60Gy 70Gy mean 60Gy, rV70Gy and rDmean were the strongest predictors of rectal mucosal alterations. In-depth analysis is required to correlate each mucosal alteration with late rectal toxicity in order to suggest early proctoscopy as surrogate end-point for rectal late toxicity in studies aimed at reducing this important complication.

  1. Correlation between olfactory dysfunction and various clinical parameters in patients with multiple sclerosis

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    Kostić Jelena

    2009-01-01

    Full Text Available Background/Aim. Multiple sclerosis (MS is a chronic inflammatory disease of the central nervous system (CNS characterized by myelin destruction and axon loss. Among various clinical manifestations of MS cognitive disorders are frequent. Olfactory disorders are also noticed but they are rarely considered in clinical practice. The aim of the present study was to examine frequency of olfactory dysfunction in patients with MS and its relationship to clinical parameters. Methods. Our study comprised 61 consecutive patients with definite MS who were hospitalized at the Department for Multiple Sclerosis and Other Immune- Mediated Disorders of CNS, Institute of Neurology, Clinical Center of Serbia, Belgrade, and 45 gender-, age- and education-matched healthy voluntaries. The Pocket Smell Test (PST was used for examination of olfactory function. Cognitive functions were analyzed using the tests from the Brief Battery of Neuropsychological Tests: Paced Auditory Serial Addition Test 3-minute Version (PASAT 3', Word List Generation (WLG and Symbol Digit Modalities Test (SDMT. Results. Olfactory dysfunction was found in 26 (43% MS patients and 5 (11% controls (p = 0.001. Statistically significant positive correlation was found only between PST score and WLG scores (r = 0.297, p = 0.030. In comparison with the previously published normative values, our subjects with MS had decrease in the mean indices of the PASAT 3' in 28%, SDMT in 51% and WLG in 90% of the subjects. Conclusion. Olfactory dysfunction is frequent in our population of patients with MS. This disturbance correlates with the impairment of cognitive functions in these patients.

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Jin-You Wang

    2014-05-01

    Full Text Available 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 clinically 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 all 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.

  4. Use of clinical movement screening tests to predict injury in sport.

    Science.gov (United States)

    Chimera, Nicole J; Warren, Meghan

    2016-04-18

    Clinical movement screening tests are gaining popularity as a means to determine injury risk and to implement training programs to prevent sport injury. While these screens are being used readily in the clinical field, it is only recently that some of these have started to gain attention from a research perspective. This limits applicability and poses questions to the validity, and in some cases the reliability, of the clinical movement tests as they relate to injury prediction, intervention, and prevention. This editorial will review the following clinical movement screening tests: Functional Movement Screen™, Star Excursion Balance Test, Y Balance Test, Drop Jump Screening Test, Landing Error Scoring System, and the Tuck Jump Analysis in regards to test administration, reliability, validity, factors that affect test performance, intervention programs, and usefulness for injury prediction. It is important to review the aforementioned factors for each of these clinical screening tests as this may help clinicians interpret the current body of literature. While each of these screening tests were developed by clinicians based on what appears to be clinical practice, this paper brings to light that this is a need for collaboration between clinicians and researchers to ensure validity of clinically meaningful tests so that they are used appropriately in future clinical practice. Further, this editorial may help to identify where the research is lacking and, thus, drive future research questions in regards to applicability and appropriateness of clinical movement screening tools. PMID:27114928

  5. Testing life history predictions in a long-lived seabird: a population matrix approach with improved parameter estimation

    Science.gov (United States)

    Doherty, P.F., Jr.; Schreiber, E.A.; Nichols, J.D.; Hines, J.E.; Link, W.A.; Schenk, G.A.; Schreiber, R.W.

    2004-01-01

    Life history theory and associated empirical generalizations predict that population growth rate (lambda) in long-lived animals should be most sensitive to adult survival; the rates to which lambda is most sensitive should be those with the smallest temporal variances; and stochastic environmental events should most affect the rates to which lambda is least sensitive. To date, most analyses attempting to examine these predictions have been inadequate, their validity being called into question by problems in estimating parameters, problems in estimating the variability of parameters, and problems in measuring population sensitivities to parameters. We use improved methodologies in these three areas and test these life-history predictions in a population of red-tailed tropicbirds (Phaethon rubricauda). We support our first prediction that lambda is most sensitive to survival rates. However the support for the second prediction that these rates have the smallest temporal variance was equivocal. Previous support for the second prediction may be an artifact of a high survival estimate near the upper boundary of 1 and not a result of natural selection canalizing variances alone. We did not support our third prediction that effects of environmental stochasticity (El Ni?o) would most likely be detected in vital rates to which lambda was least sensitive and which are thought to have high temporal variances. Comparative data-sets on other seabirds, within and among orders, and in other locations, are needed to understand these environmental effects.

  6. 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

  7. 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.

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

    International Nuclear Information System (INIS)

    Purpose: To investigate the plasma dynamics of 5 proinflammatory/fibrogenic cytokines, including interleukin-1beta (IL-1β), IL-6, IL-8, tumor necrosis factor alpha (TNF-α), and transforming growth factor beta1 (TGF-β1) 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.

  9. 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

    Abstract Objective. The aim of the present study was to compare the ability of four clinical prediction rules to predict adverse outcome in perforated peptic ulcer (PPU): the Boey score, the American Society of Anesthesiologists (ASA) score, the Acute Physiology and Chronic Health Evaluation...... 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...... patients had at least one co-existing disease. The 30-day mortality proportion was 17% (20/117). The AUCs: the Boey score, 0.63; the sepsis score, 0.69; the ASA score, 0.73; and the APACHE II score, 0.76. Overall, the PPVs of all four prediction rules were low and the NPVs high. Conclusions. The Boey score...

  10. 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

  11. Atherothrombotic stroke: clinical data and parameters of platelet haemostasis in patients in acute stage

    Directory of Open Access Journals (Sweden)

    Laskovets A.B.

    2012-12-01

    Full Text Available

     

    The aim of the study was the investigation of clinical data and platelet haemostasis parameters in patients with atherothrombotic stroke for secondary prophylaxis improvement. Materials and Methods. 41 patient: 26 (63,4% males, 15 (36,6% females, mean age 66,0±9,4 years and 18 healthy person were examined. Neurological examination was performed, patient condition was estimated with National Institute of Health Stroke Scale and Rankin scale. The set of investigation included clinical blood analysis, adenosinediphosphate-induced agregometry, flow-cytometry, molecular genetic analysis of gene Iba. Results. There was platelet activation in patients with atherothrombotic stroke according to flow-cytometry data. The conventional optic agregometry was not helpful for revealing of platelet activation. The expression of 1ba receptors correlated with the point of National Institute of Health Stroke Scale at discharge. The incidence of Iba gene mutation was higher in patients with atherothrombotic stroke comparing with control group. Conclusions. The revealed data predispose to the possibility of individual administration of antiagregant therapy in such patients.

  12. 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.

  13. Relationships between organohalogen contaminants and blood plasma clinical-chemical parameters in chicks of three raptor species from Northern Norway

    DEFF Research Database (Denmark)

    Sonne, Christian; Bustnes, Jan Ove; Herzke, Dorte; Jaspers, Veerle L.B.; Covaci, Adrian; Halley, Duncan J.; Moum, Truls; Eulaers, Igor; Eens, Marcel; Ims, Rolf A.; Hanssen, Sveinn A.; Erikstad, Kjell Einar; Johnsen, Trond; Schnug, Lisbeth; Riget, Frank Farsø; Jensen, Asger Lundorff

    2010-01-01

    Organohalogen contaminants (OHCs) may affect various physiological parameters in birds including blood chemistry. We therefore examined blood plasma clinical-chemical parameters and OHCs in golden eagle, white-tailed eagle and goshawk chicks from Northern Norway. Correlation analyses on pooled da...

  14. Early seizures in patients with acute stroke: Frequency, predictive factors, and effect on clinical outcome

    OpenAIRE

    Andrea Alberti; Maurizio Paciaroni; Valeria Caso; Michele Venti; Francesco Palmerini; Giancarlo Agnelli

    2008-01-01

    Andrea Alberti, Maurizio Paciaroni, Valeria Caso, Michele Venti, Francesco Palmerini, Giancarlo AgnelliStroke Unit and Division of Internal and Cardiovascular Medicine, University of Perugia, Perugia, ItalyBackground: Early seizure (ES) may complicate the clinical course of patients with acute stroke. The aim of this study was to assess the rate of and the predictive factors for ES as well the effects of ES on the clinical outcome at hospital discharge in patients with first-ever stroke.Patie...

  15. Pulmonary embolism in intensive care unit: Predictive factors, clinical manifestations and outcome

    OpenAIRE

    Bahloul Mabrouk; Chaari Anis; Kallel Hatem; Abid Leila; Hamida Chokri Ben; Dammak Hassen; Rekik Noureddine; Mnif Jameleddine; Chelly Hedi; Bouaziz Mounir

    2010-01-01

    Objective : To determine predictive factors, clinical and demographics characteristics of patients with pulmonary embolism (PE) in ICU, and to identify factors associated with poor outcome in the hospital and in the ICU. Methods : During a four-year prospective study, a medical committee of six ICU physicians prospectively examined all available data for each patient in order to classify patients according to the level of clinical suspicion of pulmonary thromboembolism. During the study...

  16. School based oral health promotional intervention: Effect on knowledge, practices and clinical oral health related parameters

    Directory of Open Access Journals (Sweden)

    Arjun Gauba

    2013-01-01

    Full Text Available Background: No organized school oral health program is existent in India. Aim: The aim of this study is to test the feasibility and efficacy of an economical school oral health promotional intervention with educational and preventive components. Settings and Design: School oral health promotional intervention carried out in one of the randomly selected school and evaluated through short duration prospective model. Materials and Methods: A total of 100 children with an age range of 10-12 years with no previous history of dental intervention were enrolled. Interventions comprised of oral health education (delivered through lecture and demonstrations by an undergraduate dental student and topical antibacterial therapy (fluoride varnish and povidone iodine. Outcomes consisted of Knowledge and practices (KAP regarding oral health, clinical oral health related parameters such as plaque index (PI, gingival index (GI and caries activity as per Modified Snyder′s test. These were reported at baseline, 3 weeks and 6 months follow-up examination by a calibrated examiner. Statistical Analysis: McNemar Bowker′s test, Student′s t-test, Pearson Chi-square tests were used. Results: Highly significant (P < 0.001 improvements in KAP scores, PI scores, GI scores and caries activity were reported at 3 weeks and 6 months follow-up examination. Conclusion: This small economical school oral health program positively influenced oral health related practices and parameters of oral health such as oral cleanliness, gingival health and caries activity.

  17. Profiles of some clinical chemical parameters in growing dwarf and landrace kids.

    Science.gov (United States)

    Mbassa, G K; Poulsen, J S

    1991-11-01

    Plasma creatinine, urea, bilirubin, glucose, cholesterol, calcium, magnesium, sodium, potassium, inorganic phosphorus and total serum proteins were analyzed in kids of Dwarf and Danish Landrace breeds from birth to 12 months of age. The purpose was to determine the reference ranges, age profiles and the influence of other factors. Comparisons between parametric (mean +/- standard deviation) and the corresponding nonparametric (5th and 95th percentile, median) values were calculated for each parameter, the results of which indicated no apparent differences. The levels were very much dependent on age. Creatinine, urea and total serum protein levels increased gradually with age. Glucose and cholesterol levels were high at birth and then decreased with age. The electrolyte concentrations were maintained within narrow limits. Significant differences were observed between kids of different ages (within the breeds), breeds (within similar age) and herds (within the same age and breed). Differences between female and male Landrace kids of the same ages were observed in plasma urea, creatinine, glucose and total serum proteins. It is concluded that age has a major influence on the clinical chemical reference values in young goats, followed by herd and breed, but the influence of sex was small and is negligible in most parameters. PMID:1771990

  18. Nomograms for the Prediction of Pathologic Stage of Clinically Localized Prostate Cancer in Korean Men

    OpenAIRE

    Song, Cheryn; Kang, Taejin; Ro, Jae Y.; Lee, Moo-Song; Kim, Choung-Soo; Ahn, Hanjong

    2005-01-01

    We analyzed the prostate cancer data of 317 Korean men with clinically localized prostate cancer who underwent radical prostatectomy at Asan Medical Center between June 1990 and November 2003 to construct nomograms predicting the pathologic stage of these tumors, and compared the outcome with preexisting nomograms. Multinomial log-linear regression was performed for the simultaneous prediction of organ-confined disease (OCD), extracapsular extension (ECE), seminal vesicle invasion (SVI) and l...

  19. Robust Microarray Meta-Analysis Identifies Differentially Expressed Genes for Clinical Prediction

    OpenAIRE

    Phan, John H.; Andrew N. Young; Wang, May D.

    2012-01-01

    Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers. Because of this, feature selection methods often suffer from false discoveries, resulting in poorly performing predictive models. We develop a simple meta-analysis-ba...

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

    OpenAIRE

    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 clinically diagnosed prostate cancer. The study cohort comprised 269 Chinese prostate cancer patients who underwent prostate biopsy with a minimum of 10 cores and w...

  1. Capsaicin cough sensitivity and the association with clinical parameters in bronchiectasis.

    Directory of Open Access Journals (Sweden)

    Wei-jie Guan

    Full Text Available BACKGROUND: Cough hypersensitivity has been common among respiratory diseases. OBJECTIVE: To determine associations of capsaicin cough sensitivity and clinical parameters in adults with clinically stable bronchiectasis. METHODS: We recruited 135 consecutive adult bronchiectasis patients and 22 healthy subjects. History inquiry, sputum culture, spirometry, chest high-resolution computed tomography (HRCT, Leicester Cough Questionnaire scoring, Bronchiectasis Severity Index (BSI assessment and capsaicin inhalation challenge were performed. Cough sensitivity was measured as the capsaicin concentration eliciting at least 2 (C2 and 5 coughs (C5. RESULTS: Despite significant overlap between healthy subjects and bronchiectasis patients, both C2 and C5 were significantly lower in the latter group (all P<0.01. Lower levels of C5 were associated with a longer duration of bronchiectasis symptoms, worse HRCT score, higher 24-hour sputum volume, BSI and sputum purulence score, and sputum culture positive for P. aeruginosa. Determinants associated with increased capsaicin cough sensitivity, defined as C5 being 62.5 µmol/L or less, encompassed female gender (OR: 3.25, 95%CI: 1.35-7.83, P<0.01, HRCT total score between 7-12 (OR: 2.57, 95%CI: 1.07-6.173, P = 0.04, BSI between 5-8 (OR: 4.05, 95%CI: 1.48-11.06, P<0.01 and 9 or greater (OR: 4.38, 95%CI: 1.48-12.93, P<0.01. CONCLUSION: Capsaicin cough sensitivity is heightened in a subgroup of bronchiectasis patients and associated with the disease severity. Gender and disease severity, but not sputum purulence, are independent determinants of heightened capsaicin cough sensitivity. Current testing for cough sensitivity diagnosis may be limited because of overlap with healthy subjects but might provide an objective index for assessment of cough in future clinical trials.

  2. Predictors, Moderators, and Treatment Parameters of Community and Clinic-Based Treatment for Child Disruptive Behavior Disorders

    OpenAIRE

    Shelleby, Elizabeth C.; Kolko, David J.

    2013-01-01

    This study examines predictors, moderators, and treatment parameters associated with two key child outcomes in a recent clinical trial comparing the effects of a modular treatment that was applied by study clinicians in the community (COMM) or a clinic (CLINIC) for children with oppositional defiant disorder (ODD) or conduct disorder (CD). Based on a literature review, moderator and predictor variables across child, parent, and family domains were examined in relation to changes in parental r...

  3. Dosemetric Parameters Predictive of Rib Fractures after Proton Beam Therapy for Early-Stage Lung Cancer.

    Science.gov (United States)

    Ishikawa, Yojiro; Nakamura, Tatsuya; Kato, Takahiro; Kadoya, Noriyuki; Suzuki, Motohisa; Azami, Yusuke; Hareyama, Masato; Kikuchi, Yasuhiro; Jingu, Keiichi

    2016-01-01

    Proton beam therapy (PBT) is the preferred modality for early-stage lung cancer. Compared with X-ray therapy, PBT offers good dose concentration as revealed by the characteristics of the Bragg peak. Rib fractures (RFs) after PBT lead to decreased quality of life for patients. However, the incidence of and the risk factors for RFs after PBT have not yet been clarified. We therefore explored the relationship between irradiated rib volume and RFs after PBT for early-stage lung cancer. The purpose of this study was to investigate the incidence and the risk factors for RFs following PBT for early-stage lung cancer. We investigated 52 early-stage lung cancer patients and analyzed a total of 215 irradiated ribs after PBT. Grade 2 RFs occurred in 12 patients (20 ribs); these RFs were symptomatic without displacement. No patient experienced more severe RFs. The median time to grade 2 RFs development was 17 months (range: 9-29 months). The three-year incidence of grade 2 RFs was 30.2%. According to the analysis comparing radiation dose and rib volume using receiver operating characteristic curves, we demonstrated that the volume of ribs receiving more than 120 Gy3 (relative biological effectiveness (RBE)) was more than 3.7 cm(3) at an area under the curve of 0.81, which increased the incidence of RFs after PBT (P < 0.001). In this study, RFs were frequently observed following PBT for early-stage lung cancer. We demonstrated that the volume of ribs receiving more than 120 Gy3 (RBE) was the most significant parameter for predicting RFs. PMID:27087118

  4. Degree of Predicted Minor Histocompatibility Antigen Mismatch Correlates with Poorer Clinical Outcomes of Nonmyeloablative Allogeneic Hematopoietic Cell Transplantation

    DEFF Research Database (Denmark)

    Larsen, Malene Erup; Kornblit, B; Larsen, Mette Voldby; Masmas, TN; Nielsen, Morten; Thiim, Martin Hansen; Garred, P; Stryhn, A; Lund, Ole; Buus, S; Vindelov, L

    2010-01-01

    In fully HLA-matched allogeneic hematopoietic cell transplantations (HCT), the main mechanism of the beneficial graft-versus-tumor (GVT) effect and of the detrimental graft-versus-host disease (GVHD) is believed to be caused by donor cytotoxic T cells directed against disparate recipient minor...... HCT (matched related donor, n=70; matched unrelated donor, n=56) for hematologic malignancies. Initially, the cohort was genotyped for 53 nsSNPs in 11 known miHA source proteins. Twenty-three nsSNPs within six miHA source proteins showed variation in the graft-versus-host (GVH) direction. No...... mortality (39% vs 10%, P=0.0094, adjusted HR 4.6, P=0.0038). No association between number of predicted miHAs and any other clinical outcome parameters was observed. Collectively, our data suggest that the clinical outcome of HCT is not affected by disparate nsSNPs per se, but rather by the HLA...

  5. Debris-flows scale predictions based on basin spatial parameters calculated from Remote Sensing images in Wenchuan earthquake area

    International Nuclear Information System (INIS)

    Debris flow is a common hazard in the Wenchuan earthquake area. Collapse and Landslide Regions (CLR), caused by earthquakes, could be located from Remote Sensing images. CLR are the direct material source regions for debris flow. The Spatial Distribution of Collapse and Landslide Regions (SDCLR) strongly impact debris-flow formation. In order to depict SDCLR, we referred to Strahler's Hypsometric analysis method and developed 3 functional models to depict SDCLR quantitatively. These models mainly depict SDCLR relative to altitude, basin mouth and main gullies of debris flow. We used the integral of functions as the spatial parameters of SDCLR and these parameters were employed during the process of debris-flows scale predictions. Grouping-occurring debris-flows triggered by the rainstorm, which occurred on September 24th 2008 in Beichuan County, Sichuan province China, were selected to build the empirical equations for debris-flows scale predictions. Given the existing data, only debris-flows runout zone parameters (Max. runout distance L and Lateral width B) were estimated in this paper. The results indicate that the predicted results were more accurate when the spatial parameters were used. Accordingly, we suggest spatial parameters of SDCLR should be considered in the process of debris-flows scale prediction and proposed several strategies to prevent debris flow in the future

  6. QSAR Studies of Copper Azamacrocycles and Thiosemicarbazones: MM3 Parameter Development and Prediction of Biological Properties

    OpenAIRE

    Wolohan, Peter; Yoo, Jeongsoo; Welch, Michael J.; Reichert, David E.

    2005-01-01

    Genetic algorithms (GA) were used to develop specific copper metal-ligand force field parameters for the MM3 force field, from a combination of crystallographic structures and ab initio calculations. These new parameters produced results in good agreement with experiment and previously reported copper metal-ligand parameters for the AMBER force field. The MM3 parameters were then used to develop several Quantitative Structure Activity Relationship (QSAR) models. A successful QSAR for predicti...

  7. [On the prediction of the supercompensation phase by determining the parameters of the living functional system of the body].

    Science.gov (United States)

    Zaĭtsev, A A; Sazonov, S V

    2007-01-01

    On the basis of the overdamped Duffing model, a technique for determining the key parameters of functional systems of the living body has been developed which characterizes its properties during recovery from standard physical load. As an example, the dynamics of restoration of pulse frequency is considered. The knowledge of these parameters allows one to predict the response of the living body to intensive external loads. This information can also be helpful for the optimization of the training process. PMID:17907417

  8. 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.

  9. 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.

  10. 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.

  11. Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model

    Science.gov (United States)

    Hu, Scott B.; Wong, Deborah J. L.; Correa, Aditi; Li, Ning; Deng, Jane C.

    2016-01-01

    Introduction Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5–10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used routine vital signs and laboratory values obtained from the electronic medical record (EMR) along with a machine learning algorithm called a neural network to develop a prediction model that would increase the predictive accuracy and decrease false alarm rates. Design Retrospective cohort study. Setting The hematologic malignancy unit in an academic medical center in the United States. Patient Population Adult patients admitted to the hematologic malignancy unit from 2009 to 2010. Intervention None. Measurements and Main Results Vital signs and laboratory values were obtained from the electronic medical record system and then used as predictors (features). A neural network was used to build a model to predict clinical deterioration events (ICU transfer and cardiac arrest). The performance of the neural network model was compared to the VitalPac Early Warning Score (ViEWS). Five hundred sixty five consecutive total admissions were available with 43 admissions resulting in clinical deterioration. Using simulation, the neural network outperformed the ViEWS model with a positive predictive value of 82% compared to 24%, respectively. Conclusion We developed and tested a neural network-based prediction model for clinical deterioration in patients hospitalized in the hematologic malignancy unit. Our neural network model outperformed an existing model, substantially increasing the positive predictive value, allowing the clinician to be confident in the alarm raised. This system can be readily implemented in a real-time fashion in existing EMR systems. PMID:27532679

  12. Clinical and Actuarial Prediction of Physical Violence in a Forensic Intellectual Disability Hospital: A Longitudinal Study

    Science.gov (United States)

    McMillan, Dean; Hastings, Richard P.; Coldwell, Jon

    2004-01-01

    Background: There is a high rate of physical violence in populations with intellectual disabilities, and this has been linked to problems for the victim, the assailant, members of staff and services. Despite the clinical significance of this behaviour, few studies have assessed methods of predicting its occurrence. The present study examined…

  13. 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...

  14. Role of parameter errors in the spring predictability barrier for ENSO events in the Zebiak-Cane model

    Science.gov (United States)

    Yu, Liang; Mu, Mu; Yu, Yanshan

    2014-05-01

    The impact of both initial and parameter errors on the spring predictability barrier (SPB) is investigated using the Zebiak-Cane model (ZC model). Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation (CNOP) errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors (optimal errors when only initial errors are considered) are calculated and then three aspects of error growth are compared: (1) the tendency of the seasonal error growth; (2) the prediction error of the sea surface temperature anomaly; and (3) the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the El Niño during spring by simply focusing on reducing the initial errors in this model.

  15. 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; Arce, Joan-Carles

    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 infert...

  16. Clinical prediction of occupational and non-specific low back pain

    Directory of Open Access Journals (Sweden)

    Ingrid Tolosa-Guzmán

    2012-09-01

    Full Text Available Non-specific Occupational Low Back Pain (NOLBP is a health condition that generates a high absenteeism and disability. Due to multifactorial causes is difficult to determine accurate diagnosis and prognosis. The clinical prediction of NOLBP is identified as a series of models that integrate a multivariate analysis to determine early diagnosis, course, and occupational impact of this health condition. Objective: to identify predictor factors of NOLBP, and the type of material referred to in the scientific evidence and establish the scopes of the prediction. Materials and method: the title search was conducted in the databases PubMed, Science Direct, and Ebsco Springer, between 1985 and 2012. The selected articles were classified through a bibliometric analysis allowing to define the most relevant ones. Results: 101 titles met the established criteria, but only 43 met the purpose of the review. As for NOLBP prediction, the studies varied in relation to the factors for example: diagnosis, transition of lumbar pain from acute to chronic, absenteeism from work, disability and return to work. Conclusion: clinical prediction is considered as a strategic to determine course and prognostic of NOLBP, and to determine the characteristics that increase the risk of chronicity in workers with this health condition. Likewise, clinical prediction rules are tools that aim to facilitate decision making about the evaluation, diagnosis, prognosis and intervention for low back pain, which should incorporate risk factors of physical, psychological and social.

  17. An approach to predicting bowing control parameter contours in violin performance

    OpenAIRE

    Maestre E.; Ramirez R.

    2010-01-01

    We present a machine learning approach to modeling bowing control parameter contours in violin performance. Using accurate sensing techniques we obtain relevant timbre-related bowing control parameters such as bow transversal velocity, bow pressing force, and bow-bridge distance of each performed note. Each performed note is represented by a curve parameter vector and a number of note classes are defined. The principal components of the data represented by the set of curve p...

  18. Simple blood tests as predictive markers of disease severity and clinical condition in patients with venous insufficiency.

    Science.gov (United States)

    Karahan, Oguz; Yavuz, Celal; Kankilic, Nazim; Demirtas, Sinan; Tezcan, Orhan; Caliskan, Ahmet; Mavitas, Binali

    2016-09-01

    Chronic venous insufficiency (CVI) is a progressive inflammatory disease. Because of its inflammatory nature, several circulating markers were investigated for predicting disease progression. We aimed to investigate simple inflammatory blood markers as predictors of clinical class and disease severity in patients with CVI. Eighty patients with CVI were divided into three groups according to clinical class (grade 1, 2 and 3) and score of disease severity (mild, moderate and severe). The basic inflammatory blood markers [neutrophil, lymphocyte, mean platelet volume (MPV), white blood cell (WBC), platelet, albumin, D-dimer, fibrinogen, fibrinogen to albumin ratio, and neutrophil to lymphocyte ratio] were investigated in each group. Serum neutrophil, lymphocyte, MPV, platelet count, D-dimer and neutrophil to lymphocyte ratio levels were similar among the groups (P > 0.05). Although the serum WBC levels were significant in the clinical severity groups (P < 0.05), it was useless to separate each severity class. However, albumin, fibrinogen and the fibrinogen to albumin ratio were significant predictors of clinical class and disease severity. Especially, the fibrinogen to albumin ratio was detected as an independent indicator for a clinical class and disease severity with high sensitivity and specificity (75% sensitivity and 87.5% specificity for clinical class and 90% sensitivity and 88.3% specificity for disease severity). Serum fibrinogen and albumin levels can be useful parameters to determine clinical class and disease severity in patients with CVI. Moreover, the fibrinogen to albumin ratio is a more sensitive and specific predictor of the progression of CVI. PMID:26650463

  19. Physical re-examination of parameters on a molecular collisions-based diffusion model for diffusivity prediction in polymers.

    Science.gov (United States)

    Ohashi, Hidenori; Tamaki, Takanori; Yamaguchi, Takeo

    2011-12-29

    Molecular collisions, which are the microscopic origin of molecular diffusive motion, are affected by both the molecular surface area and the distance between molecules. Their product can be regarded as the free space around a penetrant molecule defined as the "shell-like free volume" and can be taken as a characteristic of molecular collisions. On the basis of this notion, a new diffusion theory has been developed. The model can predict molecular diffusivity in polymeric systems using only well-defined single-component parameters of molecular volume, molecular surface area, free volume, and pre-exponential factors. By consideration of the physical description of the model, the actual body moved and which neighbor molecules are collided with are the volume and the surface area of the penetrant molecular core. In the present study, a semiempirical quantum chemical calculation was used to calculate both of these parameters. The model and the newly developed parameters offer fairly good predictive ability. PMID:22082054

  20. 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.

  1. Clinical and Radiographic Factors Predicting Hearing Preservation Rates in Large Vestibular Schwannomas.

    Science.gov (United States)

    Mendelsohn, Daniel; Westerberg, Brian D; Dong, Charles; Akagami, Ryojo

    2016-06-01

    Objectives Postoperative hearing preservation rates for patients with large vestibular schwannomas range from 0 to 43%. The clinical and radiographic factors predicting hearing preservation in smaller vestibular schwannomas are well described; however, their importance in larger tumors is unclear. We investigated factors predicting hearing preservation in large vestibular schwannomas. Design Retrospective review. Setting Quaternary care academic center. Participants A total of 85 patients with unilateral vestibular schwannomas > 3 cm underwent retrosigmoid resections. Main Outcomes Measures Preoperative and postoperative serviceable hearing rates. Methods Clinical and radiographic data including preoperative and postoperative audiograms, preoperative symptoms, magnetic resonance imaging features, and postoperative facial weakness were analyzed. Results Hearing was preserved in 41% of patients (17 of 42) with preoperative serviceable hearing. Hypertension and diabetes increased the likelihood of preoperative hearing loss. Preoperative tinnitus predicted a lower likelihood of hearing preservation. No radiographic factors predicted hearing preservation; however, larger tumor size, smaller fourth ventricular width, and the presence of a cerebrospinal fluid cleft surrounding the tumor predicted postoperative facial weakness. Conclusion Systemic comorbidities may influence hearing loss preoperatively in patients with large vestibular schwannomas. The absence of tinnitus may reflect hearing reserve and propensity for hearing preservation. Preoperative radiographic features did not predict hearing preservation despite some associations with postoperative facial weakness. PMID:27175312

  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. Platelet parameters from an automated hematology analyzer in dogs with inflammatory clinical diseases.

    Science.gov (United States)

    Smith, Jo R; Smith, Katherine F; Brainard, Benjamin M

    2014-09-01

    The mean platelet component (MPC) is a proprietary algorithm of an automated laser-based hematology analyzer system which measures the refractive index of platelets. The MPC is related linearly to platelet density and is an indirect index of platelet activation status. Previous investigations of canine inflammatory conditions and models of endotoxemia demonstrated a significant decrease in the MPC, consistent with platelet activation. The purpose of this study was to evaluate the MPC and other platelet parameters in dogs with different diseases to determine if they could show differential platelet activation with different pathologies. The hypothesis was that the MPC would decrease in clinical conditions associated with systemic inflammation or platelet activation. Complete blood counts run on the analyzer from dogs with different inflammatory conditions (primary immune-mediated hemolytic anemia (IMHA) or thrombocytopenia (ITP), pituitary-dependent hyperadrenocorticism, intra-abdominal sepsis, pancreatitis, intravascular thrombus or thromboembolus and hemangiosarcoma) were reviewed retrospectively and compared with those of control dogs presenting for orthopedic evaluation. Dogs with ITP had a decreased plateletcrit and MPC, with an increased platelet volume and number of large platelets (P Dogs with IMHA had an increased plateletcrit and mass, and more numerous large platelets (P < 0.001).With the exception of the ITP group, there was no difference in MPC in the diseased groups when compared with the controls. The results of this study suggest the MPC does not change in certain canine diseases associated with systemic inflammation. PMID:25082397

  4. 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.

  5. 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.

  6. Relative role of parameter vs. climate uncertainty for predictions of future Southeastern U.S. pine carbon cycling

    Science.gov (United States)

    Jersild, A.; Thomas, R. Q.; Brooks, E.; Teskey, R. O.; Wynne, R. H.; Arthur, D.; Gonzalez, C.; Thomas, V. A.; Fox, T. D.; Smallman, L.

    2015-12-01

    Predictions of the how forest productivity and carbon sequestration will respond to climate change are essential for assisting land managers in adapting to future climate. However, current predictions can include considerable uncertainty that is often not well quantified. To address the need for better quantification of uncertainty, we calculated and compared parameter and climate prediction uncertainty for predictions of Southeastern U.S. pine forest productivity. We used a Metropolis-Hastings Markov Chain Monte Carlo-based data assimilation technique to fuse regionally widespread and diverse datasets with the Physiological Principles Predicting Growth model (3PG) model. The datasets incorporated include biomass observations from forest research plots that are part of the Pine Integrated Network: Education, Mitigation, and Adaptation project (PINEMAP) project, photosynthesis and evaporation observations from loblolly pine Ameriflux sites, and productivity responses to elevated CO2 from the Duke Free Air C site. These spatially and temporally diverse data sets give our unique analysis a more accurately measured uncertainty by constraining complimentary components of the model. In our analysis, parameter uncertainty was quantified using simulations that integrate across the posterior parameter distributions, while climate model uncertainty was quantified using downscaled RCP 8.5 simulations from twenty different CMIP5 climate models. Overall, we found that the uncertainty in future productivity of Southeastern U.S. managed pine forests that was associated with parameterization is comparable to the uncertainty associated with climate simulations. Our results indicate that reducing parameterization in ecosystem model development can improve future predictions of forest productivity and carbon sequestration, but uncertainties in future climate predictions also need to be properly quantified and communicated to forest owners and managers.

  7. 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...

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Parameters extraction of photovoltaic module for long-term prediction using Artifical Bee Colony optimization

    OpenAIRE

    Garoudja, Elyes; Kara, Kamel; Chouder, Aissa; Silvestre Bergés, Santiago

    2015-01-01

    In this paper, a heuristic optimization approach based on Artificial Bee Colony (ABC) algorithm is applied to the extraction of the five electrical parameters of a photovoltaic (PV) module. The proposed approach has several interesting features such as no prior knowledge of the physical system and its convergence is not dependent on the initial conditions. The extracted parameters have been tested against several static IV characteristics of different PV modules from diff...

  14. 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. PMID:25576352

  15. Interleukin-1β level in peri-implant crevicular fluid and its correlation with the clinical and radiographic parameters

    OpenAIRE

    Aniruddha M Kajale; Mehta, Dhoom S.

    2014-01-01

    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 biochemic...

  16. Predicting Ovarian Cancer Patients' Clinical Response to Platinum-Based Chemotherapy by Their Tumor Proteomic Signatures.

    Science.gov (United States)

    Yu, Kun-Hsing; Levine, Douglas A; Zhang, Hui; Chan, Daniel W; Zhang, Zhen; Snyder, Michael

    2016-08-01

    Ovarian cancer is the deadliest gynecologic malignancy in the United States with most patients diagnosed in the advanced stage of the disease. Platinum-based antineoplastic therapeutics is indispensable to treating advanced ovarian serous carcinoma. However, patients have heterogeneous responses to platinum drugs, and it is difficult to predict these interindividual differences before administering medication. In this study, we investigated the tumor proteomic profiles and clinical characteristics of 130 ovarian serous carcinoma patients analyzed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), predicted the platinum drug response using supervised machine learning methods, and evaluated our prediction models through leave-one-out cross-validation. Our data-driven feature selection approach indicated that tumor proteomics profiles contain information for predicting binarized platinum response (P drug responses as well as provided insights into the biological processes influencing the efficacy of platinum-based therapeutics. Our analytical approach is also extensible to predicting response to other antineoplastic agents or treatment modalities for both ovarian and other cancers. PMID:27312948

  17. Predicting reoffense in pedophilic child molesters by clinical diagnoses and risk assessment.

    Science.gov (United States)

    Eher, Reinhard; Olver, Mark E; Heurix, Isabelle; Schilling, Frank; Rettenberger, Martin

    2015-12-01

    A Diagnostic and Statistical Manual of Mental Disorders (DSM)-based diagnosis of pedophilia has so far failed to predict sexual reoffense in convicted child molesters, probably because of its broad and unspecific conceptualization. In this study, therefore, we investigated the prognostic value of the subtype exclusive pedophilia and a series of customary risk assessment instruments (SSPI, Static-99, Stable-2007, VRS:SO) and the PCL-R in a sample of prison released pedophilic sexual offenders. First, we examined the convergent validity of risk assessment instruments (N = 261). Then, we calculated the predictive accuracy of the measures and diagnosis for sexual recidivism by ROC analyses and subsequent Cox regression (N = 189). Also, predictive values with more clinical immediacy were calculated (sensitivity, specificity, PPV and NPV). The VRS:SO, the SSPI, and the Static-99 significantly predicted sexual recidivism, as did a diagnosis of exclusive pedophilia. Also, the VRS:SO predicted sexual reoffense significantly better than the Stable-2007, the Static-99/Stable-2007 combined score, and the PCL-R. When used combined, only the VRS:SO and a diagnosis of exclusive pedophilia added incremental validity to each other. Our findings support that the clinical diagnosis of an exclusive pedophilia based on DSM criteria and VRS:SO defined risk factors can reliably discriminate higher from lower risk offenders, even within the select subgroup of pedophilic child molesters. PMID:26146817

  18. 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.

  19. [Dissociation of structural and functional parameters of the retina and optic nerve in a patient with Alzheimer's disease (clinical case)].

    Science.gov (United States)

    Erichev, V P; Panyushkina, L A; Ronzina, I A

    2015-01-01

    Visual impairment is often one of the earliest sings of Alzheimer's disease. This article reports a clinical case of a female patient diagnosed with mild dementia due to Alzheimer's disease. As revealed by a comprehensive examination, her visual fields and visual evoked potentials were markedly changed, while morphometric parameters of the retina and optic nerve appeared normal. Such a significant dissociation of structural and functional parameters may indicate a more proximal involvement of visual pathways in Alzheimer's disease. PMID:26080589

  20. Delayed heart rate recovery after treadmill test: comparison with clinical, exercise and myocardial perfusion parameters

    International Nuclear Information System (INIS)

    Imbalance autonomic nervous tone are fundamental risk factors for cardiac death. Recent studies reported abnormal heart rate recovery(HRR) after the treadmill exercise test is a powerful predictor of significant excess mortality. To evaluate HRR as an index of coronary artery disease(CAD). we have compared perfusion defect. 252 patients(147 men) underwent exercise myocardial perfusion imaging were included. The value for HRR was defined as the decrease in heart rate from peak exercise to 1 minute after termination of exercise. Myocardial perfusion imaging was acquired at 1 hour after 740MBq 99mTc-MIBI injection using dual head gamma camera(Vertex Plus, ADAC, USA). Summed stress score(SSS) and stress ejection fraction(sEF) were obtained from AutoQUANT program. 23 beats/min was defined as the lowest normal value for HRR. Patients were divided two groups: abnormal HRR(abHRR) and normal HRR(nHRR). We compared clinical(age, sex, pervious CAD history, DM, HTN), exercise test(exercise capacity, duration) and myocardial perfusion parameters(SSS, sEF) between two groups. Mean value of HRR was 50.814.2 beat/min. There were 25 patients(9.9%) with an abHRR. Patient with abHRR were generally in older age(61.59.2 vs 54.48.9yr), were more likely men(72 vs 56.8%), had a higher frequency of DM(16.7 vs 9%), HTN(52 vs 27.6%) and CAD history (28 vs 7%) compared to nHRR. In exercise and myocardial perfusion parameters, abHRR were showed more positive result(60 vs 30%), had short exercise duration(7.0±3.0 vs 9.1±2.7min) and small exercise capacity(7.2±2.3 vs 10.0±2.7Mets) compared to nHRR, had a higher frequency of CAD(76 vs 41.4%) and multivessel disease(25 vs 6.5%), had larger SSS(8.1±8.8 vs 3.7±6.3) and had smaller sEF(47.7±14.3 vs 57.9±10.3%) compared to nHRR. AbHRR was frequently found in patients with CAD, large myocardial perfusion defect and decreased LV function. It seems that the HRR may be considered a reliable index of the severity of CAD

  1. Radiobiologic Parameters and Local Effect Model Predictions for Head-and-Neck Squamous Cell Carcinomas Exposed to High Linear Energy Transfer Ions

    International Nuclear Information System (INIS)

    Purpose: To establish the radiobiologic parameters of head-and-neck squamous cell carcinomas (HNSCC) in response to ion irradiation with various linear energy transfer (LET) values and to evaluate the relevance of the local effect model (LEM) in HNSCC. Methods and Materials: Cell survival curves were established in radiosensitive SCC61 and radioresistant SQ20B cell lines irradiated with [33.6 and 184 keV/n] carbon, [302 keV/n] argon, and X-rays. The results of ion experiments were confronted to LEM predictions. Results: The relative biologic efficiency ranged from 1.5 to 4.2 for SCC61 and 2.1 to 2.8 for SQ20B cells. Fixing an arbitrary D0 parameter, which characterized survival to X-ray at high doses (>10 Gy), gave unsatisfying LEM predictions for both cell lines. For D0 = 10 Gy, the error on survival fraction at 2 Gy amounted to a factor of 10 for [184 keV/n] carbon in SCC61 cells. We showed that the slope (smax) of the survival curve at high doses was much more reliable than D0. Fitting smax to 2.5 Gy-1 gave better predictions for both cell lines. Nevertheless, LEM could not predict the responses to fast and slow ions with the same accuracy. Conclusions: The LEM could predict the main trends of these experimental data with correct orders of magnitude while smax was optimized. Thus the efficiency of carbon ions cannot be simply extracted from the clinical response of a patient to X-rays. LEM should help to optimize planning for hadrontherapy if a set of experimental data is available for high-LET radiations in various types of tumors

  2. Physiologically-based pharmacokinetic modeling to predict the clinical pharmacokinetics of monoclonal antibodies.

    Science.gov (United States)

    Glassman, Patrick M; Balthasar, Joseph P

    2016-08-01

    Accurate prediction of the clinical pharmacokinetics of new therapeutic entities facilitates decision making during drug discovery, and increases the probability of success for early clinical trials. Standard strategies employed for predicting the pharmacokinetics of small-molecule drugs (e.g., allometric scaling) are often not useful for predicting the disposition monoclonal antibodies (mAbs), as mAbs frequently demonstrate species-specific non-linear pharmacokinetics that is related to mAb-target binding (i.e., target-mediated drug disposition, TMDD). The saturable kinetics of TMDD are known to be influenced by a variety of factors, including the sites of target expression (which determines the accessibility of target to mAb), the extent of target expression, the rate of target turnover, and the fate of mAb-target complexes. In most cases, quantitative information on the determinants of TMDD is not available during early phases of drug discovery, and this has complicated attempts to employ mechanistic mathematical models to predict the clinical pharmacokinetics of mAbs. In this report, we introduce a simple strategy, employing physiologically-based modeling, to predict mAb disposition in humans. The approach employs estimates of inter-antibody variability in rate processes of extravasation in tissues and fluid-phase endocytosis, estimates for target concentrations in tissues derived through use of categorical immunohistochemical scores, and in vitro measures of the turnover of target and target-mAb complexes. Monte Carlo simulations were performed for four mAbs (cetuximab, figitumumab, dalotuzumab, trastuzumab) directed against three targets (epidermal growth factor receptor, insulin-like growth factor receptor 1, human epidermal growth factor receptor 2). The proposed modeling strategy was able to predict well the pharmacokinetics of cetuximab, dalotuzumab, and trastuzumab at a range of doses, but trended towards underprediction of figitumumab concentrations

  3. Postoperative myocardial infarction and cardiac death. Predictive value of dipyridamole-thallium imaging and five clinical scoring systems based on multifactorial analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lette, J.; Waters, D.; Lassonde, J.; Dube, S.; Heyen, F.; Picard, M.; Morin, M. (Maisonneuve-Rosemont Hospital, Montreal (Canada))

    1990-01-01

    Sixty-six patients unable to complete a standard preoperative exercise test because of physical limitations were studied to determine the predictive value of individual clinical parameters, of clinical scoring systems based on multifactorial analysis, and of dipyridamole-thallium imaging before major general and vascular surgery. Study endpoints were limited to postoperative myocardial infarction or cardiac death before hospital discharge. There were nine postoperative cardiac events (seven deaths and two nonfatal infarctions). There was no statistical correlation between cardiac events and preoperative clinical descriptors, including individual clinical parameters, the Dripps-American Surgical Association score, the Goldman Cardiac Risk Index score, the Detsky Modified Cardiac Risk Index score, Eagle's clinical markers of low surgical risk, and the probability of postoperative events as determined by Cooperman's equation. There were no cardiac events in 30 patients with normal dipyridamole-thallium scans or in nine patients with fixed myocardial perfusion defects. Of 21 patients with reversible perfusion defects who underwent surgery, nine had a postoperative cardiac event (sensitivity, 100%; specificity, 43%). In the six other patients with reversible defects, preoperative angiography showed severe coronary disease or cardiomyopathy. Thus in patients unable to complete a standard exercise stress test, postoperative outcome cannot be predicted clinically before major general and vascular surgery, whereas dipyridamole-thallium imaging successfully identified all patients who sustained a postoperative cardiac event.

  4. Postoperative myocardial infarction and cardiac death. Predictive value of dipyridamole-thallium imaging and five clinical scoring systems based on multifactorial analysis

    International Nuclear Information System (INIS)

    Sixty-six patients unable to complete a standard preoperative exercise test because of physical limitations were studied to determine the predictive value of individual clinical parameters, of clinical scoring systems based on multifactorial analysis, and of dipyridamole-thallium imaging before major general and vascular surgery. Study endpoints were limited to postoperative myocardial infarction or cardiac death before hospital discharge. There were nine postoperative cardiac events (seven deaths and two nonfatal infarctions). There was no statistical correlation between cardiac events and preoperative clinical descriptors, including individual clinical parameters, the Dripps-American Surgical Association score, the Goldman Cardiac Risk Index score, the Detsky Modified Cardiac Risk Index score, Eagle's clinical markers of low surgical risk, and the probability of postoperative events as determined by Cooperman's equation. There were no cardiac events in 30 patients with normal dipyridamole-thallium scans or in nine patients with fixed myocardial perfusion defects. Of 21 patients with reversible perfusion defects who underwent surgery, nine had a postoperative cardiac event (sensitivity, 100%; specificity, 43%). In the six other patients with reversible defects, preoperative angiography showed severe coronary disease or cardiomyopathy. Thus in patients unable to complete a standard exercise stress test, postoperative outcome cannot be predicted clinically before major general and vascular surgery, whereas dipyridamole-thallium imaging successfully identified all patients who sustained a postoperative cardiac event

  5. 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.

  6. Prediction of optimal operation point existence and parameters in lossy compression of noisy images

    Science.gov (United States)

    Zemliachenko, Alexander N.; Abramov, Sergey K.; Lukin, Vladimir V.; Vozel, Benoit; Chehdi, Kacem

    2014-10-01

    This paper deals with lossy compression of images corrupted by additive white Gaussian noise. For such images, compression can be characterized by existence of optimal operation point (OOP). In OOP, MSE or other metric derived between compressed and noise-free image might have optimum, i.e., maximal noise removal effect takes place. If OOP exists, then it is reasonable to compress an image in its neighbourhood. If no, more "careful" compression is reasonable. In this paper, we demonstrate that existence of OOP can be predicted based on very simple and fast analysis of discrete cosine transform (DCT) statistics in 8x8 blocks. Moreover, OOP can be predicted not only for conventional metrics as MSE or PSNR but also for visual quality metrics. Such prediction can be useful in automatic compression of multi- and hyperspectral remote sensing images.

  7. [Critical evaluation and predictive value of clinical presentation in out-patients with acute community-acquired pneumonia].

    Science.gov (United States)

    Mayaud, C; Fartoukh, M; Prigent, H; Parrot, A; Cadranel, J

    2006-01-01

    Diagnostic probability of community-acquired pneumonia (CAP) depends on data related to age and clinical and radiological findings. The critical evaluation of data in the literature leads to the following conclusions: 1) the prevalence of CAP in a given population with acute respiratory disease is 5% in outpatients and 10% in an emergency care unit. This could be as low as 2% in young people and even higher than 40% in hospitalized elderly patients; 2) the collection of clinical data is linked to the way the patient is examined and to the expertise of the clinician. The absolute lack of "vital signs" has a good negative predictive value in CAP; presence of unilateral crackles has a good positive predictive value; 3) there is a wide range of X-ray abnormalities: localized alveolar opacities; interstitial opacities, limited of diffused. The greatest radiological difficulties are encountered in old people with disorders including chronic respiratory or cardiac opacities and as a consequence of the high prevalence of bronchopneumonia episodes at this age; 4) among patients with lower respiratory tract (LRT) infections, the blood levels of leukocytes, CRP and procalcitonine are higher in CAP patients, mainly when their disease has a bacterial origin. Since you have not a threshold value reliably demonstrated in large populations with LRT infections or acute respiratory disease, presence or absence of these parameters could only be taken as a slight hint for a CAP diagnosis. PMID:17084571

  8. Method for evaluation of predictive models of microwave ablation via post-procedural clinical imaging

    Science.gov (United States)

    Collins, Jarrod A.; Brown, Daniel; Kingham, T. Peter; Jarnagin, William R.; Miga, Michael I.; Clements, Logan W.

    2015-03-01

    Development of a clinically accurate predictive model of microwave ablation (MWA) procedures would represent a significant advancement and facilitate an implementation of patient-specific treatment planning to achieve optimal probe placement and ablation outcomes. While studies have been performed to evaluate predictive models of MWA, the ability to quantify the performance of predictive models via clinical data has been limited to comparing geometric measurements of the predicted and actual ablation zones. The accuracy of placement, as determined by the degree of spatial overlap between ablation zones, has not been achieved. In order to overcome this limitation, a method of evaluation is proposed where the actual location of the MWA antenna is tracked and recorded during the procedure via a surgical navigation system. Predictive models of the MWA are then computed using the known position of the antenna within the preoperative image space. Two different predictive MWA models were used for the preliminary evaluation of the proposed method: (1) a geometric model based on the labeling associated with the ablation antenna and (2) a 3-D finite element method based computational model of MWA using COMSOL. Given the follow-up tomographic images that are acquired at approximately 30 days after the procedure, a 3-D surface model of the necrotic zone was generated to represent the true ablation zone. A quantification of the overlap between the predicted ablation zones and the true ablation zone was performed after a rigid registration was computed between the pre- and post-procedural tomograms. While both model show significant overlap with the true ablation zone, these preliminary results suggest a slightly higher degree of overlap with the geometric model.

  9. Handbook of parameter values for the prediction of radionuclide transfer to wildlife

    International Nuclear Information System (INIS)

    This handbook provides generic parameter values for estimating the transfer of radionuclides from environmental media to wildlife for the purpose of assessing potential radiation exposure under equilibrium conditions. These data are intended for use where site specific data are either not available or not required, and to parameterize generic assessment models. They are based on a comprehensive review of the available literature, including many Russian language publications that have not previously been available in English. The publication addresses the limitations of the parameter values and the applicability of data. Some general background information on the assessment of potential impacts of radioactive releases on wildlife is also included. It complements the existing handbook in the same IAEA series with parameter to assess the radiological impact to humans

  10. FEM and ANN combined approach for predicting pressure source parameters at Etna volcano

    Directory of Open Access Journals (Sweden)

    A. Di Stefano

    2010-05-01

    Full Text Available A hybrid approach for forward and inverse geophysical modeling, based on Artificial Neural Networks (ANN and Finite Element Method (FEM, is proposed in order to properly identify the parameters of volcanic pressure sources from geophysical observations at ground surface. The neural network is trained and tested with a set of patterns obtained by the solutions of numerical models based on FEM. The geophysical changes caused by magmatic pressure sources were computed developing a 3-D FEM model with the aim to include the effects of topography and medium heterogeneities at Etna volcano. ANNs are used to interpolate the complex non linear relation between geophysical observations and source parameters both for forward and inverse modeling. The results show that the combination of neural networks and FEM is a powerful tool for a straightforward and accurate estimation of source parameters in volcanic regions.

  11. 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.

  12. 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

  13. Handbook of parameter values for the prediction of radionuclide transfer in temperate environments

    International Nuclear Information System (INIS)

    This Handbook has been prepared in response to a widely expressed interest in having a convenient and authoritative reference for radionuclide transfer parameter values used in biospheric assessment models. It draws on data from North America and Europe, much of which was collected through projects of the International Union of Radioecologists (IUR) and the Commission of European Communities (CEC) over the last decade. It is intended to supplement existing IAEA publications on environmental assessment methodology, primarily Generic Models and Parameters for Assessing the Environmental Transfer of Radionuclides from Routine Releases, IAEA Safety Series No. 57 (1982). 219 refs, 3 figs, 32 tabs

  14. 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.

  15. Effective feature selection of clinical and genetic to predict warfarin dose using artificial neural network

    OpenAIRE

    Mohammad Karim Sohrabi; Alireza Tajik

    2016-01-01

    Background: Warfarin is one of the most common oral anticoagulant, which role is to prevent the clots. The dose of this medicine is very important because changes can be dangerous for patients. Diagnosis is difficult for physicians because increase and decrease in use of warfarin is so dangerous for patients. Identifying the clinical and genetic features involved in determining dose could be useful to predict using data mining techniques. The aim of this paper is to provide a convenient way t...

  16. Fusion of clinical and stochastic finite element data for hip fracture risk prediction.

    Science.gov (United States)

    Jiang, Peng; Missoum, Samy; Chen, Zhao

    2015-11-26

    Hip fracture affects more than 250,000 people in the US and 1.6 million worldwide per year. With an aging population, the development of reliable fracture risk models is therefore of prime importance. Due to the complexity of the hip fracture phenomenon, the use of clinical data only, as it is done traditionally, might not be sufficient to ensure an accurate and robust hip fracture prediction model. In order to increase the predictive ability of the risk model, the authors propose to supplement the clinical data with computational data from finite element models. The fusion of the two types of data is performed using deterministic and stochastic computational data. In the latter case, uncertainties in loading and material properties of the femur are accounted for and propagated through the finite element model. The predictive capability of a support vector machine (SVM) risk model constructed by combining clinical and finite element data was assessed using a Women׳s Health Initiative (WHI) dataset. The dataset includes common factors such as age and BMD as well as geometric factors obtained from DXA imaging. The fusion of computational and clinical data systematically leads to an increase in predictive ability of the SVM risk model as measured by the AUC metric. It is concluded that the largest gains in AUC are obtained by the stochastic approach. This gain decreases as the dimensionality of the problem increases: a 5.3% AUC improvement was achieved for a 9 dimensional problem involving geometric factors and weight while a 1.3% increase was obtained for a 20 dimensional case including geometric and conventional factors. PMID:26482733

  17. A Clinical Scoring System to Predict the Development of Bronchopulmonary Dysplasia

    OpenAIRE

    Gürsoy, Tuğba; Hayran, Mutlu; Derin, Hatice; Ovalı, Fahri

    2015-01-01

    ObjectiveThis study aims to develop a scoring system for the prediction of bronchopulmonary dysplasia (BPD). MethodsMedical records of 652 infants whose gestational age and birth weight were below 32 weeks and 1,500g, respectively, and who survived beyond 28th postnatal day were reviewed retrospectively. Logistic regression methods were used to determine the clinical and demographic risk factors within the first 72 hours of life associated with BPD, as well as the weights of these factors on ...

  18. Prediction of the saturated hydraulic conductivity from Brooks and Corey’s water retention parameters

    NARCIS (Netherlands)

    P. Nasta; J.A. Vrugt; N. Romano

    2013-01-01

    Prediction of flow through variably saturated porous media requires accurate knowledge of the soil hydraulic properties, namely the water retention function (WRF) and the hydraulic conductivity function (HCF). Unfortunately, direct measurement of the HCF is time consuming and expensive. In this stud

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

    DEFF Research Database (Denmark)

    Henriksen, H. Risvig; Madsen, Jørgen Øgaard

    From users' and the safety officers' point of view, it is important among the products available in the market to find the protective glove, suit or accessory with the optimal permeation characteristics - breakthrough time and permeation rate. A reliable and efficient predictive method would be v...

  20. 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...

  1. Crack under biaxial loading: Two-parameter description and prediction of crack growth direction

    Czech Academy of Sciences Publication Activity Database

    Seitl, Stanislav

    2014-01-01

    Roč. 31, APR (2014), s. 44-49. ISSN 0213-3725 R&D Projects: GA MŠk(CZ) 7AMB14AT012 Institutional support: RVO:68081723 Keywords : Concrete * T-stress * cracks growth prediction * numerical calculation * biaxial loading Subject RIV: JL - Materials Fatigue, Friction Mechanics

  2. Developing a computational tool for predicting physical parameters of a typical VVER-1000 core based on artificial neural network

    International Nuclear Information System (INIS)

    Highlights: ► Thermal–hydraulics parameters of a VVER-1000 core based on neural network (ANN), are carried out. ► Required data for ANN training are found based on modified COBRA-EN code and then linked each other using MATLAB software. ► Based on ANN method, average and maximum temperature of fuel and clad as well as MDNBR of each FA are predicted. -- Abstract: The main goal of the present article is to design a computational tool to predict physical parameters of the VVER-1000 nuclear reactor core based on artificial neural network (ANN), taking into account a detailed physical model of the fuel rods and coolant channels in a fuel assembly. Predictions of thermal characteristics of fuel, clad and coolant are performed using cascade feed forward ANN based on linear fission power distribution and power peaking factors of FAs and hot channels factors (which are found based on our previous neutronic calculations). A software package has been developed to prepare the required data for ANN training which applies a modified COBRA-EN code for sub-channel analysis and links the codes using the MATLAB software. Based on the current estimation system, five main core TH parameters are predicted, which include the average and maximum temperatures of fuel and clad as well as the minimum departure from nucleate boiling ratio (MDNBR) for each FA. To get the best conditions for the considered ANNs training, a comprehensive sensitivity study has been performed to examine the effects of variation of hidden neurons, hidden layers, transfer functions, and the learning algorithms on the training and simulation results. Performance evaluation results show that the developed ANN can be trained to estimate the core TH parameters of a typical VVER-1000 reactor quickly without loss of accuracy.

  3. Evaluation of non-microbial salivary caries activity parameters and salivary biochemical indicators in predicting dental caries

    OpenAIRE

    A. Kaur; K S Kwatra; Kamboj, P.

    2012-01-01

    Aim: The aim of the present study was the evaluation of non-microbial salivary caries activity parameters and salivary biochemical indicators in predicting dental caries. Materials and Methods: The present study was carried out on 60 children, aged 4-6 years, selected from the schools of Panchkula district, Haryana, on the basis of their caries status. Level of hydration, flow rate, pH, buffering capacity, relative viscosity, calcium, phosphorus and alkaline phosphatase levels in caries-free ...

  4. COMPARISON OF ANN WITH RSM IN PREDICTING SURFACE ROUGHNESS WITH RESPECT TO PROCESS PARAMETERS IN Nd: YAG LASER DRILLING

    OpenAIRE

    ARINDAM MAJUMDER

    2010-01-01

    Micro machining is a ready solution towards the miniaturization of component and devices. The process parameters of low power pulse Nd:YAG laser machining such as pulse rate, pulse width, speed play a major role in deciding the surface quality. Two methods, response surface methodology (RSM) and artificial neural network (ANN) were used to predict the surface roughness of Nd:YAG laser drilled mild steel specimens. The experiments were conducted based on the three factors, three levels and cen...

  5. Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images

    OpenAIRE

    Couteron, Pierre; Pelissier, Raphaël; Nicolini, Eric,; Paget, P.

    2005-01-01

    1. Predicting stand structure parameters for tropical forests from remotely sensed data has numerous important applications, such as estimating above-ground biomass and carbon stocks and providing spatial information for forest mapping and management planning, as well as detecting potential ecological determinants of plant species distributions. As an alternative to direct measurement of physical attributes of the vegetation and individual tree crown delineation, we present a powerful holisti...

  6. A predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic

    International Nuclear Information System (INIS)

    Purpose: To develop a predictive model for survival from the time of presentation in an outpatient palliative radiotherapy clinic. Methods and Materials: Sixteen factors were analyzed prospectively in 395 patients seen in a dedicated palliative radiotherapy clinic in a large tertiary cancer center using Cox's proportional hazards regression model. Results: Six prognostic factors had a statistically significant impact on survival, as follows: primary cancer site, site of metastases, Karnofsky performance score (KPS), and fatigue, appetite, and shortness of breath scores from the modified Edmonton Symptom Assessment Scale. Risk group stratification was performed (1) by assigning weights to the prognostic factors based on their levels of significance, and (2) by the number of risk factors present. The weighting method provided a Survival Prediction Score (SPS), ranging from 0 to 32. The survival probability at 3, 6, and 12 months was 83%, 70%, and 51%, respectively, for patients with SPS ≤13 (n=133); 67%, 41%, and 20% for patients with SPS 14-19 (n=129); and 36%, 18%, and 4% for patients with SPS ≥20 (n=133) (p<0.0001). Corresponding survival probabilities based on number of risk factors were as follows: 85%, 72%, and 52% (≤3 risk factors) (n=98); 68%, 47%, and 24% (4 risk factors) (n=117); and 46%, 24%, and 11% (≥5 factors) (n=180) (p<0.0001). Conclusion: Clinical prognostic factors can be used to predict prognosis among patients attending a palliative radiotherapy clinic. If validated in an independent series of patients, the model can be used to guide clinical decisions, plan supportive services, and allocate resource use

  7. 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.

  8. 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.

  9. 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. PMID:17125183

  10. House thermal model parameter estimation method for Model Predictive Control applications

    NARCIS (Netherlands)

    Leeuwen, van R.P.; Wit, de J.B.; Fink, J.; Smit, G.J.M.

    2015-01-01

    In this paper we investigate thermal network models with different model orders applied to various Dutch low-energy house types with high and low interior thermal mass and containing floor heating. Parameter estimations are performed by using data from TRNSYS simulations. The paper discusses results

  11. 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

    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...

  12. Discrete element modelling (DEM) input parameters: understanding their impact on model predictions using statistical analysis

    Science.gov (United States)

    Yan, Z.; Wilkinson, S. K.; Stitt, E. H.; Marigo, M.

    2015-09-01

    Selection or calibration of particle property input parameters is one of the key problematic aspects for the implementation of the discrete element method (DEM). In the current study, a parametric multi-level sensitivity method is employed to understand the impact of the DEM input particle properties on the bulk responses for a given simple system: discharge of particles from a flat bottom cylindrical container onto a plate. In this case study, particle properties, such as Young's modulus, friction parameters and coefficient of restitution were systematically changed in order to assess their effect on material repose angles and particle flow rate (FR). It was shown that inter-particle static friction plays a primary role in determining both final angle of repose and FR, followed by the role of inter-particle rolling friction coefficient. The particle restitution coefficient and Young's modulus were found to have insignificant impacts and were strongly cross correlated. The proposed approach provides a systematic method that can be used to show the importance of specific DEM input parameters for a given system and then potentially facilitates their selection or calibration. It is concluded that shortening the process for input parameters selection and calibration can help in the implementation of DEM.

  13. Mathcad computer applications predicting antenna parameters from antenna physical dimensions and ground characteristics

    OpenAIRE

    Gerry, Donald D.

    1993-01-01

    Approved for public release; distribution is unlimited. This report provides the documentation for a set of computer applications for the evaluation of antenna parameters. The applications are written for the Mathcad personal computer software for various antenna types listed in the thesis index. Antenna dimen Lieutenant Commander, United States Navy

  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. Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles

    International Nuclear Information System (INIS)

    The goal of this study is to realize real-time predictions of the peak power/state of power (SOP) for lithium-ion batteries in electric vehicles (EVs). To allow the proposed method to be applicable to different temperature and aging conditions, a training-free battery parameter/state estimator is presented based on an equivalent circuit model using a dual extended Kalman filter (DEKF). In this estimator, the model parameters are no longer taken as functions of factors such as SOC (state of charge), temperature, and aging; instead, all parameters will be directly estimated under the present conditions, and the impact of the temperature and aging on the battery model will be included in the parameter identification results. Then, the peak power/SOP will be calculated using the estimated results under the given limits. As an improvement to the calculation method, a combined limit of current and voltage is proposed to obtain results that are more reasonable. Additionally, novel verification experiments are designed to provide the true values of the cells' peak power under various operating conditions. The proposed methods are implemented in experiments with LiFePO4/graphite cells. The validating results demonstrate that the proposed methods have good accuracy and high adaptability. - Highlights: • A real-time peak power/SOP prediction method for lithium-ion batteries is proposed. • A training-free method based on DEKF is presented for parameter identification. • The proposed method can be applied to different temperature and aging conditions. • The calculation of peak power under the current and voltage limits is improved. • Validation experiments are designed to verify the accuracy of prediction results

  16. 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

  17. The effect of sugar-free chewing gum on plaque and clinical parameters of gingival inflammation: a systematic review

    NARCIS (Netherlands)

    R.S. Keukenmeester; D.E. Slot; M.S. Putt; G.A. van der Weijden

    2013-01-01

    Objective The aim of this study was to systematically review the current literature on the clinical effects of sugar-free chewing gum on plaque indices and parameters of gingival inflammation. Material and methods The MEDLINE-PubMed, Cochrane-CENTRAL and EMBASE databases were searched up to 20 April

  18. The effect of medicated, sugar-free chewing gum on plaque and clinical parameters of gingival inflammation: a systematic review

    NARCIS (Netherlands)

    R.S. Keukenmeester; D.E. Slot; M.S. Putt; G.A. van der Weijden

    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

  19. Microsatellite Instability Predicts Clinical Outcome in Radiation-Treated Endometrioid Endometrial Cancer

    International Nuclear Information System (INIS)

    Purpose: To elucidate whether microsatellite instability (MSI) predicts clinical outcome in radiation-treated endometrioid endometrial cancer (EEC). Methods and Materials: A consecutive series of 93 patients with EEC treated with extrafascial hysterectomy and postoperative radiotherapy was studied. The median clinical follow-up of patients was 138 months, with a maximum of 232 months. Five quasimonomorphic mononucleotide markers (BAT-25, BAT-26, NR21, NR24, and NR27) were used for MSI classification. Results: Twenty-five patients (22%) were classified as MSI. Both in the whole series and in early stages (I and II), univariate analysis showed a significant association between MSI and poorer 10-year local disease-free survival, disease-free survival, and cancer-specific survival. In multivariate analysis, MSI was excluded from the final regression model in the whole series, but in early stages MSI provided additional significant predictive information independent of traditional prognostic and predictive factors (age, stage, grade, and vascular invasion) for disease-free survival (hazard ratio [HR] 3.25, 95% confidence interval [CI] 1.01-10.49; p = 0.048) and cancer-specific survival (HR 4.20, 95% CI 1.23-14.35; p = 0.022) and was marginally significant for local disease-free survival (HR 3.54, 95% CI 0.93-13.46; p = 0.064). Conclusions: These results suggest that MSI may predict radiotherapy response in early-stage EEC.

  20. Generation and mid-IR measurement of a gas-phase to predict security parameters of aviation jet fuel

    Energy Technology Data Exchange (ETDEWEB)

    Gomez-Carracedo, M.P.; Andrade, J.M.; Calvino, M.A.; Prada, D.; Fernandez, E.; Muniategui, S. [Department of Analytical Chemistry, University of A Coruna, Campus da Zapateira s/n, E-15071, A Coruna (Spain)

    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.

  1. Predicting lepton mixing parameters including Majorana phases from Δ(6n2) flavour symmetry and generalised CP

    International Nuclear Information System (INIS)

    An important class of flavour groups that are subgroups of U(3) and that predict experimentally viable lepton mixing parameters including Majorana phases is the Δ(6n2) series. The most well-known member is Δ(24)=S4. I present results of several extensive studies of lepton mixing predictions obtained in models with a Δ(6n2) flavour group that preserve either the full Klein symmetry or a Z2 subgroup for neutrinos and can include a generalised CP symmetry. Predictions include mixing angles and Dirac CP phase generally; and if invariance under a generalised CP symmetry is included, also Majorana phases. For this, the interplay of flavour group and generalised CP symmetry has to be studied carefully. Furthermore, I present results for neutrinoless double-beta decay.

  2. 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%.

  3. 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.

  4. Soil parameters are key factors to predict metal bioavailability to snails based on chemical extractant data

    International Nuclear Information System (INIS)

    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-HClO4, EDTA, CaCl2, NH4NO3, NaNO3, 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 (r²adj = 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 (r²adj = 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: ► New approach to identify chemical methods able to predict metal bioavailability to snails. ► Bioavailability of cadmium, lead and zinc to snails was determined by assessing

  5. 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

  6. 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.

  7. A Design of Experiment approach to predict product and process parameters for a spray dried influenza vaccine.

    Science.gov (United States)

    Kanojia, Gaurav; Willems, Geert-Jan; Frijlink, Henderik W; Kersten, Gideon F A; Soema, Peter C; Amorij, Jean-Pierre

    2016-09-25

    Spray dried vaccine formulations might be an alternative to traditional lyophilized vaccines. Compared to lyophilization, spray drying is a fast and cheap process extensively used for drying biologicals. The current study provides an approach that utilizes Design of Experiments for spray drying process to stabilize whole inactivated influenza virus (WIV) vaccine. The approach included systematically screening and optimizing the spray drying process variables, determining the desired process parameters and predicting product quality parameters. The process parameters inlet air temperature, nozzle gas flow rate and feed flow rate and their effect on WIV vaccine powder characteristics such as particle size, residual moisture content (RMC) and powder yield were investigated. Vaccine powders with a broad range of physical characteristics (RMC 1.2-4.9%, particle size 2.4-8.5μm and powder yield 42-82%) were obtained. WIV showed no significant loss in antigenicity as revealed by hemagglutination test. Furthermore, descriptive models generated by DoE software could be used to determine and select (set) spray drying process parameter. This was used to generate a dried WIV powder with predefined (predicted) characteristics. Moreover, the spray dried vaccine powders retained their antigenic stability even after storage for 3 months at 60°C. The approach used here enabled the generation of a thermostable, antigenic WIV vaccine powder with desired physical characteristics that could be potentially used for pulmonary administration. PMID:27523619

  8. 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 are...... included for analysis covering a total of 626 patients with malignant melanoma treated with DC based therapy. Clinical response (CR, PR and SD) were found to be significantly correlated with the use of peptide antigens (p = 0.03), the use of any helper antigen/adjuvant (p = 0.002), and induction of antigen...

  9. 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...... subjects undergoing retinopathy screening in the county of North Jutland. The association between the presence of clinically significant macular oedema and blood-pressure, HbA1c, BMI, age, onset of diabetes, duration of diabetes, blood pressure reducing medication, lipid lowering medication, neuropathy......, and urinary albumin excretion was explored using multiple logistic regression analysis. Findings: We found no significant association between the presence of clinically significant macular oedema and any of the examined parameters in type 1 diabetic subjects. In type 2 diabetic subjects the duration...

  10. 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.

  11. On better estimating and normalizing the relationship between clinical parameters: comparing respiratory modulations in the photoplethysmogram and blood pressure signal (DPOP versus PPV).

    Science.gov (United States)

    Addison, Paul S; Wang, Rui; Uribe, Alberto A; Bergese, Sergio D

    2015-01-01

    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. PMID:25691912

  12. Future methane, hydroxyl, and their uncertainties: key climate and emission parameters for future predictions

    OpenAIRE

    Holmes, C. D.; Prather, M. J.; O.A. Søvde; Myhre, G.

    2012-01-01

    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 mo...

  13. 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; Dardenne, Pierre; 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...

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

    CERN Document Server

    King, S F

    2005-01-01

    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 $\\theta_{23}= 45^\\circ$, corrected by the quark mixing angle $\\theta_{23}^{\\mathrm{CKM}}\\approx 2.4^\\circ$, with the correction controlled by an undetermined phase in the quark sector. The solar angle is predicted by the tri-bimaximal complementarity relation: $\\theta_{12}+ \\frac{1}{\\sqrt{2}}\\frac{\\theta_{\\mathrm{C}}}{3} \\cos (\\delta - \\pi)...

  15. Future methane, hydroxyl, and their uncertainties: key climate and emission parameters for future predictions

    Directory of Open Access Journals (Sweden)

    C. D. Holmes

    2012-08-01

    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 multiple models over the period 1997–2009, adding sensitivity tests to determine key variables that drive the year-to-year variability. Across three atmospheric chemistry and transport models – UCI CTM, GEOS-Chem, and Oslo CTM3 – we find that temperature, water vapor, ozone column, biomass burning and lightning NOx are the dominant sources of interannual changes in methane lifetime. We also evaluate the model responses to forcings that have impacts on decadal time scales, such as methane feedback, and anthropogenic NOx 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.

  16. Simple radiographic parameter predicts fracturing in metastatic femoral bone lesions: results from a randomised trial

    International Nuclear Information System (INIS)

    Background and purpose: In the randomised Dutch Bone Metastasis Study on the palliative effect of a single fraction (SF) of 8 Gy versus six fractions of 4 Gy on painful bone metastases, 14 fractures occurred in 102 patients with femoral metastases. Purpose of the present study was to identify lesional risk factors for fracturing and to evaluate the influence of the treatment schedule. Material and methods: Pretreatment radiographs of femoral metastases were collected. Three observers separately measured the lesions and scored radiographic characteristics. Results: Ten fractures occurred after median 7 weeks in 44 SF patients (23%) and four after median 20 weeks in 58 multiple fraction patients (7%) (UV, P=0.02). In 110 femoral metastases, an axial cortical involvement >30 mm significantly predicted fracturing (MV, P=0.02). Twelve out of 14 fractured lesions and 40 out of 96 non-fractured metastases had an axial cortical involvement >30 mm (negative predictive value, 97%). When correcting for the axial cortical involvement, the treatment schedule was not predictive anymore (MV, P=0.07). Conclusions: Fracturing of the femur mostly depended on the amount of axial cortical involvement of the metastasis. We recommend to treat femoral metastases with an axial cortical involvement ≤30 mm with an SF of 8 Gy for relief of pain. If the axial cortical involvement is >30 mm, prophylactic surgery should be performed to minimize the risk of pathological fracturing or, if the patient's condition is limited, irradiation to a higher total dose

  17. Local control after radiosurgery for brain metastases: predictive factors and implications for clinical decision

    International Nuclear Information System (INIS)

    To evaluate the local control of brain metastases (BM) in patients treated with stereotactic radiosurgery (SRS), correlate the outcome with treatment parameters and lesion characteristics, and define its implications for clinical decisions. Between 2007 and 2012, 305 BM in 141 consecutive patients were treated with SRS. After exclusions, 216 BM in 100 patients were analyzed. Doses were grouped as follows: ≤15 Gy, 16–20 Gy, and ≥21 Gy. Sizes were classified as ≤10 mm and >10 mm. Local control (LC) and overall survival (OS) were estimated using the Kaplan-Meier method. Log-rank statistics were used to identify the prognostic factors affecting LC and OS. For multivariate analyses, a Cox proportional model was applied including all potentially significant variables reached on univariate analyses. Median age was 54 years (18–80). Median radiological follow-up of the lesions was 7 months (1–66). Median LC and the LC at 1 year were 22.3 months and 69.7%, respectively. On univariate analysis, tumor size, SRS dose, and previous whole brain irradiation (WBRT) were significant factors for LC. Patients with lesions >10 and ≤10 mm had an LC at 1 year of 58.6% and 79.1%, respectively (p = 0.008). In lesions receiving ≤15 Gy, 16–20 Gy, and ≥21 Gy, the 1-year LC rates were 39.6%, 71.7%, and 92.3%, respectively (p < 0.001). When WBRT was done previously, LC at 1 year was 57.9% compared with 78.4% for those who did not undergo WBRT (p = 0.004). On multivariate analysis, dose remained the single most powerful prognostic factor for LC. Median OS for all patients was 17 months, with no difference among the groups. Dose is the most important predictive factor for LC of BM. Doses below 16 Gy correlated with poor LC. The SRS dose as salvage treatment after previous WBRT should not be reduced unless there is a pressing reason to do so

  18. Prediction of radiation inactivation of presonicated a-amylase in terms of kinetic parameters

    International Nuclear Information System (INIS)

    Full text: In-vitro radiation inactivation of enzyme amylase denies display of optimum enzyme function owing to alterations in active site. Certain extent of enzyme activity seems to be protected in case of radiation inactivated enzyme, prior-exposed to ultrasonic frequencies. The present investigation, exploring trends of changes in kinetic parameters and its dependence on ultrasonic frequencies and gamma doses, will be discussed to highlight the functional status of active site under situation

  19. 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

    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...... by ones and zeroes only. These results illustrate the application of multivariate analysis as an effective strategy for improving the quality of frozen fish products. (C) 1998 Society of Chemical Industry...

  20. Predicting Global Solar Radiation Using an Artificial Neural Network Single-Parameter Model

    Directory of Open Access Journals (Sweden)

    Karoro Angela

    2011-01-01

    Full Text Available We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19°N, a longitude of 32.34°E, and an altitude of 1200 m above sea level. The five-year data was split into two parts in 2003–2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m2 and a root mean square error of 0.521 MJ/m2. The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours.

  1. 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

  2. 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

  3. Effect of oral lactulose on clinical and immunohistochemical parameters in patients with inflammatory bowel disease: a pilot study

    Directory of Open Access Journals (Sweden)

    Manns Michael P

    2007-09-01

    Full Text Available Abstract Background The prebiotic potential of lactulose is well established and preclinical studies demonstrated a protective effect of lactulose in murine models of colitis. The aim of the present study was to investigate the clinical and histological efficacy of lactulose in patients with inflammatory bowel disease (IBD, for which probiotic therapy yielded promising results. Methods Patients were treated with standard medication alone or combined with 10 g lactulose daily as adjuvant therapy for 4 months. Clinical efficacy of treatment was assessed using clinical activity indices, a quality of life index (IBDQ, endoscopic scores, defecation frequency and monitoring corticosteroid medication. Orsomucoid, alpha1-antitrypsin and other laboratory parameters were determined. In addition, in some participants colonic biopsies were analyzed with haematoxylin-eosin staining or with antibodies against HLA-DR, CD68, IgA and CD3, and evaluated systematically. All measurements were performed both at enrolment and at the end of the trial. Results 14 patients presenting ulcerative colitis (UC and 17 patients presenting Crohn's disease (CD, most of them in a clinically active state, were enrolled in this pilot study. After 4 month no significant improvement of clinical activity index, endoscopic score or immunohistochemical parameters was observed in CD or UC patients receiving lactulose in comparison to the control group. However, significant improvement of quality of life was observed in UC patients receiving lactulose compared to the control group (p = 0.04. Conclusion The findings of the present pilot study indicate that oral lactulose has no beneficial effects in IBD patients in particular with regard to clinical activity, endoscopic score or immunohistochemical parameters. The importance of the beneficial effect of lactulose in UC patients regarding the quality of life needs further evaluation in larger controlled clinical trials. Trial registration

  4. Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

    Directory of Open Access Journals (Sweden)

    Pereira J.C.R.

    2004-01-01

    Full Text Available The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA and by fuzzy max-min compositions (fuzzy, and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

  5. 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. PMID:27261565

  6. 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.

  7. Search for morphological parameters influential for prediction of the mechanical characteristics of an austeno-ferritic duplex stainless steel

    International Nuclear Information System (INIS)

    Duplex stainless steels are commonly used (among others in nuclear industry) for their good properties. However these steels may 'age' in service condition at high temperatures. As their mechanical properties (Charpy impact toughness, resistance to ductile tearing) are often very scattered and tend to decrease after ageing, it has become essential to predict them with high precision. For this, we propose to explain a part of the scattering of the mechanical properties with measurable parameters in relation with the particularly complicated two-phase morphology. The two-phase and bi-percolated morphology of the ferrite and austenite phases is first characterised from the observation of 2D images and from the reconstitution of a 3D image. At the same time we precise the genesis of the formation's mechanisms of the structure (germination and growth of the austenitic phase in the solidified ferri tic one) in relation with the literature. The morphological characteristics so observed corresponding with classical notions of mathematical morphology, - size, covariance, connexity -, we use morphological operators to measure morphological variables by image analysis. We establish then a link between toughness and a parameter measuring fineness of the morphology. The lack of data for very aged steels prevent us from proposing a model of toughness which could take this parameter into consideration at these ageing states, for which it is properly the more crucial to obtain specially precise predictions. A mathematical mo del of the 3D structure of the steel is finally proposed. We choose an homogeneous Markov chain of 3D spatial processes, whose evolution in time mimes the solidification. The morphology of the microstructure is so summarised with 8 parameters. This model is liable to be coupled with a model of toughness, for which it would so enlarge the possibilities of prediction. It could also be used to simulate subsequently the damage and the rupture of two

  8. 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

  9. 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.

  10. Acute pyelonephritis: role of enhanced CT scan in the prediction of clinical outcome

    International Nuclear Information System (INIS)

    To correlate the CT findings of acute pyelonephritis with its outcome and with clinical data. Thirty five contrast enhanced CT scans in patients diagnosed as suffering from acute pyelonephritis were retrospectively analyzed. Findings based on the morphology of perfusion defect in the renal parenchyma were classified as normal, focal wedge, multifocal wedge, focal mass, or mixed form composed of wedge and mass. These findings were correlated with clinical data such as degree of fever, leukocytosis, the period after antibiotic treatment during which fever was reduced, and the presence of pyuria in each group Analysis was then performed. Perfusion defects were seen in 23 of 35 cases, and their morphology was classified as follow; focal wedge (n=2), multifocal wedge (n=8), focal mass (n=4), and mixed form (n=9). Twelve cases (34.3%) showed no perfusion defect. The duration of fever was significantly prolonged in patients with focal mass form (p < .05). There was no significant correlation between the morphology of perfusion defect, bilaterality, and other clinical factors. The present study shows that the clinical course of the focal mass form of perfusion defect, as seen on CT, is different from that of other types. CT could be effective in predicting clinical progress and the outcome of treatment in cases of acute pyelonephritis

  11. Acute pyelonephritis: role of enhanced CT scan in the prediction of clinical outcome

    Energy Technology Data Exchange (ETDEWEB)

    Jo, Byung June; Kim, Ki Whang; Yu, Jeong Sik; Kim, Jai Keun; Yoon, Sang Wook; Ha, Sung Kyu; Park, Chong Hoon [Yonsei Univ. College of Medicine, Seoul (Korea, Republic of)

    1997-04-01

    To correlate the CT findings of acute pyelonephritis with its outcome and with clinical data. Thirty five contrast enhanced CT scans in patients diagnosed as suffering from acute pyelonephritis were retrospectively analyzed. Findings based on the morphology of perfusion defect in the renal parenchyma were classified as normal, focal wedge, multifocal wedge, focal mass, or mixed form composed of wedge and mass. These findings were correlated with clinical data such as degree of fever, leukocytosis, the period after antibiotic treatment during which fever was reduced, and the presence of pyuria in each group Analysis was then performed. Perfusion defects were seen in 23 of 35 cases, and their morphology was classified as follow; focal wedge (n=2), multifocal wedge (n=8), focal mass (n=4), and mixed form (n=9). Twelve cases (34.3%) showed no perfusion defect. The duration of fever was significantly prolonged in patients with focal mass form (p < .05). There was no significant correlation between the morphology of perfusion defect, bilaterality, and other clinical factors. The present study shows that the clinical course of the focal mass form of perfusion defect, as seen on CT, is different from that of other types. CT could be effective in predicting clinical progress and the outcome of treatment in cases of acute pyelonephritis.

  12. 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

  13. 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).

  14. 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, but...

  15. Seasonal dependence of the "forecast parameter" based on the EIA characteristics for the prediction of Equatorial Spread F (ESF

    Directory of Open Access Journals (Sweden)

    S. V. Thampi

    2008-06-01

    Full Text Available In an earlier study, Thampi et al. (2006 have shown that the strength and asymmetry of Equatorial Ionization Anomaly (EIA, obtained well ahead of the onset time of Equatorial Spread F (ESF have a definite role on the subsequent ESF activity, and a new "forecast parameter" has been identified for the prediction of ESF. This paper presents the observations of EIA strength and asymmetry from the Indian longitudes during the period from August 2005–March 2007. These observations are made using the line of sight Total Electron Content (TEC measured by a ground-based beacon receiver located at Trivandrum (8.5° N, 77° E, 0.5° N dip lat in India. It is seen that the seasonal variability of EIA strength and asymmetry are manifested in the latitudinal gradients obtained using the relative TEC measurements. As a consequence, the "forecast parameter" also displays a definite seasonal pattern. The seasonal variability of the EIA strength and asymmetry, and the "forecast parameter" are discussed in the present paper and a critical value for has been identified for each month/season. The likely "skill factor" of the new parameter is assessed using the data for a total of 122 days, and it is seen that when the estimated value of the "forecast parameter" exceeds the critical value, the ESF is seen to occur on more than 95% of cases.

  16. TU-C-12A-09: Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemo-Radiotherapy Using Quantitative PET/CT Features, Clinical Parameters and Demographics

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, H; Chen, W; Kligerman, S; D’Souza, W; Suntharalingam, M; Lu, W [University of Maryland School of Medicine, Baltimore, MD (United States); Tan, S [Huazhong University of Science and Technology, Wuhan (China); Kim, G [Duke University, High Point, NC (United States)

    2014-06-15

    Purpose: To develop predictive models using quantitative PET/CT features for the evaluation of tumor response to neoadjuvant chemo-radiotherapy (CRT) in patients with locally advanced esophageal cancer. Methods: This study included 20 patients who underwent tri-modality therapy (CRT + surgery) and had {sup 18}F-FDG PET/CT scans before initiation of CRT and 4-6 weeks after completion of CRT but prior to surgery. Four groups of tumor features were examined: (1) conventional PET/CT response measures (SUVmax, tumor diameter, etc.); (2) clinical parameters (TNM stage, histology, etc.) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, using cross-validations to avoid model over-fitting. Prediction accuracy was assessed via area under the receiver operating characteristic curve (AUC), and precision was evaluated via confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). Using spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications), significantly better than using conventional PET/CT measures or clinical parameters and demographics alone. For groups with a large number of tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than the LR model. Conclusion: The SVM model using all features

  17. Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal {sup 18}F-FDG PET Features, Clinical Parameters, and Demographics

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Hao [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland (United States); Tan, Shan [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland (United States); Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan (China); Chen, Wengen; Kligerman, Seth [Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland (United States); Kim, Grace; D' Souza, Warren D.; Suntharalingam, Mohan [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland (United States); Lu, Wei, E-mail: wlu@umm.edu [Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland (United States)

    2014-01-01

    Purpose: To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer. Methods and Materials: This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent {sup 18}F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]{sub max}, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher

  18. Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal 18F-FDG PET Features, Clinical Parameters, and Demographics

    International Nuclear Information System (INIS)

    Purpose: To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer. Methods and Materials: This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]max, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than

  19. TU-C-12A-09: Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemo-Radiotherapy Using Quantitative PET/CT Features, Clinical Parameters and Demographics

    International Nuclear Information System (INIS)

    Purpose: To develop predictive models using quantitative PET/CT features for the evaluation of tumor response to neoadjuvant chemo-radiotherapy (CRT) in patients with locally advanced esophageal cancer. Methods: This study included 20 patients who underwent tri-modality therapy (CRT + surgery) and had 18F-FDG PET/CT scans before initiation of CRT and 4-6 weeks after completion of CRT but prior to surgery. Four groups of tumor features were examined: (1) conventional PET/CT response measures (SUVmax, tumor diameter, etc.); (2) clinical parameters (TNM stage, histology, etc.) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, using cross-validations to avoid model over-fitting. Prediction accuracy was assessed via area under the receiver operating characteristic curve (AUC), and precision was evaluated via confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). Using spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications), significantly better than using conventional PET/CT measures or clinical parameters and demographics alone. For groups with a large number of tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than the LR model. Conclusion: The SVM model using all features including

  20. Predicting the clinical effect of a short acting bronchodilator in individual patients using artificial neural networks.

    Science.gov (United States)

    de Matas, Marcel; Shao, Qun; Biddiscombe, Martyn F; Meah, Sally; Chrystyn, Henry; Usmani, Omar S

    2010-12-23

    Artificial neural networks were used in this study to model the relationships between in vitro data, subject characteristics and in vivo outcomes from N=18 mild-moderate asthmatics receiving monodisperse salbutamol sulphate aerosols of 1.5, 3 and 6 μm mass median aerodynamic diameter in a cumulative dosing schedule of 10, 20, 40 and 100 μg. Input variables to the model were aerodynamic particle size (APS), body surface area (BSA), age, pre-treatment forced expiratory volume in one-second (FEV(1)), forced vital capacity, cumulative emitted drug dose and bronchodilator reversibility to a standard salbutamol sulphate 200 μg dose MDI (REV(%)). These factors were used by the model to predict the bronchodilator response at 10 (T10) and 20 (T20) min after receiving each of the 4 doses for each of the 3 different particle sizes. Predictability was assessed using data from selected patients in this study, which were set aside and not used in model generation. Models reliably predicted ΔFEV(1)(%) in individual subjects with non-linear determinants (R(2)) of ≥ 0.8. The average error between predicted and observed ΔFEV(1)(%) for individual subjects was <4% across the cumulative dosing regimen. Increases in APS and drug dose gave improved ΔFEV(1)(%). Models also showed trends towards improved responses in younger patients and those having greater REV(%), whilst BSA was also shown to influence clinical effect. These data show that APS can be used to discriminate predictably between aerosols giving different bronchodilator responses across a cumulative dosing schedule, whilst patient characteristics can be used to reliably estimate clinical response in individual subjects. PMID:20932900

  1. Clinical utility of polymorphisms in one-carbon metabolism for breast cancer risk prediction

    Directory of Open Access Journals (Sweden)

    Shaik Mohammad Naushad

    2011-01-01

    Full Text Available This study addresses the issues in translating the laboratory derived data obtained during discovery phase of research to a clinical setting using a breast cancer model. Laboratory-based risk assessment indi-cated that a family history of breast cancer, reduced folate carrier 1 (RFC1 G80A, thymidylate synthase (TYMS 5’-UTR 28bp tandem repeat, methylene tetrahydrofolate reductase (MTHFR C677T and catecholamine-O-methyl transferase (COMT genetic polymorphisms in one-carbon metabolic pathway increase the risk for breast cancer. Glutamate carboxypeptidase II (GCPII C1561T and cytosolic serine hydroxymethyl transferase (cSHMT C1420T polymorphisms were found to decrease breast cancer risk. In order to test the clinical validity of this information in the risk prediction of breast cancer, data was stratified based on number of protective alleles into four categories and in each category sensitivity and 1-specificity values were obtained based on the distribution of number of risk alleles in cases and controls. Receiver operating characteristic (ROC curves were plotted and the area under ROC curve (C was used as a measure of discriminatory ability between cases and controls. In subjects without any protective allele, aberrations in one-carbon metabolism showed perfect prediction (C=0.93 while the predictability was lost in subjects with one protective allele (C=0.60. However, predictability increased steadily with increasing number of protective alleles (C=0.63 for 2 protective alleles and C=0.71 for 3 protective alleles. The cut-off point for discrimination was >4 alleles in all predictable combinations. Models of this kind can serve as valuable tools in translational re-search, especially in identifying high-risk individuals and reducing the disease risk either by life style modification or by medical intervention.

  2. 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. PMID:25363401

  3. The clinical factors′ prediction of increased intradialytic qt dispersion on the electrocardiograms of chronic hemodialysis patients

    Directory of Open Access Journals (Sweden)

    Dina Oktavia

    2013-01-01

    Full Text Available Ventricular arrhythmias and sudden death are common in patients on maintenance hemodialysis (HD. The increase in QT dispersion (QTd on the electrocardiogram (ECG reflects increased tendency for ventricular repolarization that predisposes to arrhythmias. The purpose of the study was to identify the clinical factors that may predict the increased intradialytic QTd and to assess differences in QTd before and after HD. Each of 61 chronic HD patients underwent 12-lead ECG and blood pressure (BP measurement before and every 1 h during a single HD session. The QT intervals were corrected for heart rate using Bazett′s formula. Intradialytic QTd increased in 30 (49% patients. There was no correlation between the increased QTd and the clinical factors including hypertension, pulse pressure, intradialytic hypotension, left ventricular hypertrophy, old myocardial infarct, diabetes mellitus, and nutritional status. The means of QT interval and QTd increased after HD session (from 382 ± 29 to 444 ± 26 ms, P <0.05; and from 74 ± 21 to 114 ± 53 ms, respectively, P <0.05. We conclude that the increased intradialytic QTd could not be predicted by any of the clinical factors evaluated in this study. There was significant difference in the means of QTd before and after HD session.

  4. A Panel of Cancer Testis Antigens and Clinical Risk Factors to Predict Metastasis in Colorectal Cancer

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    Ramyar Molania

    2014-01-01

    Full Text Available Colorectal cancer (CRC is the third common carcinoma with a high rate of mortality worldwide and several studies have investigated some molecular and clinicopathological markers for diagnosis and prognosis of its malignant phenotypes. The aim of this study is to evaluate expression frequency of PAGE4, SCP-1, and SPANXA/D cancer testis antigen (CTA genes as well as some clinical risk markers to predict liver metastasis of colorectal cancer patients. The expression frequency of PAGE4, SCP-1, and SPANXA/D cancer/testis antigen (CTA genes was obtained using reverse transcription polymerase chain reaction (RT-PCR assay in 90 colorectal tumor samples including both negative and positive liver metastasis tumors. Statistical analysis was performed to assess the association of three studied genes and clinical risk factors with CRC liver metastasis. The frequency of PAGE4 and SCP-1 genes expression was significantly higher in the primary tumours with liver metastasis when statistically compared with primary tumors with no liver metastasis (P<0.05. Among all clinical risk factors studied, the lymph node metastasis and the depth of invasion were statistically correlated with liver metastasis of CRC patients. In addition, using multiple logistic regression, we constructed a model based on PAGE4 and lymph node metastasis to predict liver metastasis of CRC.

  5. Quantitative prediction and clinical evaluation of an unexplored herb-drug interaction mechanism in healthy volunteers.

    Science.gov (United States)

    Gufford, B T; Barr, J T; González-Pérez, V; Layton, M E; White, J R; Oberlies, N H; Paine, M F

    2015-12-01

    Quantitative prediction of herb-drug interaction risk remains challenging. A quantitative framework to assess a potential interaction was used to evaluate a mechanism not previously tested in humans. The semipurified milk thistle product, silibinin, was selected as an exemplar herbal product inhibitor of raloxifene intestinal glucuronidation. Physiologically based pharmacokinetic (PBPK) model simulations of the silibinin-raloxifene interaction predicted up to 30% increases in raloxifene area under the curve (AUC0-inf) and maximal concentration (Cmax). Model-informed clinical evaluation of the silibinin-raloxifene interaction indicated minimal clinical interaction liability, with observed geometric mean raloxifene AUC0-inf and Cmax ratios lying within the predefined no effect range (0.75-1.33). Further refinement of PBPK modeling and simulation approaches will enhance confidence in predictions and facilitate generalizability to additional herb-drug combinations. This quantitative framework can be used to develop guidances to evaluate potential herb-drug interactions prospectively, providing evidenced-based information about the risk or safety of these interactions. PMID:26904384

  6. Early seizures in patients with acute stroke: Frequency, predictive factors, and effect on clinical outcome

    Directory of Open Access Journals (Sweden)

    Andrea Alberti

    2008-06-01

    Full Text Available Andrea Alberti, Maurizio Paciaroni, Valeria Caso, Michele Venti, Francesco Palmerini, Giancarlo AgnelliStroke Unit and Division of Internal and Cardiovascular Medicine, University of Perugia, Perugia, ItalyBackground: Early seizure (ES may complicate the clinical course of patients with acute stroke. The aim of this study was to assess the rate of and the predictive factors for ES as well the effects of ES on the clinical outcome at hospital discharge in patients with first-ever stroke.Patients and methods: A total of 638 consecutive patients with first-ever stroke (543 ischemic, 95 hemorrhagic, admitted to our Stroke Unit, were included in this prospective study. ES were defined as seizures occurring within 7 days from acute stroke. Patients with history of epilepsy were excluded.Results: Thirty-one patients (4.8% had ES. Seizures were significantly more common in patients with cortical involvement, severe and large stroke, and in patient with cortical hemorrhagic transformation of ischemic stroke. ES was not associated with an increase in adverse outcome (mortality and disability. After multivariate analysis, hemorrhagic transformation resulted as an independent predictive factor for ES (OR = 6.5; 95% CI: 1.95–22.61; p = 0.003.Conclusion: ES occur in about 5% of patients with acute stroke. In these patients hemorrhagic transformation is a predictive factor for ES. ES does not seem to be associated with an adverse outcome at hospital discharge after acute stroke.Keywords: seizures, stroke, cortical involvement, hemorrhagic transformation

  7. Early seizures in patients with acute stroke: Frequency, predictive factors, and effect on clinical outcome

    Science.gov (United States)

    Alberti, Andrea; Paciaroni, Maurizio; Caso, Valeria; Venti, Michele; Palmerini, Francesco; Agnelli, Giancarlo

    2008-01-01

    Background Early seizure (ES) may complicate the clinical course of patients with acute stroke. The aim of this study was to assess the rate of and the predictive factors for ES as well the effects of ES on the clinical outcome at hospital discharge in patients with first-ever stroke. Patients and methods A total of 638 consecutive patients with first-ever stroke (543 ischemic, 95 hemorrhagic), admitted to our Stroke Unit, were included in this prospective study. ES were defined as seizures occurring within 7 days from acute stroke. Patients with history of epilepsy were excluded. Results Thirty-one patients (4.8%) had ES. Seizures were significantly more common in patients with cortical involvement, severe and large stroke, and in patient with cortical hemorrhagic transformation of ischemic stroke. ES was not associated with an increase in adverse outcome (mortality and disability). After multivariate analysis, hemorrhagic transformation resulted as an independent predictive factor for ES (OR = 6.5; 95% CI: 1.95–22.61; p = 0.003). Conclusion ES occur in about 5% of patients with acute stroke. In these patients hemorrhagic transformation is a predictive factor for ES. ES does not seem to be associated with an adverse outcome at hospital discharge after acute stroke. PMID:18827922

  8. Predicting clinically unrecognized coronary artery disease: use of two- dimensional echocardiography

    Directory of Open Access Journals (Sweden)

    Nagueh Sherif F

    2009-03-01

    Full Text Available Abstract Background 2-D Echo is often performed in patients without history of coronary artery disease (CAD. We sought to determine echo features predictive of CAD. Methods 2-D Echo of 328 patients without known CAD performed within one year prior to stress myocardial SPECT and angiography were reviewed. Echo features examined were left ventricular and atrial enlargement, LV hypertrophy, wall motion abnormality (WMA, LV ejection fraction (EF 15% LV perfusion defect or multivessel distribution. Severe coronary artery stenosis (CAS was defined as left main, 3 VD or 2VD involving proximal LAD. Results The mean age was 62 ± 13 years, 59% men, 29% diabetic (DM and 148 (45% had > 2 risk factors. Pharmacologic stress was performed in 109 patients (33%. MPA was present in 200 pts (60% of which, 137 were high risk. CAS was present in 166 pts (51%, 75 were severe. Of 87 patients with WMA, 83% had MPA and 78% had CAS. Multivariate analysis identified age >65, male, inability to exercise, DM, WMA, MAC and AS as independent predictors of MPA and CAS. Independent predictors of high risk MPA and severe CAS were age, DM, inability to exercise and WMA. 2-D echo findings offered incremental value over clinical information in predicting CAD by angiography. (Chi square: 360 vs. 320 p = 0.02. Conclusion 2-D Echo was valuable in predicting presence of physiological and anatomical CAD in addition to clinical information.

  9. Biomarkers for predicting clinical response to immunosuppressive therapy in aplastic anemia.

    Science.gov (United States)

    Narita, Atsushi; Kojima, Seiji

    2016-08-01

    The decision to select hematopoietic stem cell transplantation (HSCT) or immunosuppressive therapy (IST) as initial therapy in acquired aplastic anemia (AA) is currently based on patient age and the availability of a human leukocyte antigen (HLA)-matched donor. Although IST is a promising treatment option, the ability to predict its long-term outcomes remains poor due to refractoriness, relapses, and the risk of clonal evolution. Several predictive biomarkers for response to IST have been posited, including age, gender, pre-treatment blood cell counts, cytokines, gene mutations, paroxysmal nocturnal hemoglobinuria (PNH), and telomere length (TL). While previous studies have provided substantial biological insights into the utility of IST, the prognostic power of the reported biomarkers is currently insufficient to contribute to clinical decision making. Recently, a large retrospective analysis proposed the combination of minor PNH clones and TL as an efficient predictor of IST response. Identification of a reliable predictor would provide a useful tool for determining the most appropriate treatment choice for AA patients, including up-front HSCT from HLA-matched unrelated donor. The present review summarizes studies evaluating the utility of biomarkers in predicting the clinical response to IST of patients with AA, and provides a baseline for prospective studies aimed at validating previously reported biomarkers. PMID:27091471

  10. 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

  11. Neural network prediction of hardness in HAZ of temper bead welding using the proposed thermal cycle tempering parameter (TCTP)

    International Nuclear Information System (INIS)

    A new thermal cycle tempering parameter (TCTP) to characterize the tempering effect during multi-pass thermal cycles has been proposed by extending the Larson-Miller parameter (LMP) to non-isothermal heat treatment. Experimental results revealed that the hardness in synthetic HAZ of low-alloy steel subjected to multi-pass tempering thermal cycles has a good linear relationship with the TCTP. The new hardness prediction system was constructed by using a neural network taking into consideration of the tempering effect during multi-pass welding, estimated by using the TCTP. Based on the thermal cycles numerically obtained by FEM and the experimentally obtained hardness database, the hardness distribution in HAZ of low-alloy steel welded with temper bead welding method was calculated. The predicted hardness was in good accordance with the experimental results. It follows that our new prediction system is effective for estimating the tempering effect in HAZ during multi-pass welding and hence enables us to assess the effectiveness of temper bead welding. (author)

  12. Predicting PTSD using the New York Risk Score with genotype data: potential clinical and research opportunities

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    Boscarino JA

    2013-04-01

    Full Text Available Joseph A Boscarino,1,2 H Lester Kirchner,3,4 Stuart N Hoffman,5 Porat M Erlich1,4 1Center for Health Research, Geisinger Clinic, Danville, 2Department of Psychiatry, Temple University School of Medicine, Philadelphia, 3Division of Medicine, Geisinger Clinic, Danville, 4Department of Medicine, Temple University School of Medicine, Philadelphia, 5Department of Neurology, Geisinger Clinic, Danville, PA, USA Background: We previously developed a post-traumatic stress disorder (PTSD screening instrument, ie, the New York PTSD Risk Score (NYPRS, that was effective in predicting PTSD. In the present study, we assessed a version of this risk score that also included genetic information. Methods: Utilizing diagnostic testing methods, we hierarchically examined different prediction variables identified in previous NYPRS research, including genetic risk-allele information, to assess lifetime and current PTSD status among a population of trauma-exposed adults. Results: We found that, in predicting lifetime PTSD, the area under the receiver operating characteristic curve (AUC for the Primary Care PTSD Screen alone was 0.865. When we added psychosocial predictors from the original NYPRS to the model, including depression, sleep disturbance, and a measure of health care access, the AUC increased to 0.902, which was a significant improvement (P = 0.0021. When genetic information was added in the form of a count of PTSD risk alleles located within FKBP, COMT, CHRNA5, and CRHR1 genetic loci (coded 0–6, the AUC increased to 0.920, which was also a significant improvement (P = 0.0178. The results for current PTSD were similar. In the final model for current PTSD with the psychosocial risk factors included, genotype resulted in a prediction weight of 17 for each risk allele present, indicating that a person with six risk alleles or more would receive a PTSD risk score of 17 × 6 = 102, the highest risk score for any of the predictors studied. Conclusion: Genetic

  13. Evaluation of several FDG PET parameters for prediction of soft tissue tumour grade at primary diagnosis and recurrence

    International Nuclear Information System (INIS)

    This study evaluates the diagnostic accuracy of SUV-based parameters derived from [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 SUVmax, SUVpeak, SUVmax/SUVliver and SUVpeak/SUVliver showed good correlation with tumour grade. SUVpeak (area under the receiver-operating-characteristic, AUC-ROC: 0.82) and SUVpeak/SUVliver (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 SUVpeak/SUVliver 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 SUVpeak 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.)

  14. Application of pseudopotential theory for the prediction of superconducting state parameters of binary alloys

    International Nuclear Information System (INIS)

    The BCS-Eliasberg-McMillan formulation of metallic superconductors has been extended to the binary alloy of metallic superconductors and has been applied for the prediction of the transition temperature Tc, isotope effect exponent α and interaction strength NoV of In sub(l-c)Bc ie In-based alloys of seven superconductors (B), at different values of c. The results obtained show a good agreement with the experimental data available in literature. The various forms of the prefactor in McMillan's Tc formula, available in literature have also been examined for the In-based alloys of three superconductors. It is concluded that the prefactor θD/1.45 yields the best results. (author). 28 refs., 3 tabs

  15. 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.

  16. Clinical prediction rules in Staphylococcus aureus bacteremia demonstrate the usefulness of reporting likelihood ratios in infectious diseases.

    Science.gov (United States)

    Bai, A D; Showler, A; Burry, L; Steinberg, M; Tomlinson, G A; Bell, C M; Morris, A M

    2016-09-01

    Infectious diseases specialists often use diagnostic tests to assess the probability of a disease based on knowledge of the diagnostic properties. It has become standard for published studies on diagnostic tests to report sensitivity, specificity and predictive values. Likelihood ratios are often omitted. We compared published clinical prediction rules in Staphylococcus aureus bacteremia to illustrate the importance of likelihood ratios. We performed a narrative review comparing published clinical prediction rules used for excluding endocarditis in S. aureus bacteremia. Of nine published clinical prediction rules, only three studies reported likelihood ratios. Many studies concluded that the clinical prediction rule could safely exclude endocarditis based on high sensitivity and high negative predictive value. Of the studies with similar high sensitivity and high negative predictive value, calculated negative likelihood ratios were able to differentiate and identify the best clinical prediction rule for excluding endocarditis. Compared to sensitivity, specificity and predictive values, likelihood ratios can be more directly used to interpret diagnostic test results to assist in ruling in or ruling out a disease. Therefore, a new standard should be set to include likelihood ratios in reporting of diagnostic tests in infectious diseases research. PMID:27357965

  17. Fatigue life prediction for wind turbines: A case study on loading spectra and parameter sensitivity

    Science.gov (United States)

    Sutherland, H. J.; Veers, P. S.; Ashwill, T. D.

    Wind turbines are fatigue-critical machines used to produce electrical energy from the wind. These rotating machines are subjected to environmental loadings that are highly irregular in nature. Historical examples of fatigue problems in both research and commercial wind turbine development are presented. Some example data on wind turbine environments, loadings and material properties are also shown. Before a description of how the authors have chosen to attack the cumulative damage assessment, questions are presented for the reader's reflection. The solution technique used by the authors is then presented, followed by a case study applying the procedures to an actual wind turbine blade joint. The wind turbine is the 34-meter diameter vertical axis wind turbine (VAWT) erected by Sandia National Laboratories near Bushland, Texas. The case study examines parameter sensitivities for realistic uncertainties in inputs defining the turbine environment, stress response and material properties. The fatigue lifetimes are calculated using a fatigue analysis program, called LIFE2, which was developed at Sandia. The LIFE2 code, described in some detail in an appendix, is a PC-based, menu-driven package that leads the user through the steps required to characterize the loading and material properties, then uses Miner's rule or a linear crack propagation rule to numerically calculate the time to failure. Only S-n based cumulative damage applications are illustrated here. The LIFE2 code is available to educational institutions for use as a case study in describing complicated loading histories and for use by students in examining, hands on, parameter sensitivity of fatigue life analysis.

  18. Effects Of Music Therapy On Clinical And Biochemical Parameters Of Metabolic Syndrome

    Directory of Open Access Journals (Sweden)

    Rajnee

    2011-12-01

    Full Text Available Background: Music therapy is a new approach being used for the management of metabolic abnormalities and stress related illness.Objective: To study the effect of Music therapy on various clinical and biochemical parameters of Metabolic Syndrome.Methods: This cross sectional study was carried out on 100 patients of metabolic syndrome selected randomly. These patients were divided into two equal groups after age, sex adjustment. In control group (group I 50 patients underwent the conventional treatment. 50 patients in study group were treated with supervised music protocol along with conventional treatment. The Body Mass Index, ;Waist-Hip ratio, Blood pressure, Fasting blood sugar were monitored weekly while HbA1c and lipid profile were determined at the baseline and after three months of exposure to music therapy. Statistical analysis was performed by employing student t- test.Results: In the study group there was a significant decrease in BMI (27.18±5.02 to 25.44±3.49 kg/m2, p<0.05, waist hip ratio (0.95±0.05 to 0.93±0.05 cm, p<0.05, Fasting blood sugar (196.00±47.80mg/ dl to152.00±16.19mg/dl , p<0.001, HbA1c (8.41±1.31% to 7.08±0.78 % p<0.001, Systolic Blood Pressure (151.00±12.10 to 136±9.04 mmHg p<0.001, Diastolic Blood Pressure (94±4.80 to 86.44±3.16 mmHg, p<0.01, Mean serum cholesterol (257.80±18.92 to 229.12±17.82mg/dl, p<0.001 and triglycerides (180.86±14.04 to 136.50±8.92mg/dl, p<0.001, LDL (167.97±14.40 to 140.20±15.41mg/dl, p<0.001, and VLDL (33.60±2.88 to 28.04±3.08mg/dl, p<0.001 and increase in HDL (33.32±3.38 to 39.71±3.41mg/dl, p<0.001, when compared with those of control group not receiving the music therapy along with the conventional treatment.Conclusion: The promising outcomes of Music therapy showed that it may be considered as a useful adjunct to conventional treatment in management of the metabolic syndrome. This study advocates music therapy to establish it from a general well being concepts to a

  19. Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences

    Directory of Open Access Journals (Sweden)

    Alejandro Pironti

    2014-11-01

    Full Text Available Introduction: Rules-based HIV-1 drug-resistance interpretation (DRI systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1 the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2 the assessment of the benefit of taking all available amino-acid positions into account for DRI. Materials and Methods: A dataset containing 34,934 therapy-naïve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull, the second one only considers IAS drug-resistance positions (DEonlyIAS, and the third one disregards IAS drug-resistance positions (DEnoIAS. For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR predictions. With each method, a genetic susceptibility score (GSS was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. Results: A comparison of the therapy-success prediction performances among the different interpretation systems for test

  20. A text mining approach to the prediction of disease status from clinical discharge summaries.

    Science.gov (United States)

    Yang, Hui; Spasic, Irena; Keane, John A; Nenadic, Goran

    2009-01-01

    OBJECTIVE The authors present a system developed for the Challenge in Natural Language Processing for Clinical Data-the i2b2 obesity challenge, whose aim was to automatically identify the status of obesity and 15 related co-morbidities in patients using their clinical discharge summaries. The challenge consisted of two tasks, textual and intuitive. The textual task was to identify explicit references to the diseases, whereas the intuitive task focused on the prediction of the disease status when the evidence was not explicitly asserted. DESIGN The authors assembled a set of resources to lexically and semantically profile the diseases and their associated symptoms, treatments, etc. These features were explored in a hybrid text mining approach, which combined dictionary look-up, rule-based, and machine-learning methods. MEASUREMENTS The methods were applied on a set of 507 previously unseen discharge summaries, and the predictions were evaluated against a manually prepared gold standard. The overall ranking of the participating teams was primarily based on the macro-averaged F-measure. RESULTS The implemented method achieved the macro-averaged F-measure of 81% for the textual task (which was the highest achieved in the challenge) and 63% for the intuitive task (ranked 7(th) out of 28 teams-the highest was 66%). The micro-averaged F-measure showed an average accuracy of 97% for textual and 96% for intuitive annotations. CONCLUSIONS The performance achieved was in line with the agreement between human annotators, indicating the potential of text mining for accurate and efficient prediction of disease statuses from clinical discharge summaries. PMID:19390098

  1. How well do lipophilicity parameters, MEEKC microemulsion capacity factor, and plasma protein binding predict CNS tissue binding?

    Science.gov (United States)

    Zamek-Gliszczynski, Maciej J; Sprague, Karen E; Espada, Alfonso; Raub, Thomas J; Morton, Stuart M; Manro, Jason R; Molina-Martin, Manuel

    2012-05-01

    Brain fraction unbound (Fu) is critical to understanding the pharmacokinetics/dynamics of central nervous system (CNS) drugs, thus several surrogate predictors have been proposed. At present, correlations between brain Fu, microemulsion electrokinetic chromatography capacity factor (MEEKC k'), plasma Fu, octanol-water partition coefficient (clogP), and LogP at pH 7.4 (clogD(7.4) ) were compared for 94 diverse molecules, and additionally for 587 compounds. MEEKC k' was a better predictor of brain Fu (r(2) = 0.74) than calculated lipophilicity parameters (clogP r(2) = 0.51-0.54, clogD(7.4) r(2) = 0.41-0.44), but it was not superior to plasma Fu (r(2) = 0.74-0.85) as a predictor of brain Fu. MEEKC k' did not predict plasma Fu(r(2) = 0.58) as well as brain Fu, and the extent of improvement over clogP or clogD(7.4) (r(2) = 0.41-0.49) was less pronounced. Although log-log-correlation analysis supported seemingly strong prediction of brain Fu both by MEEKC k' and by plasma Fu (r(2) ≥ 0.74), analysis of prediction error estimated a 10-fold and 6.9-8.6-fold prediction interval for brain Fu estimated using MEEKC k' and plasma Fu, respectively. Therefore, MEEKC k' and plasma Fu can predict the log order of CNS tissue binding, but they cannot provide truly quantitative brain Fu predictions necessary to support in-vitro-to-in-vivo extrapolations and pharmacokinetic/dynamic data interpretation. PMID:22344827

  2. 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

  3. 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.

  4. Predictive Factors of Gastrointestinal Caustic Injury According to Clinical and Endoscopic Findings

    Directory of Open Access Journals (Sweden)

    Cherie Quingking

    2013-03-01

    Full Text Available Background: Ingestion of caustic substances is the main reason for referral to Philippines National Poison Management and Control Center among other causes of acute poisoning. Rapid assessment of severity of injury is important for treatment and prognosis of these cases. This study was aimed to investigate the correlation of clinical factors with severity of gastrointestinal (GI mucosal injury. Methods: In this retrospective study, a total of 105 patients were included. Patients were categorized into two groups including 35 patients with low grade and 70 patients with high grade GI injury to compare the predictive value of clinical findings. Results: Mean (SD age of patients was 27 (10 and 47% of patients were male. Oral burns (P

  5. Molecular biomarkers of colorectal cancer: prognostic and predictive tools for clinical practice

    Institute of Scientific and Technical Information of China (English)

    Wei-qin JIANG; Fang-fang FU; Yang-xia LI; Wei-bin WANG; Hao-hao WANG; Hai-ping JIANG; Li-song TENG

    2012-01-01

    Colorectal cancer remains one of the most common types of cancer and leading causes of cancer death worldwide.Although we have made steady progress in chemotherapy and targeted therapy,evidence suggests that the majority of patients undergoing drug therapy experience severe,debilitating,and even lethal adverse drug events which considerably outweigh the benefits.The identification of suitable biomarkers will allow clinicians to deliver the most appropriate drugs to specific patients and spare them ineffective and expensive treatments.Prognostic and predictive biomarkers have been the subjects of many published papers,but few have been widely incorporated into clinical practice.Here,we want to review recent biomarker data related to colorectal cancer,which may have been ready for clinical use.

  6. 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.

  7. Potential Clinical Value of Multiparametric PET in the Prediction of Alzheimer’s Disease Progression

    Science.gov (United States)

    Chen, Xueqi; Zhou, Yun; Wang, Rongfu; Cao, Haoyin; Reid, Savina; Gao, Rui; Han, Dong

    2016-01-01

    Objective To evaluate the potential clinical value of quantitative functional FDG PET and pathological amyloid-β PET with cerebrospinal fluid (CSF) biomarkers and clinical assessments in the prediction of Alzheimer’s disease (AD) progression. Methods We studied 82 subjects for up to 96 months (median = 84 months) in a longitudinal Alzheimer’s Disease Neuroimaging Initiative (ADNI) project. All preprocessed PET images were spatially normalized to standard Montreal Neurologic Institute space. Regions of interest (ROI) were defined on MRI template, and standard uptake values ratios (SUVRs) to the cerebellum for FDG and amyloid-β PET were calculated. Predictive values of single and multiparametric PET biomarkers with and without clinical assessments and CSF biomarkers for AD progression were evaluated using receiver operating characteristic (ROC) analysis and logistic regression model. Results The posterior precuneus and cingulate SUVRs were identified for both FDG and amyloid-β PET in predicating progression in normal controls (NCs) and subjects with mild cognitive impairment (MCI). FDG parietal and lateral temporal SUVRs were suggested for monitoring NCs and MCI group progression, respectively. 18F-AV45 global cortex attained (78.6%, 74.5%, 75.4%) (sensitivity, specificity, accuracy) in predicting NC progression, which is comparable to the 11C-PiB global cortex SUVR’s in predicting MCI to AD. A logistic regression model to combine FDG parietal and posterior precuneus SUVR and Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-Cog) Total Mod was identified in predicating NC progression with (80.0%, 94.9%, 93.9%) (sensitivity, specificity, accuracy). The selected model including FDG posterior cingulate SUVR, ADAS-Cog Total Mod, and Mini-Mental State Exam (MMSE) scores for predicating MCI to AD attained (96.4%, 81.2%, 83.6%) (sensitivity, specificity, accuracy). 11C-PiB medial temporal SUVR with MMSE significantly increased 11C-PiB PET AUC to 0.915 (p<0

  8. Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

    Energy Technology Data Exchange (ETDEWEB)

    Nelms, Benjamin E.; Zhen Heming; Tome, Wolfgang A. [Canis Lupus LLC and Department of Human Oncology, University of Wisconsin, Merrimac, Wisconsin 53561 (United States); Department of Medical Physics, University of Wisconsin, Madison, Wisconsin 53705 (United States); Departments of Human Oncology, Medical Physics, and Biomedical Engineering, University of Wisconsin, Madison, Wisconsin 53792 (United States)

    2011-02-15

    Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa

  9. Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors

    International Nuclear Information System (INIS)

    Purpose: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. Methods: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as ''simulated measurements'' (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. Results: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called ''false negatives.'' The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa. Conclusions: There is a lack of correlation between

  10. Sputum biomarkers and the prediction of clinical outcomes in patients with cystic fibrosis.

    Directory of Open Access Journals (Sweden)

    Theodore G Liou

    Full Text Available Lung function, acute pulmonary exacerbations (APE, and weight are the best clinical predictors of survival in cystic fibrosis (CF; however, underlying mechanisms are incompletely understood. Biomarkers of current disease state predictive of future outcomes might identify mechanisms and provide treatment targets, trial endpoints and objective clinical monitoring tools. Such CF-specific biomarkers have previously been elusive. Using observational and validation cohorts comprising 97 non-transplanted consecutively-recruited adult CF patients at the Intermountain Adult CF Center, University of Utah, we identified biomarkers informative of current disease and predictive of future clinical outcomes. Patients represented the majority of sputum producers. They were recruited March 2004-April 2007 and followed through May 2011. Sputum biomarker concentrations were measured and clinical outcomes meticulously recorded for a median 5.9 (interquartile range 5.0 to 6.6 years to study associations between biomarkers and future APE and time-to-lung transplantation or death. After multivariate modeling, only high mobility group box-1 protein (HMGB-1, mean=5.84 [log ng/ml], standard deviation [SD] =1.75 predicted time-to-first APE (hazard ratio [HR] per log-unit HMGB-1=1.56, p-value=0.005, number of future APE within 5 years (0.338 APE per log-unit HMGB-1, p<0.001 by quasi-Poisson regression and time-to-lung transplantation or death (HR=1.59, p=0.02. At APE onset, sputum granulocyte macrophage colony stimulating factor (GM-CSF, mean 4.8 [log pg/ml], SD=1.26 was significantly associated with APE-associated declines in lung function (-10.8 FEV(1% points per log-unit GM-CSF, p<0.001 by linear regression. Evaluation of validation cohorts produced similar results that passed tests of mutual consistency. In CF sputum, high HMGB-1 predicts incidence and recurrence of APE and survival, plausibly because it mediates long-term airway inflammation. High APE-associated GM

  11. Do clinical factors help to predict disease course in inflammatory bowel disease?

    Institute of Scientific and Technical Information of China (English)

    Edouard; Louis; Jacques; Belaiche; Catherine; Reenaers

    2010-01-01

    While therapeutic strategies able to change the natural history of the disease are developing,it is of major importance to have available predictive factors for aggressive disease to try and target these therapeutic strategies.Clinical predictors have probably been the most broadly studied.In both Crohn's disease(CD) and ulcerative colitis(UC),age at diagnosis,disease location and smoking habit are currently the strongest predictors of disease course.A younger age at onset is associated with more aggressive...

  12. Systematic prediction of drug combinations based on clinical side-effects.

    Science.gov (United States)

    Huang, Hui; Zhang, Ping; Qu, Xiaoyan A; Sanseau, Philippe; Yang, Lun

    2014-01-01

    Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures. PMID:25418113

  13. 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...

  14. 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.

  15. Derivation of stellar parameters from Gaia RVS spectra with prediction uncertainty using Generative Artificial Neural Networks (GANNs)

    Science.gov (United States)

    Manteiga, Minia; Dafonte, Jose Carlos; Ulla, Ana; Alvarez, Marco Antonio; Garabato, Daniel; Fustes, Diego

    2015-08-01

    The main purpose of Gaia Radial Velocity Spectrograph (RVS) is to measure the radial velocity of stars in the near infrared CaII spectral region. However, RVS will be used also for estimating the main stellar astrophysical parameters: effective temperature (Teff), logarithm of surface gravity (logg), abundance of metal elements with respect to hydrogen ([Fe/H]) and abundance of alpha elements with respect to iron ([α/Fe]). The software package being developed by Gaia DPAC (Data Processing and Analysis Consorcium) is composed by a bunch of modules which address the problem of parameterization from different perspectives This work focuses on developments carried out in the framework of one of these modules, called ANN, that is based on the application of Artificial Neural Networks.ANNs are a great tool that offers non-linear regression capabilities to any degree of complexity. Furthermore, they can provide accurate predictions when new data is presented to them, since they can generalize their solutions. However, in principle, ANNs are not able to give a measure of uncertainty over their predictions. Giving a measure of uncertainty over predictions is desirable in application domains where posterior inferences need to assess the quality of the predictions, especially when the behaviour of the system is not completely known. This is the case of data analysis coming from complex scientific missions like Gaia. This work presents a new architecture for ANNs, Generative ANNs (GANNs), that models the forward function instead of the inverse one. The advantage of forward modelling is that it estimates the actual observation, so that the fit between the estimated observation and the actual observation can be assessed, which allows for novelty detection, model evaluation and active learning. Furthermore, GANNs can be integrated in a Bayesian framework, which allows to estimate the full posterior distribution over the parameters of interest, to perform model comparisons, etc.

  16. 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.

  17. Study on meteorological parameters during pre-monsoon period at Trombay for environmental impact predictions

    International Nuclear Information System (INIS)

    Bhabha Atomic Research Centre, Trombay site is characterized by complex topography with approximately 330 meter hill at one side and the Arabian Sea at the other which at the eastern coast of Mumbai. A research reactor (DHRUVA) is located in the almost central part of the BARC site. During the operation of research reactor, it gives rise to the formation of fission product noble gases (FPNGs) and other radionuclides but are retained in the fuel matrix itself. Any minor defects in the fuel matrix may lead to the release of the activity to the environment after the filtration through the bank of the High Efficiency Particulate Activity (HEPA) filters. The various reactor components like shut off rods, pneumatic carrier facilities etc. are cooled by processed air that leads to the formation of activation products like 41Ar, which is discharged through the elevated stack. In case of release of gaseous radioactive effluents to the environment though much below permissible limits, exposure to the public may occur by various pathways. External exposure occurs during the passage of the radioactive plume. The doses at different locations due to dispersion of the released gaseous effluent activity can vary depending on the meteorological conditions and effect of topography. In order to estimate the public exposures due to the plume dispersion, the meteorological parameters like wind speed, wind direction and stability category are essential. The paper presents the conclusions from meteorological data for two consecutive years (2012-2013) for Trombay, Mumbai

  18. Diagnostic and prognostic accuracy of clinical and laboratory parameters in community-acquired pneumonia

    Directory of Open Access Journals (Sweden)

    Nusbaumer Charly

    2007-03-01

    Full Text Available Abstract Background Community-acquired pneumonia (CAP is the most frequent infection-related cause of death. The reference standard to diagnose CAP is a new infiltrate on chest radiograph in the presence of recently acquired respiratory signs and symptoms. This study aims to evaluate the diagnostic and prognostic accuracy of clinical signs and symptoms and laboratory biomarkers for CAP. Methods 545 patients with suspected lower respiratory tract infection, admitted to the emergency department of a university hospital were included in a pre-planned post-hoc analysis of two controlled intervention trials. Baseline assessment included history, clinical examination, radiography and measurements of procalcitonin (PCT, highly sensitive C-reactive protein (hsCRP and leukocyte count. Results Of the 545 patients, 373 had CAP, 132 other respiratory tract infections, and 40 other final diagnoses. The AUC of a clinical model including standard clinical signs and symptoms (i.e. fever, cough, sputum production, abnormal chest auscultation and dyspnea to diagnose CAP was 0.79 [95% CI, 0.75–0.83]. This AUC was significantly improved by including PCT and hsCRP (0.92 [0.89–0.94]; p Conclusion PCT, and to a lesser degree hsCRP, improve the accuracy of currently recommended approaches for the diagnosis of CAP, thereby complementing clinical signs and symptoms. PCT is useful in the severity assessment of CAP.

  19. A clinical comparative study of anatomic parameters before and after total hip replacement on congenital dysplasia.

    Science.gov (United States)

    Huang, Ziqiang; Zhou, Yonggang; Chai, Wei; Ji, Weiping; Cui, Guopeng; Ma, Miaoqun; Zhu, Yin

    2016-07-01

    [Purpose] To study preoperative and postoperative hip circumference data of various types of congenital dysplasia of the hip treated with total hip replacement, including the femoral offset, femoral neck length, height, and hip abductor arm parameters. [Subjects and Methods] This study included seventy-eight cases of congenital dysplasia of the hip (I-III type). Furthermore, four parameters were measured, including the preoperative and postoperative femoral offset. Statistical data analysis was performed using the SPSS 13.0 software. [Results] The femoral offset was 33.3 ± 8.4 mm (preoperative) and 39.1 ± 7.1 mm (postoperative). The femoral head height was 59.5 ± 8.7 mm (preoperative) and 68.8 ± 11.0 mm (postoperative). The femoral neck length was 50.8 ± 10.8 mm (preoperative) and 61.5 ± 10.4 mm (postoperative). The hip abductor arm was 54.3 ± 9.6 mm (preoperative) 64.7 ± 10.1 mm (postoperative). The preoperative and postoperative parameters showed statistical differences. Furthermore, no significant differences were evidenced when comparing the postoperative hip parameters with the normal data parameters. [Conclusion] Total hip replacement on congenital dysplasia of the hip could lead to the rebuilt of an almost normal physiological anatomy for each hip case (type I-III). PMID:27512242

  20. Predictor value of some clinical-biological parameters for the onset of depressive disorder in elderly patients with unstable angina

    OpenAIRE

    Cristina Moşuţan; George Săraci; Caius R. Duncea

    2012-01-01

    Abstract. Objective: To evaluate the potential predictor value of some parameters for the onset of depression after an episode of unstableangina in elderly. Material and Methods: We included 103 elderly patients who suffered an acute unstable angina episode. Clinical, laband imagistic data was recorded in the first week after admittance. Six month after unstable angina episode, patients were evaluated for thepresence of depression. Results: Univariate analysis showed statistically significant...

  1. Detection of human herpes viruses in patients with chronic and aggressive periodontitis and relationship between viruses and clinical parameters

    OpenAIRE

    Das, Sushma; Krithiga, G Shobha Prakash; Gopalakrishnan, S.

    2012-01-01

    Background and Aims: Recent microbiological researches have revealed the possible role of human cytomegalovirus (HCMV), Epstein barr virus (EBV), and herpes simplex virus (HSV-1 and HSV-2) in the etiopathogenesis of periodontal diseases. The present pilot study has been undertaken to detect the presence of these viruses in chronic periodontitis, aggressive periodontitis, and healthy individuals and to determine the relationship between these viruses and the clinical parameters. Materials and ...

  2. Retinopathy of Prematurity in Very Low Birth Weight Infants: Effects of Serum Vitamin A and Clinical Parameters

    OpenAIRE

    Esra Arun Özer; Özlem Sivaslı Gül; Gamze Men; Ekrem Talay; Sümer Sütçüoğlu; Ali Kanık; Ebru Türkoğlu; Zelal Kahramaner; Hese Coşar; Aydın Erdemir; Işın Yaprak

    2011-01-01

    Pur po se: Retinopathy of prematurity (ROP) is a proliferative vascular disease which affects premature newborns and occurs during vessel development. The pathogenesis of ROP is complex and includes oxidative damage to the developing retina. The aim of this study was to evaluate the relationship of ROP with serum vitamin A levels and clinical parameters in infants with a gestational age of ≤32 weeks and birth weight of ≤1500 grams. Ma te ri al and Met hod: Newborns admitted to Newbor...

  3. Hunting for hydrogen: random structure searching and prediction of NMR parameters of hydrous wadsleyite.

    Science.gov (United States)

    Moran, Robert F; McKay, David; Pickard, Chris J; Berry, Andrew J; Griffin, John M; Ashbrook, Sharon E

    2016-04-21

    The structural chemistry of materials containing low levels of nonstoichiometric hydrogen is difficult to determine, and producing structural models is challenging where hydrogen has no fixed crystallographic site. Here we demonstrate a computational approach employing ab initio random structure searching (AIRSS) to generate a series of candidate structures for hydrous wadsleyite (β-Mg2SiO4 with 1.6 wt% H2O), a high-pressure mineral proposed as a repository for water in the Earth's transition zone. Aligning with previous experimental work, we solely consider models with Mg3 (over Mg1, Mg2 or Si) vacancies. We adapt the AIRSS method by starting with anhydrous wadsleyite, removing a single Mg(2+) and randomly placing two H(+) in a unit cell model, generating 819 candidate structures. 103 geometries were then subjected to more accurate optimisation under periodic DFT. Using this approach, we find the most favourable hydration mechanism involves protonation of two O1 sites around the Mg3 vacancy. The formation of silanol groups on O3 or O4 sites (with loss of stable O1-H hydroxyls) coincides with an increase in total enthalpy. Importantly, the approach we employ allows observables such as NMR parameters to be computed for each structure. We consider hydrous wadsleyite (∼1.6 wt%) to be dominated by protonated O1 sites, with O3/O4-H silanol groups present as defects, a model that maps well onto experimental studies at higher levels of hydration (J. M. Griffin et al., Chem. Sci., 2013, 4, 1523). The AIRSS approach adopted herein provides the crucial link between atomic-scale structure and experimental studies. PMID:27020937

  4. Autologous Immunoglobulin Therapy in Patients With Severe Recalcitrant Atopic Dermatitis: Long-Term Changes of Clinical Severity and Laboratory Parameters.

    Science.gov (United States)

    Nahm, Dong Ho; Ahn, Areum; Kim, Myoung Eun; Cho, Su Mi; Park, Mi Jung

    2016-07-01

    This report evaluated long-term changes in clinical severity and laboratory parameters in 3 adult patients with severe recalcitrant atopic dermatitis (AD) who were treated with intramuscular injections of 50 mg of autologous immunoglobulin G (IgG) twice a week for 4 weeks (autologous immunoglobulin therapy, AIGT) and followed up for more than 2 years after the treatment. We observed the following 4 major findings in these 3 patients during the long-term follow-up after AIGT. (1) Two of the 3 patients showed a long-term clinical improvement for more than 36 weeks after AIGT with a maximum decrease in clinical severity score greater than 80% from baseline. (2) These 2 patients also showed long-term decreases in serum total IgE concentrations and peripheral blood eosinophil count for more than 36 weeks after AIGT with a maximum decrease in the two laboratory parameters of allergic inflammatory greater than 70% from baseline. (3) No significant side effect was observed during the 2 years of follow-up period after the AIGT in all 3 patients. (4) Serum levels of IgG anti-idiotype antibodies to the F(ab')₂ fragment of autologous IgG administered for the treatment were not significantly changed after AIGT in all 3 patients. These findings suggest that AIGT has long-term favorable effects on both clinical severity and laboratory parameters in selected patients with severe recalcitrant AD. Further studies are required to evaluate the clinical usefulness and therapeutic mechanism of AIGT for AD. PMID:27126731

  5. ESTABLISHMENT OF ECHOCARDIOGRAPHIC PARAMETERS OF CLINICALLY HEALTHY FLORIDA MANATEES (TRICHECHUS MANATUS LATIROSTRIS).

    Science.gov (United States)

    Gerlach, Trevor J; Estrada, Amara H; Sosa, Ivan S; Powell, Melanie; Lamb, Kenneth E; Ball, Ray L; de Wit, Martine; Walsh, Mike T

    2015-06-01

    A standardized echocardiographic technique was recently established for the Florida manatee (Trichechus manatus latirostris). There are no available published data on normal echocardiographic parameters in any Sirenian species. The purpose of this study was to report reference parameters for various echocardiographic measurements. These parameters are intended to serve as a comparison for future research into the prevalence of cardiac diseases in the manatee and to aid in diagnosing animals with suspected cardiac disease in rehabilitation facilities. Annual health assessments of free-ranging manatees in Crystal River National Wildlife Refuge, Florida, and pre-release health assessments of rehabilitated manatees at Tampa's Lowry Park Zoo permitted comparison of echocardiographic measurements in adult (n=14), subadult (n=7), and calf (n=8) animals under manual restraint. PMID:26056870

  6. Evaluation of Carotid Arterial Intima-Media Thickness (IMT and Its Relation to Clinical Parameters in Japanese Children

    Directory of Open Access Journals (Sweden)

    Tamura,Hiroko

    2011-02-01

    Full Text Available The aim of this study was to evaluate the carotid arterial intima-media thickness (IMT and its relation to clinical parameters in Japanese children. Fifty-two healthy children (39 boys and 13 girls, aged 6-14 years, were enrolled in this cross-sectional investigation study. IMT of the common carotid artery was determined using ultrasonography. We also investigated anthropometric parameters, blood pressure (BP, lifestyles and blood examinations. The mean value of IMT was 0.4±0.1mm, which was lower than the normal value (1.0mm in adults. IMT was positively correlated with age (r=0.340 and height (r=0.346 in boys, while it was positively correlated with body mass index (BMI (r=0.584 and diastolic BP (DBP (r=0.563 in girls. In addition, IMT was associated with sleeping hours and hours of watching television (TV by using stepwise regression analysis. In conclusion, IMT increased with aging, and it was linked to some clinical parameters of atherosclerosis and lifestyles in children. Therefore, this reference data will be helpful for future assessment of age-related change in Japanese children in clinical practice, and IMT might be a good predictor of atherosclerosis in Japanese children.

  7. Factors predicting suicidal ideation in the preceding 12 months among patients attending a community psychiatric outpatient clinic.

    LENUS (Irish Health Repository)

    Anyansi, Tochukwu E

    2013-06-01

    Predictive factors are used to alert the clinician to the necessity of carrying out a suicide risk assessment in those patients whose demographic and clinical characteristics suggest the possibility of suicide.

  8. Clinical and radiological parameters of patients with lung thromboembolism, diagnosed by high probability ventilation / perfusion scintigraphies

    International Nuclear Information System (INIS)

    Background: pulmonary embolism (PE) remains an elusive diagnosis, and still causes too many unexpected deaths. Because of this, noninvasive investigations are done when pulmonary embolism is suspected. Objective: to determine the clinical and x-rays findings in patients with diagnosis of pulmonary embolism by high probability ventilation/perfusion (V/Q) lung scan. Materials and methods: inpatient medical records of 91 patients with clinical suspected PE and high and low probability V/Q lung scan were analyzed (PIOPED criterion). Results: there were statistics correlation with four clinical findings: hemoptysis (p value=0,02, odds ratio=8,925), taquicardia (p value=0,02 odds ratio=3,5), chest pain (p value=0,01, odds ratio=1,87), and recent surgery (p value=0,02, odds ratio=2,762). The 70,7% chest x-rays were normal (p value < 0,001). Conclusion: the clinical and x-rays findings in patients with diagnosis of PE by high probability V/Q lung scan were: hemoptysis, taquicardia, chest pain, recent surgery and normal chest x-ray. This is important because would help to choose the patients in whom the V/Q lung scan will have the maximal performance (Au)

  9. Outcomes of Health System Structures, Highly Pertinent Clinical Information, Idea Stimulators, Clinical Reviews, and Prediction Tools: JABFM Exemplified.

    Science.gov (United States)

    Bowman, Marjorie A; Neale, Anne Victoria; Seehusen, Dean A

    2016-01-01

    This issue exemplifies the types of articles that JABFM publishes to advance family medicine. We have articles on the implications of health system organizational structures. Three of these are international articles at the level of the national health system (1 from China) and systematic local health interventions (1 from Canada and 1 from Netherlands). Inside the United States, where there are more family physicians, there is less obesity, and designation as a Patient Centered Medical Home is related to increased rates of colorectal cancer screening. Review articles on common clinical topics discuss treatments that are changing (acne in pregnancy) or lack consensus (distal radial fractures). We have articles on making life easier in the office, such as for predicting Vitamin D levels, osteoporosis, and pre-diabetes in normal weight adults. There are articles to raise awareness of the "newest" testing or treatments, that is, auditory brainstem implants. "Reminder" articles highlight known entities that need to be reinforced to prevent over-/underdiagnosis or treatment, for example, "cotton fever." Another article discusses the increased risk for postoperative complications with sleep apnea. We also provide "thought" pieces, in this case about the terminology we are using to extend our concept of patient-centered medical homes. PMID:26957371

  10. The predictability of renin-angiotensin-aldosterone system factors for clinical outcome in patients with acute decompensated heart failure.

    Science.gov (United States)

    Nakada, Yasuki; Takahama, Hiroyuki; Kanzaki, Hideaki; Sugano, Yasuo; Hasegawa, Takuya; Ohara, Takahiro; Amaki, Makoto; Funada, Akira; Yoshida, Akemi; Yasuda, Satoshi; Ogawa, Hisao; Anzai, Toshihisa

    2016-06-01

    Although counter-regulation between B-type natriuretic peptide (BNP) levels and renin-angiotensin-aldosterone system (RAAS) activation in heart failure (HF) has been suggested, whether the regulation is preserved in acute decompensated heart failure (ADHF) patients remains unclear. This study aimed to determine: (1) the relationship between RAAS activation and clinical outcomes in ADHF patients, and (2) the relationships between plasma BNP levels and degrees of activation in RAAS factors. This study included ADHF patients (n = 103, NYHA3-4, plasma BNP > 200 pg/ml). We studied the predictability of RAAS factors for cardiovascular events and the relationships between plasma BNP levels and the degrees of activation in RAAS factors, which were evaluated by plasma renin activity (PRA) and aldosterone concentration (PAC). PRA was a strong predictor of cardiovascular (CV) events over 1 year, even after accounting for plasma BNP levels (hazard ratio (HR): 1.04, CI [1.02-1.06], p analysis, p = 0.06). Cut-off value of PRA (5.3 ng/ml/h) was determined by AUC curve. Of the enrolled patients, higher PRA was found in 40 % of them. Although no correlation between the plasma BNP levels and PRA was found (p = 0.36), after adjusting for hemodynamic parameters, eGFR and medication, a correlation was found between them (p = 0.01). Elevated RAAS factors were found in a substantial number of ADHF patients with high plasma BNP levels in the association with hemodynamic state, which predicts poor clinical outcomes. The measurements of RAAS factors help to stratify ADHF patients at risk for further CV events. PMID:25964073

  11. Clinical manifestations that predict abnormal brain computed tomography (CT in children with minor head injury

    Directory of Open Access Journals (Sweden)

    Nesrin Alharthy

    2015-01-01

    Full Text Available Background: Computed tomography (CT used in pediatric pediatrics brain injury (TBI to ascertain neurological manifestations. Nevertheless, this practice is associated with adverse effects. Reports in the literature suggest incidents of morbidity and mortality in children due to exposure to radiation. Hence, it is found imperative to search for a reliable alternative. Objectives: The aim of this study is to find a reliable clinical alternative to detect an intracranial injury without resorting to the CT. Materials and Methods: Retrospective cross-sectional study was undertaken in patients (1-14 years with blunt head injury and having a Glasgow Coma Scale (GCS of 13-15 who had CT performed on them. Using statistical analysis, the correlation between clinical examination and positive CT manifestation is analyzed for different age-groups and various mechanisms of injury. Results: No statistically significant association between parameteres such as Loss of Consciousness, ′fall′ as mechanism of injury, motor vehicle accidents (MVA, more than two discrete episodes of vomiting and the CT finding of intracranial injury could be noted. Analyzed data have led to believe that GCS of 13 at presentation is the only important clinical predictor of intracranial injury. Conclusion: Retrospective data, small sample size and limited number of factors for assessing clinical manifestation might present constraints on the predictive rule that was derived from this review. Such limitations notwithstanding, the decision to determine which patients should undergo neuroimaging is encouraged to be based on clinical judgments. Further analysis with higher sample sizes may be required to authenticate and validate findings.

  12. Clinical biochemical and hormonal profiling in plasma: a promising strategy to predict growth hormone abuse in cattle.

    Science.gov (United States)

    Doué, Mickael; Dervilly-Pinel, Gaud; Cesbron, Nora; Stefani, Annalisa; Moro, Letizia; Biancotto, Giancarlo; Le Bizec, Bruno

    2015-06-01

    Recombinant bovine somatotrophin (rbST) is widely used in some countries to increase milk production. Since 1994, both marketing and use of this substance have been prohibited within the European Union. In this context, the targeted plasma biochemical and hormonal profiling was assessed as a potential screening strategy to highlight rbST (ab)use in cattle. Twenty-one routinely measured clinical blood parameters, representative of main biological profiles (energetic, proteic, etc.), were measured in the plasma of six lactating cows before and after rbST treatment throughout a 23-day study period. Appropriate multivariate statistical analyses [principal component analysis (PCA) and orthogonal partial least square (OPLS)] enabled discriminating animal samples before and after treatment (days 0 vs. 2 to 9, P = 2.10(-9)) and highlighted the five most relevant blood parameters in this discrimination. Based on each five-analyte contribution, a simple mathematically weighted equation was suggested to predict the status of samples. A suspicious threshold was proposed, and the model was further tested with the status prediction of the supplementary samples from untreated (n = 20) and treated cows (n = 22). The calculated false-positive (10%) and false-negative (4.5%) rates were in accordance with the EU requirements for screening methods. Although the model needs to be further validated with additional samples, such targeted plasma biochemical and hormonal profiling already appears as a potential promising screening strategy to highlight rbST (ab)use in cattle. PMID:25716468

  13. Integration of noninvasive prenatal prediction of fetal blood group into clinical prenatal care.

    Science.gov (United States)

    Clausen, Frederik Banch

    2014-05-01

    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 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 targeted prenatal prophylaxis, thus avoiding unnecessary exposure to anti-D in pregnant women. The analytical aspect of noninvasive fetal RHD typing is very robust and accurate, and its routine utilization has demonstrated high sensitivities for fetal RHD detection. A high compliance with administering anti-D is essential for obtaining a clinical effect. Noninvasive fetal typing of RHC/c, RHE/e, and KEL may become more widely used in the future. PMID:24431264

  14. Knee shape might predict clinical outcome after an anterior cruciate ligament rupture.

    Science.gov (United States)

    Eggerding, V; van Kuijk, K S R; van Meer, B L; Bierma-Zeinstra, S M A; van Arkel, E R A; Reijman, M; Waarsing, J H; Meuffels, D E

    2014-06-01

    We have investigated whether shape of the knee can predict the clinical outcome of patients after an anterior cruciate ligament rupture. We used statistical shape modelling to measure the shape of the knee joint of 182 prospectively followed patients on lateral and Rosenberg view radiographs of the knee after a rupture of the anterior cruciate ligament. Subsequently, we associated knee shape with the International Knee Documentation Committee subjective score at two years follow-up. The mean age of patients was 31 years (21 to 51), the majority were male (n = 121) and treated operatively (n = 135). We found two modes (shape variations) that were significantly associated with the subjective score at two years: one for the operatively treated group (p = 0.002) and one for the non-operatively treated group (p = 0.003). Operatively treated patients who had higher subjective scores had a smaller intercondylar notch and a smaller width of the intercondylar eminence. Non-operatively treated patients who scored higher on the subjective score had a more pyramidal intercondylar notch as opposed to one that was more dome-shaped. We conclude that the shape of the femoral notch and the intercondylar eminence is predictive of clinical outcome two years after a rupture of the anterior cruciate ligament. PMID:24891572

  15. Developing a clinical utility framework to evaluate prediction models in radiogenomics

    Science.gov (United States)

    Wu, Yirong; Liu, Jie; Munoz del Rio, Alejandro; Page, David C.; Alagoz, Oguzhan; Peissig, Peggy; Onitilo, Adedayo A.; Burnside, Elizabeth S.

    2015-03-01

    Combining imaging and genetic information to predict disease presence and behavior is being codified into an emerging discipline called "radiogenomics." Optimal evaluation methodologies for radiogenomics techniques have not been established. We aim to develop a clinical decision framework based on utility analysis to assess prediction models for breast cancer. Our data comes from a retrospective case-control study, collecting Gail model risk factors, genetic variants (single nucleotide polymorphisms-SNPs), and mammographic features in Breast Imaging Reporting and Data System (BI-RADS) lexicon. We first constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail+SNP, and (3) Gail+SNP+BI-RADS. Then, we generated ROC curves for three models. After we assigned utility values for each category of findings (true negative, false positive, false negative and true positive), we pursued optimal operating points on ROC curves to achieve maximum expected utility (MEU) of breast cancer diagnosis. We used McNemar's test to compare the predictive performance of the three models. We found that SNPs and BI-RADS features augmented the baseline Gail model in terms of the area under ROC curve (AUC) and MEU. SNPs improved sensitivity of the Gail model (0.276 vs. 0.147) and reduced specificity (0.855 vs. 0.912). When additional mammographic features were added, sensitivity increased to 0.457 and specificity to 0.872. SNPs and mammographic features played a significant role in breast cancer risk estimation (p-value < 0.001). Our decision framework comprising utility analysis and McNemar's test provides a novel framework to evaluate prediction models in the realm of radiogenomics.

  16. Expression profiling to predict the clinical behaviour of ovarian cancer fails independent evaluation

    International Nuclear Information System (INIS)

    In a previously published pilot study we explored the performance of microarrays in predicting clinical behaviour of ovarian tumours. For this purpose we performed microarray analysis on 20 patients and estimated that we could predict advanced stage disease with 100% accuracy and the response to platin-based chemotherapy with 76.92% accuracy using leave-one-out cross validation techniques in combination with Least Squares Support Vector Machines (LS-SVMs). In the current study we evaluate whether tumour characteristics in an independent set of 49 patients can be predicted using the pilot data set with principal component analysis or LS-SVMs. The results of the principal component analysis suggest that the gene expression data from stage I, platin-sensitive advanced stage and platin-resistant advanced stage tumours in the independent data set did not correspond to their respective classes in the pilot study. Additionally, LS-SVM models built using the data from the pilot study – although they only misclassified one of four stage I tumours and correctly classified all 45 advanced stage tumours – were not able to predict resistance to platin-based chemotherapy. Furthermore, models based on the pilot data and on previously published gene sets related to ovarian cancer outcomes, did not perform significantly better than our models. We discuss possible reasons for failure of the model for predicting response to platin-based chemotherapy and conclude that existing results based on gene expression patterns of ovarian tumours need to be thoroughly scrutinized before these results can be accepted to reflect the true performance of microarray technology

  17. Does Dyspnoea during dipyridamole cardiac stress testing indicate bronchospasm and is the pretest clinical history predictive of this side-effect?

    International Nuclear Information System (INIS)

    This study investigates the acute effects of intravenous dipyridamole (0.7 mg/kg) on pulmonary airflow in relation to clinical parameters suggestive of chronic obstructive pulmonary disease (COPD) in order to assess predictive and causative factors of dyspnoea during cardiac stress testing. Mild pulmonary airflow obstruction was noted in all patients, but reached statistical significance only in small airways. The changes in pulmonary function parameters were independent of the clinical history. Dyspnoea under dipyridamole stress testing occurred in parallel with angina, yet was not associated with ischaemic or non-ischaemic left ventricular dysfunction. These data do not support the use of dipyridamole stress testing in asthmatics, but show that (1) the acute effects of a diagnostic dose of dipyridamole on pulmonary airflow are mild even in patients with a history suggestive of COPD and (2) dyspnoea during dipyridamole testing is not necessarily indicative of bronchospasm. (orig./MG)

  18. SITE-94. Discrete-feature modelling of the Aespoe Site: 3. Predictions of hydrogeological parameters for performance assessment

    Energy Technology Data Exchange (ETDEWEB)

    Geier, J.E. [Golder Associates AB, Uppsala (Sweden)

    1996-12-01

    A 3-dimensional, discrete-feature hydrological model is developed. The model integrates structural and hydrologic data for the Aespoe site, on scales ranging from semi regional fracture zones to individual fractures in the vicinity of the nuclear waste canisters. Predicted parameters for the near field include fracture spacing, fracture aperture, and Darcy velocity at each of forty canister deposition holes. Parameters for the far field include discharge location, Darcy velocity, effective longitudinal dispersion coefficient and head gradient, flow porosity, and flow wetted surface, for each canister source that discharges to the biosphere. Results are presented in the form of statistical summaries for a total of 42 calculation cases, which treat a set of 25 model variants in various combinations. The variants for the SITE-94 Reference Case model address conceptual and parametric uncertainty related to the site-scale hydrogeologic model and its properties, the fracture network within the repository, effective semi regional boundary conditions for the model, and the disturbed-rock zone around the repository tunnels and shafts. Two calculation cases simulate hydrologic conditions that are predicted to occur during future glacial episodes. 30 refs.

  19. SITE-94. Discrete-feature modelling of the Aespoe Site: 3. Predictions of hydrogeological parameters for performance assessment

    International Nuclear Information System (INIS)

    A 3-dimensional, discrete-feature hydrological model is developed. The model integrates structural and hydrologic data for the Aespoe site, on scales ranging from semi regional fracture zones to individual fractures in the vicinity of the nuclear waste canisters. Predicted parameters for the near field include fracture spacing, fracture aperture, and Darcy velocity at each of forty canister deposition holes. Parameters for the far field include discharge location, Darcy velocity, effective longitudinal dispersion coefficient and head gradient, flow porosity, and flow wetted surface, for each canister source that discharges to the biosphere. Results are presented in the form of statistical summaries for a total of 42 calculation cases, which treat a set of 25 model variants in various combinations. The variants for the SITE-94 Reference Case model address conceptual and parametric uncertainty related to the site-scale hydrogeologic model and its properties, the fracture network within the repository, effective semi regional boundary conditions for the model, and the disturbed-rock zone around the repository tunnels and shafts. Two calculation cases simulate hydrologic conditions that are predicted to occur during future glacial episodes. 30 refs

  20. Comparison of Existing Clinical Scoring Systems in Predicting Severity and Prognoses of Hyperlipidemic Acute Pancreatitis in Chinese Patients

    OpenAIRE

    Qiu, Lei; Sun, Rui Qing; Jia, Rong Rong; Ma, Xiu Ying; Cheng, Li; Tang, Mao Chun; Zhao, Yan

    2015-01-01

    Abstract It is important to identify the severity of acute pancreatitis (AP) in the early course of the disease. Clinical scoring systems may be helpful to predict the prognosis of patients with early AP; however, few analysts have forecast the accuracy of scoring systems for the prognosis in hyperlipidemic acute pancreatitis (HLAP). The purpose of this study was to summarize the clinical characteristics of HLAP and compare the accuracy of conventional scoring systems in predicting the progno...

  1. Correlated regions of cerebral blood flow with clinical parameters in Parkinson's disease. Comparison using 'Anatomy' and 'Talairach Daemon' software

    International Nuclear Information System (INIS)

    We assign the anatomical names of functional activation regions in the brain, based on the probabilistic cyto-architectonic atlas by Anatomy 1.7 from an analysis of correlations between regional cerebral blood flow (rCBF) and clinical parameters of the non-demented Parkinson's disease (PD) patients by statistical parametric mapping (SPM) 8. We evaluated Anatomy 1.7 of SPM toolbox compared to 'Talairach Daemon' (TD) Client 2.4.2 software. One hundred and thirty-six patients (mean age 60.0±9.09 years; 73 women and 63 men) with non-demented PD were selected. Tc-99m-HMPAO brain single-photon emission computed tomography (SPECT) scans were performed on the patients using a two-head gamma-camera. We analyzed the brain image of PD patients by SPM8 and found the anatomical names of correlated regions of rCBF perfusion with the clinical parameters using TD Client 2.4.2 and Anatomy 1.7. The SPM8 provided a correlation coefficient between clinical parameters and cerebral hypoperfusion by a simple regression method. To the clinical parameters were added age, duration of disease, education period, Hoehn and Yahr (H and Y) stage and Korean mini-mental state examination (K-MMSE) score. Age was correlated with cerebral perfusion in the Brodmann area (BA) 6 and BA 3b assigned by Anatomy 1.7 and BA 6 and pyramis in gray matter by TD Client 2.4.2 with p<0.001 uncorrected. Also, assigned significant correlated regions were found in the left and right lobules VI (Hem) with duration of disease, in left and right lobules VIIa crus I (Hem) with education, in left insula (Ig2), left and right lobules VI (Hem) with H and Y, and in BA 4a and 6 with K-MMSE score with p<0.05 uncorrected by Anatomy 1.7, respectively. Most areas of correlation were overlapped by two different anatomical labeling methods, but some correlation areas were found with different names. Age was the most significantly correlated clinical parameter with rCBF. TD Client found the exact anatomical name by the peak

  2. Predicting progression of IgA nephropathy: new clinical progression risk score.

    Directory of Open Access Journals (Sweden)

    Jingyuan Xie

    Full Text Available IgA nephropathy (IgAN is a common cause of end-stage renal disease (ESRD in Asia. In this study, based on a large cohort of Chinese patients with IgAN, we aim to identify independent predictive factors associated with disease progression to ESRD. We collected retrospective clinical data and renal outcomes on 619 biopsy-diagnosed IgAN patients with a mean follow-up time of 41.3 months. In total, 67 individuals reached the study endpoint defined by occurrence of ESRD necessitating renal replacement therapy. In the fully adjusted Cox proportional hazards model, there were four baseline variables with a significant independent effect on the risk of ESRD. These included: eGFR [HR = 0.96(0.95-0.97], serum albumin [HR = 0.47(0.32-0.68], hemoglobin [HR = 0.79(0.72-0.88], and SBP [HR = 1.02(1.00-1.03]. Based on these observations, we developed a 4-variable equation of a clinical risk score for disease progression. Our risk score explained nearly 22% of the total variance in the primary outcome. Survival ROC curves revealed that the risk score provided improved prediction of ESRD at 24th, 60th and 120th month of follow-up compared to the three previously proposed risk scores. In summary, our data indicate that IgAN patients with higher systolic blood pressure, lower eGFR, hemoglobin, and albumin levels at baseline are at a greatest risk of progression to ESRD. The new progression risk score calculated based on these four baseline variables offers a simple clinical tool for risk stratification.

  3. Translation of clinical prediction rules for febrile children to primary care practice: an observational cohort study

    Science.gov (United States)

    van Ierland, Yvette; Elshout, Gijs; Berger, Marjolein Y; Vergouwe, Yvonne; de Wilde, Marcel; van der Lei, Johan; Mol, Henriëtte A; Oostenbrink, Rianne

    2015-01-01

    Background Clinical prediction rules (CPRs) to identify children with serious infections lack validation in low-prevalence populations, which hampers their implementation in primary care practice. Aim To evaluate the diagnostic value of published CPRs for febrile children in primary care. Design and setting Observational cohort study among febrile children (<16 years) who consulted five GP cooperatives (GPCs) in the Netherlands. Method Alarm signs of serious infection and clinical management were extracted from routine clinical practice data and manually recoded with a structured electronic data-entry program. Eight CPRs were selected from literature. CPR-variables were matched with alarm signs and CPRs were applied to the GPC-population. ‘Referral to emergency department (ED)’ was used as a proxy outcome measure for ‘serious infection’. CPR performance was assessed by calibration analyses, sensitivity, specificity, and area under the ROC-curve (ROC-area). Results A total of 9794 GPC-contacts were eligible, 54% male, median age 2.3 years (interquartile range 1.0–4.6 years) and 8.1% referred to ED. Frequencies of CPR-variables varied from 0.5% (cyanosis, drowsy) to 25% (temperature ≥40°C). Alarm signs frequently included in CPRs were ‘ill appearance’, ‘inconsolable’, and ‘abnormal circulatory or respiratory signs’. The height of the CPR’s predicted risks generally corresponded with being (or not being) referred to the ED in practice. However, calibration-slopes indicated that three CPRs underestimated the risk of serious infection in the GPC-population. Sensitivities ranged from 42% to 54%, specificities from 68% to 89%. ROC-areas ranged from 0.52 to 0.81, with best performance of CPRs for children aged <3 months. Conclusion Published CPRs performed moderately well in the primary out-of-hours care population. Advice is given on how to improve translation of CPRs to primary care practice. PMID:25824182

  4. Can dosimetric parameters predict acute hematologic toxicity in rectal cancer patients treated with intensity-modulated pelvic radiotherapy?

    International Nuclear Information System (INIS)

    To identify dosimetric parameters associated with acute hematologic toxicity (HT) in rectal cancer patients undergoing concurrent chemotherapy and intensity-modulated pelvic radiotherapy. Ninety-three rectal cancer patients receiving concurrent capecitabine and pelvic intensity-modulated radiation therapy (IMRT) were analyzed. Pelvic bone marrow (PBM) was contoured for each patient and divided into three subsites: lumbosacral spine (LSS), ilium, and lower pelvis (LP). The volume of each site receiving 5–40 Gy (V 5, V10, V15, V20, V30, and V40, respectively) as well as patient baseline clinical characteristics was calculated. The endpoint for hematologic toxicity was grade ≥ 2 (HT2+) leukopenia, neutropenia, anemia or thrombocytopenia. Logistic regression was used to analyze correlation between dosimetric parameters and grade ≥ 2 hematologic toxicity. Twenty-four in ninety-three patients experienced grade ≥ 2 hematologic toxicity. Only the dosimetric parameter V40 of lumbosacral spine was correlated with grade ≥ 2 hematologic toxicity. Increased pelvic lumbosacral spine V40 (LSS-V40) was associated with an increased grade ≥ 2 hematologic toxicity (p = 0.041). Patients with LSS-V40 ≥ 60 % had higher rates of grade ≥ 2 hematologic toxicity than did patients with lumbosacral spine V40 < 60 % (38.3 %, 18/47 vs.13 %, 6/46, p =0.005). On univariate and multivariate logistic regression analysis, lumbosacral spine V40 and gender was also the variable associated with grade ≥ 2 hematologic toxicity. Female patients were observed more likely to have grade ≥ 2 hematologic toxicity than male ones (46.9 %, 15/32 vs 14.8 %, 9/61, p =0.001). Lumbosacral spine -V40 was associated with clinically significant grade ≥ 2 hematologic toxicity. Keeping the lumbosacral spine -V40 < 60 % was associated with a 13 % risk of grade ≥ 2 hematologic toxicity in rectal cancer patients undergoing concurrent chemoradiotherapy

  5. Non-Responders to Intravenous Immunoglobulin and Coronary Artery Dilatation in Kawasaki Disease: Predictive Parameters in Korean Children

    Science.gov (United States)

    Kim, Bo Young; Kim, Dongwan; Kim, Yong Hyun; Ryoo, Eell; Sun, Yong Han; Jeon, In-sang; Jung, Mi-Jin; Cho, Hye Kyung; Tchah, Hann; Choi, Deok Young

    2016-01-01

    Background and Objectives In Kawasaki disease (KD), high dose intravenous immunoglobulin (IVIG) significantly lowers the coronary complications. However, some patients either do not respond to initial therapy or develop coronary complications. We aimed to identify the predictive factors for unresponsiveness to initial IVIG therapy and coronary artery dilatation (CAD; defined by Z-score≥2.5) in the acute phase and convalescent phase. Subjects and Methods A retrospective review was conducted of 703 patients with KD, admitted to Gachon University Gil Medical Center between January 2005 and June 2013. The patients were divided into two groups—IVIG responders vs. non-responders—based on the IVIG treatments, and presence of fever after treatment. Further, these groups were divided into two subgroups based on their CAD. Results Among the 703 patients with KD, the rate of non-responders to initial IVIG was 16.8%. Serum total bilirubin, platelet count, and neutrophil proportion were independent predictive parameters of unresponsiveness (p<0.05). CAD was found in 234 patients (33.3%) in the acute phase, and in 32 patients (4.6%) in the convalescent phase. Male gender, fever duration, serum C-reactive protein, and white blood cell count were related to CAD (p<0.05). CAD was detected more frequently in non-responders than in the responders (47.5% vs. 31.5%, p=0.001). Kobayashi, Egami, and Sano scoring systems applied to our study population reflected low sensitivities (28.0-33.9%). Conclusion Several independent parameters were related to unresponsiveness to the initial IVIG or CAD. These parameters might be helpful in establishing more focused and careful monitoring of high-risk KD patients in Korea.

  6. 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.)

  7. Prediction of clinical factors associated with pandemic influenza A (H1N1 2009 in Pakistan.

    Directory of Open Access Journals (Sweden)

    Nadia Nisar

    Full Text Available BACKGROUND: Influenza is a viral infection that can lead to serious complications and death(s in vulnerable groups if not diagnosed and managed in a timely manner. This study was conducted to improve the accuracy of predicting influenza through various clinical and statistical models. METHODOLOGY: A retrospective cross sectional analysis was done on demographic and epidemiological data collected from March 2009 to March 2010. Patients were classified as ILI or SARI using WHO case definitions. Respiratory specimens were tested by RT-PCR. Clinical symptoms and co-morbid conditions were analyzed using binary logistic regression models. RESULTS: In the first approach, analysis compared children (≤12 and adults (>12. Of 1,243 cases, 262 (21% tested positive for A(H1N1pdm09 and the proportion of children (≤12 and adults (>12 were 27% and 73% respectively. Four symptoms predicted influenza in children: fever (OR 2.849, 95% CI 1.931-8.722, cough (OR 1.99, 95% CI 1.512-3.643, diarrhea (OR 2.100, 95% CI 2.040-3.25 and respiratory disease (OR 3.269, 95% CI 2.128-12.624. In adults, the strongest clinical predictor was fever (OR 2.80, 95% CI 1.025-3.135 followed by cough (OR 1.431, 95% CI 1.032-2.815. In the second instance, patients were separated into two groups: SARI 326 (26% and ILI 917 (74% cases. Male to female ratio was 1.41∶1.12 for SARI and 2∶1.5 for ILI cases. Chi-square test showed that fever, cough and sore throat were significant factors for A(H1N1pdm09 infections (p = 0.008. CONCLUSION: Studies in a primary care setting should be encouraged focused on patients with influenza-like illness to develop sensitive clinical case definition that will help to improve accuracy of detecting influenza infections. Formulation of a standard "one size fits all" case definition that best correlates with influenza infections can help guide decisions for additional diagnostic testing and also discourage unjustified antibiotic prescription and usage

  8. Artificial neural network approach to predicting engine-out emissions and performance parameters of a turbo charged diesel engine

    Directory of Open Access Journals (Sweden)

    Özener Orkun

    2013-01-01

    Full Text Available This study details the artificial neural network (ANN modelling of a diesel engine to predict the torque, power, brake-specific fuel consumption and pollutant emissions, including carbon dioxide, carbon monoxide, nitrogen oxides, total hydrocarbons and filter smoke number. To collect data for training and testing the neural network, experiments were performed on a four cylinder, four stroke compression ignition engine. A total of 108 test points were run on a dynamometer. For the first part of this work, a parameter packet was used as the inputs for the neural network, and satisfactory regression was found with the outputs (over ~95%, excluding total hydrocarbons. The second stage of this work addressed developing new networks with additional inputs for predicting the total hydrocarbons, and the regression was raised from 75 % to 90 %. This study shows that the ANN approach can be used for accurately predicting characteristic values of an internal combustion engine and that the neural network performance can be increased using additional related input data.

  9. [Rheumatic activity and clinico-pathologic dissociation. Clinical and pathologic parameters in rheumatic heart disease].

    Science.gov (United States)

    Bernal, E; Maas, M; Osornio, A; Reyes, P A

    1987-01-01

    We studied atrial appendages and valvular apparatus from patients undergoing cardiac surgery for rheumatic heart disease, looking for active histologic lesions. After reviewing 673 specimens (1980-1985) we studied two groups: 31 cases with Aschoff nodules or ill-differentiated histopathological lesions, and 31 cases, without tissular inflammatory abnormalities. In the former we found 8 cases with suspected clinical activity in a 3 months period before surgery, in the latter only 2 cases had similar findings. The Jones criteria are not useful for recognizing rheumatic activity among patients with chronic rheumatic heart disease, there is no clinical-histopathological correlations and it is possible that chronic inflammation occurs at the heart as an organ-limited condition. PMID:2952088

  10. Statistical analysis of general, clinical and radiographic parameters of navicular disease in the horse

    International Nuclear Information System (INIS)

    With 20,4 % the syndrome navicular disease has a remarkable part of lameness in the equine fore limb. Although intensive investigations of the last years, there is no uniform opinion in the clinical diagnosis of navicular disease. At first there is a differential description of the diagnosis of navicular disease and a presentation of the importance of this disease from the whole patients of the equine hospital of the Tierärztliche Hochschule Hannover about five years (1980-1984). After that, the importance of the identification of pain with pain provocation test and the elimination of pain by anesthetic nerve blocking are described. The result of the anesthetic block of the ''Ramus pulvinus'' of the medial and lateral palmar digital nerves is a necessary part in clinical diagnosis of navicular disease

  11. Clinical Dementia Rating Performed Several Years prior to Death Predicts Regional Alzheimer’s Neuropathology

    Science.gov (United States)

    Beeri, Michal Schnaider; Silverman, Jeremy M.; Schmeidler, James; Wysocki, Michael; Grossman, Hillel Z.; Purohit, Dushyant P.; Perl, Daniel P.; Haroutunian, Vahram

    2011-01-01

    Aims To assess the relationships between early and late antemortem measures of dementia severity and Alzheimer disease (AD) neuropathology severity. Methods 40 residents of a nursing home, average age at death 82.0, participated in this longitudinal cohort study with postmortem assessment. Severity of dementia was measured by Clinical Dementia Rating (CDR) at two time points, averaging 4.5 and 1.0 years before death. Densities of postmortem neuritic plaques (NPs) and neurofibrillary tangles (NFTs) were measured in the cerebral cortex, hippocampus, and entorhinal cortex. Results For most brain areas, both early and late CDRs were significantly associated with NPs and NFTs. CDRs assessed proximal to death predicted NFTs beyond the contribution of early CDRs. NPs were predicted by both early and late CDRs. NPs were predictive of both early and late CDRs after controlling for NFTs. NFTs were only associated significantly with late CDR in the cerebral cortex after controlling for NPs. Conclusions Even if assessed several years before death, dementia severity is associated with AD neuropathology. NPs are more strongly associated with dementia severity than NFTs. NFTs consistently associate better with late than early CDR, suggesting that these neuropathological changes may occur relatively later in the course of the disease. PMID:18367838

  12. Cross-sectional imaging for diagnosis and clinical outcome prediction of acute basilar artery thrombosis

    Energy Technology Data Exchange (ETDEWEB)

    Mortimer, A.M., E-mail: alex_mortimer@hotmail.co [Severn School of Radiology, Bristol (United Kingdom); Department of Radiology, Great Western Hospital, Swindon (United Kingdom); Saunders, T.; Cook, J.-L. [Department of Radiology, Great Western Hospital, Swindon (United Kingdom)

    2011-06-15

    Basilar artery occlusion is a potentially fatal condition and imaging findings can be subtle. Prompt diagnosis is vital, as recognition may lead to therapeutic recanalization that may improve functional outcome and survival. Furthermore, cross-sectional imaging signs may help predict eventual outcome and, therefore, guide which patients should be subjected to aggressive treatment. Computed tomography (CT) signs include a hyperdense basilar artery that has a high specificity, accuracy, positive and negative predictive value. Evidence regarding the prognostic significance of the hyperdense basilar artery sign is conflicting. Early magnetic resonance imaging (MRI) features include loss of flow void, seen as increased signal intensity within the basilar artery on T2-weigted images and identification of acute thrombus, seen as intermediate signal on T1-weighted images. MRI sequences are more sensitive for early detection of acute ischaemia or infarction, ideally with diffusion-weighted imaging (DWI). Both CT and MR angiography are sensitive for detection of acute thrombus, seen as a filling defect or occlusion. These are the non-invasive imaging techniques of choice to confirm diagnosis, with perhaps the speed and accessibility of CT angiography resulting in this technique being valuable in the acute setting. Several new scoring systems based on arterial segmentation rather than global volume assessment using CT angiography source images and DWI have shown early promise in the prediction of eventual clinical outcome in order to isolate those patients who may benefit from therapeutic recanalization.

  13. A Clinical Indications Prediction Scale Based on TWIST1 for Human Mesenchymal Stem Cells

    Directory of Open Access Journals (Sweden)

    Siddaraju V. Boregowda

    2016-02-01

    Full Text Available In addition to their stem/progenitor properties, mesenchymal stem cells (MSCs also exhibit potent effector (angiogenic, antiinflammatory, immuno-modulatory functions that are largely paracrine in nature. It is widely believed that effector functions underlie most of the therapeutic potential of MSCs and are independent of their stem/progenitor properties. Here we demonstrate that stem/progenitor and effector functions are coordinately regulated at the cellular level by the transcription factor Twist1 and specified within populations according to a hierarchical model. We further show that manipulation of Twist1 levels by genetic approaches or by exposure to widely used culture supplements including fibroblast growth factor 2 (Ffg2 and interferon gamma (IFN-gamma alters MSC efficacy in cell-based and in vivo assays in a predictable manner. Thus, by mechanistically linking stem/progenitor and effector functions our studies provide a unifying framework in the form of an MSC hierarchy that models the functional complexity of populations. Using this framework, we developed a CLinical Indications Prediction (CLIP scale that predicts how donor-to-donor heterogeneity and culture conditions impact the therapeutic efficacy of MSC populations for different disease indications.

  14. A Clinical Indications Prediction Scale Based on TWIST1 for Human Mesenchymal Stem Cells.

    Science.gov (United States)

    Boregowda, Siddaraju V; Krishnappa, Veena; Haga, Christopher L; Ortiz, Luis A; Phinney, Donald G

    2016-02-01

    In addition to their stem/progenitor properties, mesenchymal stem cells (MSCs) also exhibit potent effector (angiogenic, antiinflammatory, immuno-modulatory) functions that are largely paracrine in nature. It is widely believed that effector functions underlie most of the therapeutic potential of MSCs and are independent of their stem/progenitor properties. Here we demonstrate that stem/progenitor and effector functions are coordinately regulated at the cellular level by the transcription factor Twist1 and specified within populations according to a hierarchical model. We further show that manipulation of Twist1 levels by genetic approaches or by exposure to widely used culture supplements including fibroblast growth factor 2 (Ffg2) and interferon gamma (IFN-gamma) alters MSC efficacy in cell-based and in vivo assays in a predictable manner. Thus, by mechanistically linking stem/progenitor and effector functions our studies provide a unifying framework in the form of an MSC hierarchy that models the functional complexity of populations. Using this framework, we developed a CLinical Indications Prediction (CLIP) scale that predicts how donor-to-donor heterogeneity and culture conditions impact the therapeutic efficacy of MSC populations for different disease indications. PMID:26981553

  15. Tissue spectrophotometry and thermographic imaging applied to routine clinical prediction of amputation level viability

    Science.gov (United States)

    Hanson, Jon M.; Harrison, David K.; Hawthorn, Ian E.

    2002-06-01

    About 5% of British males over 50 years develop peripheral arterial occlusive disease. Of these about 2% ultimately require lower limb amputation. In 1995 we proposed a new technique using lightguide spectrophotometry to measure the oxygen saturation level of haemoglobin (SO2) in the skin as a method for predicting tissue viability. This technique, in combination with thermographic imaging, was compared with skin blood flow measurements using the I125)4- Iodoantipyrine (IAP) clearance technique. The optical techniques gave a sensitivity and selectivity of 1.0 for the prediction of successful outcome of a below knee amputation compared with a specificity of 93% using the traditional IAP technique at a below knee to above knee amputation ratio (BKA:AKA) of 75%. The present study assesses the routine clinical application of these optical techniques. The study is ongoing, but the data to date comprises 22 patients. 4 patients were recommended for above knee amputation (AKA) and 18 patients for below knee amputation on the basis of thermographic and tissue SO2 measurements. All but one of the predicted BKA amputations healed. The study to date produces evidence of 94% healing rate (specificity) for a BKA:AKA ratio of 82%. This compares favorably with the previous figures given above.

  16. Cross-sectional imaging for diagnosis and clinical outcome prediction of acute basilar artery thrombosis

    International Nuclear Information System (INIS)

    Basilar artery occlusion is a potentially fatal condition and imaging findings can be subtle. Prompt diagnosis is vital, as recognition may lead to therapeutic recanalization that may improve functional outcome and survival. Furthermore, cross-sectional imaging signs may help predict eventual outcome and, therefore, guide which patients should be subjected to aggressive treatment. Computed tomography (CT) signs include a hyperdense basilar artery that has a high specificity, accuracy, positive and negative predictive value. Evidence regarding the prognostic significance of the hyperdense basilar artery sign is conflicting. Early magnetic resonance imaging (MRI) features include loss of flow void, seen as increased signal intensity within the basilar artery on T2-weigted images and identification of acute thrombus, seen as intermediate signal on T1-weighted images. MRI sequences are more sensitive for early detection of acute ischaemia or infarction, ideally with diffusion-weighted imaging (DWI). Both CT and MR angiography are sensitive for detection of acute thrombus, seen as a filling defect or occlusion. These are the non-invasive imaging techniques of choice to confirm diagnosis, with perhaps the speed and accessibility of CT angiography resulting in this technique being valuable in the acute setting. Several new scoring systems based on arterial segmentation rather than global volume assessment using CT angiography source images and DWI have shown early promise in the prediction of eventual clinical outcome in order to isolate those patients who may benefit from therapeutic recanalization.

  17. Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

    Science.gov (United States)

    Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

    2016-01-01

    One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets. PMID:27532883

  18. Lyman-Kutcher-Burman NTCP model parameters for radia