Data-Driven Nonlinear Subspace Modeling for Prediction and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

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

Zhou, Ping; Song, Heda; Wang, Hong

Blast furnace (BF) in ironmaking is a nonlinear dynamic process with complicated physical-chemical reactions, where multi-phase and multi-field coupling and large time delay occur during its operation. In BF operation, the molten iron temperature (MIT) as well as Si, P and S contents of molten iron are the most essential molten iron quality (MIQ) indices, whose measurement, modeling and control have always been important issues in metallurgic engineering and automation field. This paper develops a novel data-driven nonlinear state space modeling for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques. First, to improvemore » modeling efficiency, a data-driven hybrid method combining canonical correlation analysis and correlation analysis is proposed to identify the most influential controllable variables as the modeling inputs from multitudinous factors would affect the MIQ indices. Then, a Hammerstein model for the prediction of MIQ indices is established using the LS-SVM based nonlinear subspace identification method. Such a model is further simplified by using piecewise cubic Hermite interpolating polynomial method to fit the complex nonlinear kernel function. Compared to the original Hammerstein model, this simplified model can not only significantly reduce the computational complexity, but also has almost the same reliability and accuracy for a stable prediction of MIQ indices. Last, in order to verify the practicability of the developed model, it is applied in designing a genetic algorithm based nonlinear predictive controller for multivariate MIQ indices by directly taking the established model as a predictor. Industrial experiments show the advantages and effectiveness of the proposed approach.« less

Does investor ownership of nursing homes compromise the quality of care?

PubMed

Harrington, C; Woolhandler, S; Mullan, J; Carrillo, H; Himmelstein, D U

2001-09-01

Two thirds of nursing homes are investor owned. This study examined whether investor ownership affects quality. We analyzed 1998 data from state inspections of 13,693 nursing facilities. We used a multivariate model and controlled for case mix, facility characteristics, and location. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5% higher than nonprofit facilities and 43.0% higher than public facilities. In multivariate analysis, investor ownership predicted 0.679 additional deficiencies per home; chain ownership predicted an additional 0.633 deficiencies. Nurse staffing was lower at investor-owned nursing homes. Investor-owned nursing homes provide worse care and less nursing care than do not-for-profit or public homes.

Attitudes and exercise adherence: test of the Theories of Reasoned Action and Planned Behaviour.

PubMed

Smith, R A; Biddle, S J

1999-04-01

Three studies of exercise adherence and attitudes are reported that tested the Theory of Reasoned Action and the Theory of Planned Behaviour. In a prospective study of adherence to a private fitness club, structural equation modelling path analysis showed that attitudinal and social normative components of the Theory of Reasoned Action accounted for 13.1% of the variance in adherence 4 months later, although only social norm significantly predicted intention. In a second study, the Theory of Planned Behaviour was used to predict both physical activity and sedentary behaviour. Path analyses showed that attitude and perceived control, but not social norm, predicted total physical activity. Physical activity was predicted from intentions and control over sedentary behaviour. Finally, an intervention study with previously sedentary adults showed that intentions to be active measured at the start and end of a 10-week intervention were associated with the planned behaviour variables. A multivariate analysis of variance revealed no significant multivariate effects for time on the planned behaviour variables measured before and after intervention. Qualitative data provided evidence that participants had a positive experience on the intervention programme and supported the role of social normative factors in the adherence process.

Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

PubMed Central

Coelho, Antonio Augusto Rodrigues

2016-01-01

This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

Risk models for post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP): smoking and chronic liver disease are predictors of protection against PEP.

PubMed

DiMagno, Matthew J; Spaete, Joshua P; Ballard, Darren D; Wamsteker, Erik-Jan; Saini, Sameer D

2013-08-01

We investigated which variables independently associated with protection against or development of postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) and severity of PEP. Subsequently, we derived predictive risk models for PEP. In a case-control design, 6505 patients had 8264 ERCPs, 211 patients had PEP, and 22 patients had severe PEP. We randomly selected 348 non-PEP controls. We examined 7 established- and 9 investigational variables. In univariate analysis, 7 variables predicted PEP: younger age, female sex, suspected sphincter of Oddi dysfunction (SOD), pancreatic sphincterotomy, moderate-difficult cannulation (MDC), pancreatic stent placement, and lower Charlson score. Protective variables were current smoking, former drinking, diabetes, and chronic liver disease (CLD, biliary/transplant complications). Multivariate analysis identified seven independent variables for PEP, three protective (current smoking, CLD-biliary, CLD-transplant/hepatectomy complications) and 4 predictive (younger age, suspected SOD, pancreatic sphincterotomy, MDC). Pre- and post-ERCP risk models of 7 variables have a C-statistic of 0.74. Removing age (seventh variable) did not significantly affect the predictive value (C-statistic of 0.73) and reduced model complexity. Severity of PEP did not associate with any variables by multivariate analysis. By using the newly identified protective variables with 3 predictive variables, we derived 2 risk models with a higher predictive value for PEP compared to prior studies.

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

PubMed Central

Daws, Richard E.; Soreq, Eyal; Johnson, Eileanoir B.; Scahill, Rachael I.; Tabrizi, Sarah J.; Barker, Roger A.; Hampshire, Adam

2018-01-01

Objective Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD. Method A multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy. Results Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models. Interpretation We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532–543 PMID:29405351

Does Investor Ownership of Nursing Homes Compromise the Quality of Care?

PubMed Central

Harrington, Charlene; Woolhandler, Steffie; Mullan, Joseph; Carrillo, Helen; Himmelstein, David U.

2001-01-01

Objectives. Two thirds of nursing homes are investor owned. This study examined whether investor ownership affects quality. Methods. We analyzed 1998 data from state inspections of 13 693 nursing facilities. We used a multivariate model and controlled for case mix, facility characteristics, and location. Results. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5% higher than nonprofit facilities and 43.0% higher than public facilities. In multivariate analysis, investor ownership predicted 0.679 additional deficiencies per home; chain ownership predicted an additional 0.633 deficiencies. Nurse staffing was lower at investor-owned nursing homes. Conclusions. Investor-owned nursing homes provide worse care and less nursing care than do not-for-profit or public homes. PMID:11527781

Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

PubMed

Westman, Eric; Aguilar, Carlos; Muehlboeck, J-Sebastian; Simmons, Andrew

2013-01-01

Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

Supporting inquiry learning by promoting normative understanding of multivariable causality

NASA Astrophysics Data System (ADS)

Keselman, Alla

2003-11-01

Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to facilitate students' metalevel and performance-level inquiry skills by enhancing their understanding of multivariable causality. Relative to an exploration-only group, sixth graders who practiced predicting an outcome (earthquake risk) based on multiple factors demonstrated increased attention to evidence, improved metalevel appreciation of effective strategies, and a trend toward consistent use of a controlled comparison strategy. Sixth graders who also received explicit instruction in making predictions based on multiple factors showed additional improvement in their ability to compare multiple instances as a basis for inferences and constructed the most accurate knowledge of the system. Gains were maintained in transfer tasks. The cognitive skills and metalevel understanding examined here are essential to inquiry learning.

Screening for Sleep Apnoea in Mild Cognitive Impairment: The Utility of the Multivariable Apnoea Prediction Index

PubMed Central

Wilson, Georgina; Terpening, Zoe; Wong, Keith; Grunstein, Ron; Norrie, Louisa; Lewis, Simon J. G.; Naismith, Sharon L.

2014-01-01

Purpose. Mild cognitive impairment (MCI) is considered an “at risk” state for dementia and efforts are needed to target modifiable risk factors, of which Obstructive sleep apnoea (OSA) is one. This study aims to evaluate the predictive utility of the multivariate apnoea prediction index (MAPI), a patient self-report survey, to assess OSA in MCI. Methods. Thirty-seven participants with MCI and 37 age-matched controls completed the MAPI and underwent polysomnography (PSG). Correlations were used to compare the MAPI and PSG measures including oxygen desaturation index and apnoea-hypopnoea index (AHI). Receiver-operating characteristics (ROC) curve analyses were performed using various cut-off scores for apnoea severity. Results. In controls, there was a significant moderate correlation between higher MAPI scores and more severe apnoea (AHI: r = 0.47, P = 0.017). However, this relationship was not significant in the MCI sample. ROC curve analysis indicated much lower area under the curve (AUC) in the MCI sample compared to the controls across all AHI severity cut-off scores. Conclusions. In older people, the MAPI moderately correlates with AHI severity but only in those who are cognitively intact. Development of further screening tools is required in order to accurately screen for OSA in MCI. PMID:24551457

What matters? Assessing and developing inquiry and multivariable reasoning skills in high school chemistry

NASA Astrophysics Data System (ADS)

Daftedar Abdelhadi, Raghda Mohamed

Although the Next Generation Science Standards (NGSS) present a detailed set of Science and Engineering Practices, a finer grained representation of the underlying skills is lacking in the standards document. Therefore, it has been reported that teachers are facing challenges deciphering and effectively implementing the standards, especially with regards to the Practices. This analytical study assessed the development of high school chemistry students' (N = 41) inquiry, multivariable causal reasoning skills, and metacognition as a mediator for their development. Inquiry tasks based on concepts of element properties of the periodic table as well as reaction kinetics required students to conduct controlled thought experiments, make inferences, and declare predictions of the level of the outcome variable by coordinating the effects of multiple variables. An embedded mixed methods design was utilized for depth and breadth of understanding. Various sources of data were collected including students' written artifacts, audio recordings of in-depth observational groups and interviews. Data analysis was informed by a conceptual framework formulated around the concepts of coordinating theory and evidence, metacognition, and mental models of multivariable causal reasoning. Results of the study indicated positive change towards conducting controlled experimentation, making valid inferences and justifications. Additionally, significant positive correlation between metastrategic and metacognitive competencies, and sophistication of experimental strategies, signified the central role metacognition played. Finally, lack of consistency in indicating effective variables during the multivariable prediction task pointed towards the fragile mental models of multivariable causal reasoning the students had. Implications for teacher education, science education policy as well as classroom research methods are discussed. Finally, recommendations for developing reform-based chemistry curricula based on the Practices are presented.

The prognostic value of panoramic radiography of inferior alveolar nerve damage after mandibular third molar removal: retrospective study of 400 cases.

PubMed

Szalma, József; Lempel, Edina; Jeges, Sára; Szabó, Gyula; Olasz, Lajos

2010-02-01

The aim of the study was to estimate the accuracy of panoramic radiographic signs predicting inferior alveolar nerve (IAN) paresthesia after lower third molar removal. In a case-control study the sample was composed of 41 cases with postoperative IAN paresthesia and 359 control cases without it. The collected data included "classic" specific signs indicating a close spatial relationship between third molar root and inferior alveolar canal (IAC), root curvatures, and the extent of IAC-root tip overlap. Bivariate and multivariate logistic regression analyses were completed to estimate the association between radiographic findings and IAN paresthesia. The multivariate logistic analysis identified 3 signs significantly associated with IAN paresthesia (P < .001): interruption of the superior cortex of the canal wall, diversion of the canal, and darkening of the root. The sensitivities and specificities ranged from 14.6% to 68.3% and from 85.5% to 96.9%, respectively. The positive predictive values, calculated to factor a 1.1% prevalence of paresthesia, ranged from 3.6% to 10.9%, whereas the negative predictive values >99%. Panoramic radiography is an inadequate screening method for predicting IAN paresthesia after mandibular third molar removal. Copyright (c) 2010 Mosby, Inc. All rights reserved.

Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon.

PubMed

Elghafghuf, Adel; Vanderstichel, Raphael; St-Hilaire, Sophie; Stryhn, Henrik

2018-04-11

Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses. In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Clinical predictors of rectal lymphogranuloma venereum infection: results from a multicentre case–control study in the UK

PubMed Central

Pallawela, S N S; Sullivan, A K; Macdonald, N; French, P; White, J; Dean, G; Smith, A; Winter, A J; Mandalia, S; Alexander, S; Ison, C; Ward, H

2014-01-01

Objective Since 2003, over 2000 cases of lymphogranuloma venereum (LGV) have been diagnosed in the UK in men who have sex with men (MSM). Most cases present with proctitis, but there are limited data on how to differentiate clinically between LGV and other pathology. We analysed the clinical presentations of rectal LGV in MSM to identify clinical characteristics predictive of LGV proctitis and produced a clinical prediction model. Design A prospective multicentre case–control study was conducted at six UK hospitals from 2008 to 2010. Cases of rectal LGV were compared with controls with rectal symptoms but without LGV. Methods Data from 98 LGV cases and 81 controls were collected from patients and clinicians using computer-assisted self-interviews and clinical report forms. Univariate and multivariate logistic regression was used to compare symptoms and signs. Clinical prediction models for LGV were compared using receiver operating curves. Results Tenesmus, constipation, anal discharge and weight loss were significantly more common in cases than controls. In multivariate analysis, tenesmus and constipation alone were suggestive of LGV (OR 2.98, 95% CI 0.99 to 8.98 and 2.87, 95% CI 1.01 to 8.15, respectively) and that tenesmus alone or in combination with constipation was a significant predictor of LGV (OR 6.97, 95% CI 2.71 to 17.92). The best clinical prediction was having one or more of tenesmus, constipation and exudate on proctoscopy, with a sensitivity of 77% and specificity of 65%. Conclusions This study indicates that tenesmus alone or in combination with constipation makes a diagnosis of LGV in MSM presenting with rectal symptoms more likely. PMID:24687130

Characterization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate Bayesian approach.

PubMed

Fu, Zhibiao; Baker, Daniel; Cheng, Aili; Leighton, Julie; Appelbaum, Edward; Aon, Juan

2016-05-01

The principle of quality by design (QbD) has been widely applied to biopharmaceutical manufacturing processes. Process characterization is an essential step to implement the QbD concept to establish the design space and to define the proven acceptable ranges (PAR) for critical process parameters (CPPs). In this study, we present characterization of a Saccharomyces cerevisiae fermentation process using risk assessment analysis, statistical design of experiments (DoE), and the multivariate Bayesian predictive approach. The critical quality attributes (CQAs) and CPPs were identified with a risk assessment. The statistical model for each attribute was established using the results from the DoE study with consideration given to interactions between CPPs. Both the conventional overlapping contour plot and the multivariate Bayesian predictive approaches were used to establish the region of process operating conditions where all attributes met their specifications simultaneously. The quantitative Bayesian predictive approach was chosen to define the PARs for the CPPs, which apply to the manufacturing control strategy. Experience from the 10,000 L manufacturing scale process validation, including 64 continued process verification batches, indicates that the CPPs remain under a state of control and within the established PARs. The end product quality attributes were within their drug substance specifications. The probability generated with the Bayesian approach was also used as a tool to assess CPP deviations. This approach can be extended to develop other production process characterization and quantify a reliable operating region. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:799-812, 2016. © 2016 American Institute of Chemical Engineers.

Model predictive control system and method for integrated gasification combined cycle power generation

DOEpatents

Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa

2013-04-09

Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

PubMed

Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

2017-02-01

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

The Impact of Asking Intention or Self-Prediction Questions on Subsequent Behavior

PubMed Central

Wood, Chantelle; Conner, Mark; Miles, Eleanor; Sandberg, Tracy; Taylor, Natalie; Godin, Gaston; Sheeran, Paschal

2015-01-01

The current meta-analysis estimated the magnitude of the impact of asking intention and self-prediction questions on rates of subsequent behavior, and examined mediators and moderators of this question–behavior effect (QBE). Random-effects meta-analysis on 116 published tests of the effect indicated that intention/prediction questions have a small positive effect on behavior (d+ = 0.24). Little support was observed for attitude accessibility, cognitive dissonance, behavioral simulation, or processing fluency explanations of the QBE. Multivariate analyses indicated significant effects of social desirability of behavior/behavior domain (larger effects for more desirable and less risky behaviors), difficulty of behavior (larger effects for easy-to-perform behaviors), and sample type (larger effects among student samples). Although this review controls for co-occurrence of moderators in multivariate analyses, future primary research should systematically vary moderators in fully factorial designs. Further primary research is also needed to unravel the mechanisms underlying different variants of the QBE. PMID:26162771

Predictive Utility of Brief Alcohol Use Disorders Identification Test (AUDIT) for human immunodeficiency virus antiretroviral medication nonadherence.

PubMed

Broyles, Lauren Matukaitis; Gordon, Adam J; Sereika, Susan M; Ryan, Christopher M; Erlen, Judith A

2011-10-01

Alcohol use negatively affects adherence to antiretroviral therapy (ART), thus human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) care providers need accurate, efficient assessments of alcohol use. Using existing data from an efficacy trial of 2 cognitive-behavioral ART adherence interventions, the authors sought to determine if results on 2 common alcohol screening tests (Alcohol Use Disorders Identification Test--Consumption [AUDIT-C] and its binge-related question [AUDIT-3]) predict ART nonadherence. Twenty-seven percent of the sample (n = 308) were positive on the AUDIT-C and 34% were positive on the AUDIT-3. In multivariate analyses, AUDIT-C-positive status predicted ART nonadherence after controlling for race, age, conscientiousness, and self-efficacy (P = .036). Although AUDIT-3-positive status was associated with ART nonadherence in unadjusted analyses, this relationship was not maintained in the final multivariate model. The AUDIT-C shows potential as an indirect screening tool for both at-risk drinking and ART nonadherence, underscoring the relationship between alcohol and chronic disease management.

Predictive Utility of Brief AUDIT for HIV Antiretroviral Medication Nonadherence

PubMed Central

Broyles, Lauren Matukaitis; Gordon, Adam J.; Sereika, Susan M.; Ryan, Christopher M.; Erlen, Judith A.

2012-01-01

Alcohol use negatively affects adherence to antiretroviral therapy (ART), thus HIV/AIDS providers need accurate, efficient assessments of alcohol use. Using existing data from an efficacy trial of two cognitive-behavioral ART adherence interventions, we sought to determine if results on two common alcohol screening tests (Alcohol Use Disorders Identification Test—Consumption (AUDIT-C) and its binge-related question (AUDIT-3)) predict ART nonadherence. Twenty seven percent of the sample (n=308) were positive on the AUDIT-C and 34% were positive on the AUDIT-3. In multivariate analyses, AUDIT-C positive status predicted ART nonadherence after controlling for race, age, conscientiousness, and self-efficacy (p=.036). While AUDIT-3 positive status was associated with ART nonadherence in unadjusted analyses, this relationship was not maintained in the final multivariate model. The AUDIT-C shows potential as an indirect screening tool for both at-risk drinking and ART nonadherence, underscoring the relationship between alcohol and chronic disease management. PMID:22014256

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

NASA Astrophysics Data System (ADS)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

Robust predictive control with optimal load tracking for critical applications. Final report

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

Tse, J.; Bentsman, J.; Miller, N.

1994-09-01

This report derives a multi-input multi-output (MIMO) version of a two-degree-of-freedom receding-horizon control law based on mixed H{sub 2}/H{infinity} minimization. First, the integrand in the frequency domain representation of the MIMO performance criterion is decomposed into disturbance and reference spectra. Then the controller is derived which minimizes the peak of the disturbance spectrum and the integral of the reference spectrum on the unit circle. The resulting two-degree-of-freedom MIMO control strategy, referred to as the minimax predictive multivariable control (MPC), is shown to have worst-case-disturbance-rejection and robust-stability properties superior to those of purely H{sub 2}-optimal controllers, such as Generalized Predictive Controlmore » (GPC), for identical horizons. An attractive feature of the receding horizon structure of MPC is that it can, in ways similar to GPC, directly incorporate input constraints and pre-programmed reference inputs, which are nontrivial tasks in the standard H{infinity} design.« less

Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

NASA Astrophysics Data System (ADS)

Teye, Ernest; Huang, Xingyi; Dai, Huang; Chen, Quansheng

2013-10-01

Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

The Irvine, Beatties, and Bresnahan (IBB) Forelimb Recovery Scale: An Assessment of Reliability and Validity

PubMed Central

Irvine, Karen-Amanda; Ferguson, Adam R.; Mitchell, Kathleen D.; Beattie, Stephanie B.; Lin, Amity; Stuck, Ellen D.; Huie, J. Russell; Nielson, Jessica L.; Talbott, Jason F.; Inoue, Tomoo; Beattie, Michael S.; Bresnahan, Jacqueline C.

2014-01-01

The IBB scale is a recently developed forelimb scale for the assessment of fine control of the forelimb and digits after cervical spinal cord injury [SCI; (1)]. The present paper describes the assessment of inter-rater reliability and face, concurrent and construct validity of this scale following SCI. It demonstrates that the IBB is a reliable and valid scale that is sensitive to severity of SCI and to recovery over time. In addition, the IBB correlates with other outcome measures and is highly predictive of biological measures of tissue pathology. Multivariate analysis using principal component analysis (PCA) demonstrates that the IBB is highly predictive of the syndromic outcome after SCI (2), and is among the best predictors of bio-behavioral function, based on strong construct validity. Altogether, the data suggest that the IBB, especially in concert with other measures, is a reliable and valid tool for assessing neurological deficits in fine motor control of the distal forelimb, and represents a powerful addition to multivariate outcome batteries aimed at documenting recovery of function after cervical SCI in rats. PMID:25071704

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

Accuracy of ultrasound for the prediction of placenta accreta.

PubMed

Bowman, Zachary S; Eller, Alexandra G; Kennedy, Anne M; Richards, Douglas S; Winter, Thomas C; Woodward, Paula J; Silver, Robert M

2014-08-01

Ultrasound has been reported to be greater than 90% sensitive for the diagnosis of accreta. Prior studies may be subject to bias because of single expert observers, suspicion for accreta, and knowledge of risk factors. We aimed to assess the accuracy of ultrasound for the prediction of accreta. Patients with accreta at a single academic center were matched to patients with placenta previa, but no accreta, by year of delivery. Ultrasound studies with views of the placenta were collected, deidentified, blinded to clinical history, and placed in random sequence. Six investigators prospectively interpreted each study for the presence of accreta and findings reported to be associated with its diagnosis. Sensitivity, specificity, positive predictive, negative predictive value, and accuracy were calculated. Characteristics of accurate findings were compared using univariate and multivariate analyses. Six investigators examined 229 ultrasound studies from 55 patients with accreta and 56 controls for 1374 independent observations. 1205/1374 (87.7% overall, 90% controls, 84.9% cases) studies were given a diagnosis. There were 371 (27.0%) true positives; 81 (5.9%) false positives; 533 (38.8%) true negatives, 220 (16.0%) false negatives, and 169 (12.3%) with uncertain diagnosis. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 53.5%, 88.0%, 82.1%, 64.8%, and 64.8%, respectively. In multivariate analysis, true positives were more likely to have placental lacunae (odds ratio [OR], 1.5; 95% confidence interval [CI], 1.4-1.6), loss of retroplacental clear space (OR, 2.4; 95% CI, 1.1-4.9), or abnormalities on color Doppler (OR, 2.1; 95% CI, 1.8-2.4). Ultrasound for the prediction of placenta accreta may not be as sensitive as previously described. Copyright © 2014 Mosby, Inc. All rights reserved.

SU-F-R-51: Radiomics in CT Perfusion Maps of Head and Neck Cancer

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

Nesteruk, M; Riesterer, O; Veit-Haibach, P

2016-06-15

Purpose: The aim of this study was to test the predictive value of radiomics features of CT perfusion (CTP) for tumor control, based on a preselection of radiomics features in a robustness study. Methods: 11 patients with head and neck cancer (HNC) and 11 patients with lung cancer were included in the robustness study to preselect stable radiomics parameters. Data from 36 HNC patients treated with definitive radiochemotherapy (median follow-up 30 months) was used to build a predictive model based on these parameters. All patients underwent pre-treatment CTP. 315 texture parameters were computed for three perfusion maps: blood volume, bloodmore » flow and mean transit time. The variability of texture parameters was tested with respect to non-standardizable perfusion computation factors (noise level and artery contouring) using intraclass correlation coefficients (ICC). The parameter with the highest ICC in the correlated group of parameters (inter-parameter Spearman correlations) was tested for its predictive value. The final model to predict tumor control was built using multivariate Cox regression analysis with backward selection of the variables. For comparison, a predictive model based on tumor volume was created. Results: Ten parameters were found to be stable in both HNC and lung cancer regarding potentially non-standardizable factors after the correction for inter-parameter correlations. In the multivariate backward selection of the variables, blood flow entropy showed a highly significant impact on tumor control (p=0.03) with concordance index (CI) of 0.76. Blood flow entropy was significantly lower in the patient group with controlled tumors at 18 months (p<0.1). The new model showed a higher concordance index compared to the tumor volume model (CI=0.68). Conclusion: The preselection of variables in the robustness study allowed building a predictive radiomics-based model of tumor control in HNC despite a small patient cohort. This model was found to be superior to the volume-based model. The project was supported by the KFSP Tumor Oxygenation of the University of Zurich, by a grant of the Center for Clinical Research, University and University Hospital Zurich and by a research grant from Merck (Schweiz) AG.« less

Multivariate statistical analysis of a high rate biofilm process treating kraft mill bleach plant effluent.

PubMed

Goode, C; LeRoy, J; Allen, D G

2007-01-01

This study reports on a multivariate analysis of the moving bed biofilm reactor (MBBR) wastewater treatment system at a Canadian pulp mill. The modelling approach involved a data overview by principal component analysis (PCA) followed by partial least squares (PLS) modelling with the objective of explaining and predicting changes in the BOD output of the reactor. Over two years of data with 87 process measurements were used to build the models. Variables were collected from the MBBR control scheme as well as upstream in the bleach plant and in digestion. To account for process dynamics, a variable lagging approach was used for variables with significant temporal correlations. It was found that wood type pulped at the mill was a significant variable governing reactor performance. Other important variables included flow parameters, faults in the temperature or pH control of the reactor, and some potential indirect indicators of biomass activity (residual nitrogen and pH out). The most predictive model was found to have an RMSEP value of 606 kgBOD/d, representing a 14.5% average error. This was a good fit, given the measurement error of the BOD test. Overall, the statistical approach was effective in describing and predicting MBBR treatment performance.

Characterizing the Associative Content of Brain Structures Involved in Habitual and Goal-Directed Actions in Humans: A Multivariate fMRI Study

PubMed Central

Liljeholm, Mimi; Zika, Ondrej; O'Doherty, John P.

2015-01-01

While there is accumulating evidence for the existence of distinct neural systems supporting goal-directed and habitual action selection in the mammalian brain, much less is known about the nature of the information being processed in these different brain regions. Associative learning theory predicts that brain systems involved in habitual control, such as the dorsolateral striatum, should contain stimulus and response information only, but not outcome information, while regions involved in goal-directed action, such as ventromedial and dorsolateral prefrontal cortex and dorsomedial striatum, should be involved in processing information about outcomes as well as stimuli and responses. To test this prediction, human participants underwent fMRI while engaging in a binary choice task designed to enable the separate identification of these different representations with a multivariate classification analysis approach. Consistent with our predictions, the dorsolateral striatum contained information about responses but not outcomes at the time of an initial stimulus, while the regions implicated in goal-directed action selection contained information about both responses and outcomes. These findings suggest that differential contributions of these regions to habitual and goal-directed behavioral control may depend in part on basic differences in the type of information that these regions have access to at the time of decision making. PMID:25740507

Prediction of UT1-UTC, LOD and AAM χ3 by combination of least-squares and multivariate stochastic methods

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Kosek, Wiesław

2008-02-01

This article presents the application of a multivariate prediction technique for predicting universal time (UT1-UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ 3). The multivariate predictions of LOD and UT1-UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ 3 time-series as the explanatory variable for the computation of LOD or UT1-UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ 3 data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ 3 predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1-UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1-UTC based on LS extrapolation or on LS + AR. In particular, the UT1-UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ 3 time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ 3 reveal the best accuracy for LS + AR.

Predictors of Arterial Blood Pressure Control During Deliberate Hypotension with Sodium Nitroprusside in Children

PubMed Central

Spielberg, David R; Barrett, Jeffrey S; Hammer, Gregory B; Drover, David R; Reece, Tammy; Cohane, Carol A; Schulman, Scott R

2014-01-01

Background Sodium nitroprusside (SNP) is used to decrease arterial blood pressure (BP) during certain surgical procedures. There are limited data regarding efficacy of BP control with SNP. There are no data on patient and clinician factors that affect BP control. We evaluated the dose-response relationship of SNP in infants and children undergoing major surgery and performed a quantitative assessment of BP control. Methods One hundred fifty-three subjects at 7 sites received a blinded infusion followed by open-label SNP during operative procedures requiring controlled hypotension. SNP was administered by continuous infusion and titrated to maintain BP control (mean arterial BP [MAP] within ±10% of clinician-defined target). BP was recorded using an arterial catheter. Statistical Process Control methodology was used to quantify BP control. A multivariable model assessed the effects of patient and procedural factors. Results BP was controlled an average 45.4% (SD 23.9%, 95% CI 41.5%-49.18%) of the time. Larger changes in infusion rate were associated with worse BP control (7.99% less control for 1 mcg•kg−•min− increase in average titration size, p=0.0009). A larger difference between a patient's baseline and target MAP predicted worse BP control (0.93% worse control per 1 mmHg increase in MAP difference, p=0.0013). Both effects persisted in multivariable models. Conclusions : SNP was effective in reducing BP. However, BP was within the target range less than half of the time. No clinician or patient factors were predictive of BP control, although two inverse relationships were identified. These relationships require additional study and may be best coupled with exposure-response modeling to propose improved dosing strategies when using SNP for controlled hypotension in the pediatric population. PMID:25099924

Predictors of arterial blood pressure control during deliberate hypotension with sodium nitroprusside in children.

PubMed

Spielberg, David R; Barrett, Jeffrey S; Hammer, Gregory B; Drover, David R; Reece, Tammy; Cohane, Carol A; Schulman, Scott R

2014-10-01

Sodium nitroprusside (SNP) is used to decrease arterial blood pressure (BP) during certain surgical procedures. There are limited data regarding efficacy of BP control with SNP. There are no data on patient and clinician factors that affect BP control. We evaluated the dose-response relationship of SNP in infants and children undergoing major surgery and performed a quantitative assessment of BP control. One hundred fifty-three subjects at 7 sites received a blinded infusion followed by open-label SNP during operative procedures requiring controlled hypotension. SNP was administered by continuous infusion and titrated to maintain BP control (mean arterial BP [MAP] within ±10% of clinician-defined target). BP was recorded using an arterial catheter. Statistical process control methodology was used to quantify BP control. A multivariable model assessed the effects of patient and procedural factors. BP was controlled an average 45.4% (SD 23.9%; 95% CI, 41.5%-49.18%) of the time. Larger changes in infusion rate were associated with worse BP control (7.99% less control for 1 μg·kg·min increase in average titration size, P = 0.0009). A larger difference between a patient's baseline and target MAP predicted worse BP control (0.93% worse control per 1-mm Hg increase in MAP difference, P = 0.0013). Both effects persisted in multivariable models. SNP was effective in reducing BP. However, BP was within the target range less than half of the time. No clinician or patient factors were predictive of BP control, although 2 inverse relationships were identified. These relationships require additional study and may be best coupled with exposure-response modeling to propose improved dosing strategies when using SNP for controlled hypotension in the pediatric population.

Clinical predictors of rectal lymphogranuloma venereum infection: results from a multicentre case-control study in the U.K.

PubMed

Pallawela, S N S; Sullivan, A K; Macdonald, N; French, P; White, J; Dean, G; Smith, A; Winter, A J; Mandalia, S; Alexander, S; Ison, C; Ward, H

2014-06-01

Since 2003, over 2000 cases of lymphogranuloma venereum (LGV) have been diagnosed in the U.K. in men who have sex with men (MSM). Most cases present with proctitis, but there are limited data on how to differentiate clinically between LGV and other pathology. We analysed the clinical presentations of rectal LGV in MSM to identify clinical characteristics predictive of LGV proctitis and produced a clinical prediction model. A prospective multicentre case-control study was conducted at six U.K. hospitals from 2008 to 2010. Cases of rectal LGV were compared with controls with rectal symptoms but without LGV. Data from 98 LGV cases and 81 controls were collected from patients and clinicians using computer-assisted self-interviews and clinical report forms. Univariate and multivariate logistic regression was used to compare symptoms and signs. Clinical prediction models for LGV were compared using receiver operating curves. Tenesmus, constipation, anal discharge and weight loss were significantly more common in cases than controls. In multivariate analysis, tenesmus and constipation alone were suggestive of LGV (OR 2.98, 95% CI 0.99 to 8.98 and 2.87, 95% CI 1.01 to 8.15, respectively) and that tenesmus alone or in combination with constipation was a significant predictor of LGV (OR 6.97, 95% CI 2.71 to 17.92). The best clinical prediction was having one or more of tenesmus, constipation and exudate on proctoscopy, with a sensitivity of 77% and specificity of 65%. This study indicates that tenesmus alone or in combination with constipation makes a diagnosis of LGV in MSM presenting with rectal symptoms more likely. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration

NASA Astrophysics Data System (ADS)

Godinho, Robson B.; Santos, Mauricio C.; Poppi, Ronei J.

2016-03-01

An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies.

Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

PubMed Central

Gould, Ian C.; Shepherd, Alana M.; Laurens, Kristin R.; Cairns, Murray J.; Carr, Vaughan J.; Green, Melissa J.

2014-01-01

Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features. PMID:25379435

Clinical performance of the Prostate Health Index (PHI) for the prediction of prostate cancer in obese men: data from the PROMEtheuS project, a multicentre European prospective study.

PubMed

Abrate, Alberto; Lazzeri, Massimo; Lughezzani, Giovanni; Buffi, Nicolòmaria; Bini, Vittorio; Haese, Alexander; de la Taille, Alexandre; McNicholas, Thomas; Redorta, Joan Palou; Gadda, Giulio M; Lista, Giuliana; Kinzikeeva, Ella; Fossati, Nicola; Larcher, Alessandro; Dell'Oglio, Paolo; Mistretta, Francesco; Freschi, Massimo; Guazzoni, Giorgio

2015-04-01

To test serum prostate-specific antigen (PSA) isoform [-2]proPSA (p2PSA), p2PSA/free PSA (%p2PSA) and Prostate Health Index (PHI) accuracy in predicting prostate cancer in obese men and to test whether PHI is more accurate than PSA in predicting prostate cancer in obese patients. The analysis consisted of a nested case-control study from the pro-PSA Multicentric European Study (PROMEtheuS) project. The study is registered at http://www.controlled-trials.com/ISRCTN04707454. The primary outcome was to test sensitivity, specificity and accuracy (clinical validity) of serum p2PSA, %p2PSA and PHI, in determining prostate cancer at prostate biopsy in obese men [body mass index (BMI) ≥30 kg/m(2) ], compared with total PSA (tPSA), free PSA (fPSA) and fPSA/tPSA ratio (%fPSA). The number of avoidable prostate biopsies (clinical utility) was also assessed. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis. Of the 965 patients, 383 (39.7%) were normal weight (BMI <25 kg/m(2) ), 440 (45.6%) were overweight (BMI 25-29.9 kg/m(2) ) and 142 (14.7%) were obese (BMI ≥30 kg/m(2) ). Among obese patients, prostate cancer was found in 65 patients (45.8%), with a higher percentage of Gleason score ≥7 diseases (67.7%). PSA, p2PSA, %p2PSA and PHI were significantly higher, and %fPSA significantly lower in patients with prostate cancer (P < 0.001). In multivariable logistic regression models, PHI significantly increased accuracy of the base multivariable model by 8.8% (P = 0.007). At a PHI threshold of 35.7, 46 (32.4%) biopsies could have been avoided. In obese patients, PHI is significantly more accurate than current tests in predicting prostate cancer. © 2014 The Authors. BJU International © 2014 BJU International.

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Modeling and control for closed environment plant production systems

NASA Technical Reports Server (NTRS)

Fleisher, David H.; Ting, K. C.; Janes, H. W. (Principal Investigator)

2002-01-01

A computer program was developed to study multiple crop production and control in controlled environment plant production systems. The program simulates crop growth and development under nominal and off-nominal environments. Time-series crop models for wheat (Triticum aestivum), soybean (Glycine max), and white potato (Solanum tuberosum) are integrated with a model-based predictive controller. The controller evaluates and compensates for effects of environmental disturbances on crop production scheduling. The crop models consist of a set of nonlinear polynomial equations, six for each crop, developed using multivariate polynomial regression (MPR). Simulated data from DSSAT crop models, previously modified for crop production in controlled environments with hydroponics under elevated atmospheric carbon dioxide concentration, were used for the MPR fitting. The model-based predictive controller adjusts light intensity, air temperature, and carbon dioxide concentration set points in response to environmental perturbations. Control signals are determined from minimization of a cost function, which is based on the weighted control effort and squared-error between the system response and desired reference signal.

Characterizing the associative content of brain structures involved in habitual and goal-directed actions in humans: a multivariate FMRI study.

PubMed

McNamee, Daniel; Liljeholm, Mimi; Zika, Ondrej; O'Doherty, John P

2015-03-04

While there is accumulating evidence for the existence of distinct neural systems supporting goal-directed and habitual action selection in the mammalian brain, much less is known about the nature of the information being processed in these different brain regions. Associative learning theory predicts that brain systems involved in habitual control, such as the dorsolateral striatum, should contain stimulus and response information only, but not outcome information, while regions involved in goal-directed action, such as ventromedial and dorsolateral prefrontal cortex and dorsomedial striatum, should be involved in processing information about outcomes as well as stimuli and responses. To test this prediction, human participants underwent fMRI while engaging in a binary choice task designed to enable the separate identification of these different representations with a multivariate classification analysis approach. Consistent with our predictions, the dorsolateral striatum contained information about responses but not outcomes at the time of an initial stimulus, while the regions implicated in goal-directed action selection contained information about both responses and outcomes. These findings suggest that differential contributions of these regions to habitual and goal-directed behavioral control may depend in part on basic differences in the type of information that these regions have access to at the time of decision making. Copyright © 2015 the authors 0270-6474/15/353764-08$15.00/0.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Pre-treatment plasma proteomic markers associated with survival in oesophageal cancer

PubMed Central

Kelly, P; Paulin, F; Lamont, D; Baker, L; Clearly, S; Exon, D; Thompson, A

2012-01-01

Background: The incidence of oesophageal adenocarcinoma is increasing worldwide but survival remains poor. Neoadjuvant chemotherapy can improve survival, but prognostic and predictive biomarkers are required. This study built upon preclinical approaches to identify prognostic plasma proteomic markers in oesophageal cancer. Methods: Plasma samples collected before and during the treatment of oesophageal cancer and non-cancer controls were analysed by surface-enhanced laser desorption/ionisation time-of-flight (SELDI-TOF) mass spectroscopy (MS). Protein peaks were identified by MS in tryptic digests of purified fractions. Associations between peak intensities obtained in the spectra and clinical endpoints (survival, disease-free survival) were tested by univariate (Fisher's exact test) and multivariate analysis (binary logistic regression). Results: Plasma protein peaks were identified that differed significantly (P<0.05, ANOVA) between the oesophageal cancer and control groups at baseline. Three peaks, confirmed as apolipoprotein A-I, serum amyloid A and transthyretin, in baseline (pre-treatment) samples were associated by univariate and multivariate analysis with disease-free survival and overall survival. Conclusion: Plasma proteins can be detected prior to treatment for oesophageal cancer that are associated with outcome and merit testing as prognostic and predictive markers of response to guide chemotherapy in oesophageal cancer. PMID:22294182

Pre-treatment plasma proteomic markers associated with survival in oesophageal cancer.

PubMed

Kelly, P; Paulin, F; Lamont, D; Baker, L; Clearly, S; Exon, D; Thompson, A

2012-02-28

The incidence of oesophageal adenocarcinoma is increasing worldwide but survival remains poor. Neoadjuvant chemotherapy can improve survival, but prognostic and predictive biomarkers are required. This study built upon preclinical approaches to identify prognostic plasma proteomic markers in oesophageal cancer. Plasma samples collected before and during the treatment of oesophageal cancer and non-cancer controls were analysed by surface-enhanced laser desorption/ionisation time-of-flight (SELDI-TOF) mass spectroscopy (MS). Protein peaks were identified by MS in tryptic digests of purified fractions. Associations between peak intensities obtained in the spectra and clinical endpoints (survival, disease-free survival) were tested by univariate (Fisher's exact test) and multivariate analysis (binary logistic regression). Plasma protein peaks were identified that differed significantly (P<0.05, ANOVA) between the oesophageal cancer and control groups at baseline. Three peaks, confirmed as apolipoprotein A-I, serum amyloid A and transthyretin, in baseline (pre-treatment) samples were associated by univariate and multivariate analysis with disease-free survival and overall survival. Plasma proteins can be detected prior to treatment for oesophageal cancer that are associated with outcome and merit testing as prognostic and predictive markers of response to guide chemotherapy in oesophageal cancer.

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Prediction of processing tomato peeling outcomes

USDA-ARS?s Scientific Manuscript database

Peeling outcomes of processing tomatoes were predicted using multivariate analysis of Magnetic Resonance (MR) images. Tomatoes were obtained from a whole-peel production line. Each fruit was imaged using a 7 Tesla MR system, and a multivariate data set was created from 28 different images. After ...

[Cesarean after labor induction: Risk factors and prediction score].

PubMed

Branger, B; Dochez, V; Gervier, S; Winer, N

2018-05-01

The objective of the study is to determine the risk factors for caesarean section at the time of labor induction, to establish a prediction algorithm, to evaluate its relevance and to compare the results with observation. A retrospective study was carried out over a year at Nantes University Hospital with 941 cervical ripening and labor inductions (24.1%) terminated by 167 caesarean sections (17.8%). Within the cohort, a case-control study was conducted with 147 caesarean sections and 148 vaginal deliveries. A multivariate analysis was carried out with a logistic regression allowing the elaboration of an equation of prediction and an ROC curve and the confrontation between the prediction and the reality. In univariate analysis, six variables were significant: nulliparity, small size of the mother, history of scarried uterus, use of prostaglandins as a mode of induction, unfavorable Bishop score<6, variety of posterior release. In multivariate analysis, five variables were significant: nulliparity, maternal size, maternal BMI, scar uterus and Bishop score. The most predictive model corresponded to an area under the curve of 0.86 (0.82-0.90) with a correct prediction percentage ("well classified") of 67.6% for a caesarean section risk of 80%. The prediction criteria would make it possible to inform the woman and the couple about the potential risk of Caesarean section in urgency or to favor a planned Caesarean section or a low-lying attempt on more objective, repeatable and transposable arguments in a medical team. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Predictive factors of flares in systemic lupus erythematosus patients: data from a multiethnic Latin American cohort.

PubMed

Ugarte-Gil, M F; Wojdyla, D; Pastor-Asurza, C A; Gamboa-Cárdenas, R V; Acevedo-Vásquez, E M; Catoggio, L J; García, M A; Bonfá, E; Sato, E I; Massardo, L; Pascual-Ramos, V; Barile, L A; Reyes-Llerena, G; Iglesias-Gamarra, A; Molina-Restrepo, J F; Chacón-Díaz, R; Alarcón, G S; Pons-Estel, B A

2018-04-01

Purpose The purpose of this paper is to determine the factors predictive of flares in systemic lupus erythematosus (SLE) patients. Methods A case-control study nested within the Grupo Latino Americano De Estudio de Lupus (GLADEL) cohort was conducted. Flare was defined as an increase ≥4 points in the SLEDAI. Cases were defined as patients with at least one flare. Controls were selected by matching cases by length of follow-up. Demographic and clinical manifestations were systematically recorded by a common protocol. Glucocorticoid use was recorded as average daily dose of prednisone and antimalarial use as percentage of time on antimalarial and categorized as never (0%), rarely (>0-25%), occasionally (>25%-50%), commonly (˃50%-75%) and frequently (˃75%). Immunosuppressive drugs were recorded as used or not used. The association between demographic, clinical manifestations, therapy and flares was examined using univariable and multivariable conditional logistic regression models. Results A total of 465 cases and controls were included. Mean age at diagnosis among cases and controls was 27.5 vs 29.9 years, p = 0.003; gender and ethnic distributions were comparable among both groups and so was the baseline SLEDAI. Independent factors protective of flares identified by multivariable analysis were older age at diagnosis (OR = 0.929 per every five years, 95% CI 0.869-0.975; p = 0.004) and antimalarial use (frequently vs never, OR = 0.722, 95% CI 0.522-0.998; p = 0.049) whereas azathioprine use (OR = 1.820, 95% CI 1.309-2.531; p < 0.001) and SLEDAI post-baseline were predictive of them (OR = 1.034, 95% CI 1.005-1.064; p = 0.022). Conclusions In this large, longitudinal Latin American cohort, older age at diagnosis and more frequent antimalarial use were protective whereas azathioprine use and higher disease activity were predictive of flares.

Prediction of Adolescents’ Glycemic Control 1 Year After Diabetes-Specific Family Conflict

PubMed Central

Hilliard, Marisa E.; Guilfoyle, Shanna M.; Dolan, Lawrence M.; Hood, Korey K.

2015-01-01

Objective To test adherence to blood glucose monitoring (BGM) as a mediator between diabetes-specific family conflict and glycemic control (hemoglobin A1c [HbA1c] levels) for 1 year. Design Three waves of prospective data spanning 1 year. Setting Diabetes clinic in a large tertiary care children’s hospital in the Midwestern United States. Participants One hundred forty-five dyads composed of an adolescent (aged 13–18 years) with type 1 diabetes mellitus and a parent. Main Exposures Adolescent- and parent-rated diabetes-specific family conflict and mean daily BGM frequency obtained through meter downloads. Main Outcome Measure Levels of HbA1c, abstracted from the medical record. Results In separate general linear models, higher adolescent-rated family conflict scores at baseline predicted less frequent BGM at 6 months (β=−0.08 [P=.01]) and higher HbA1c levels at 12 months (β=0.08 [P=.02]). In the multivariate model including baseline conflict and BGM as predictors of HbA1c levels, BGM was a significant predictor (β=−0.24 [P=.007]) and conflict was no longer significant (β=0.05 [P=.11]), supporting the mediation hypothesis. Post hoc probing showed that BGM explained 24% of the variance in the conflict-HbA1c link. The mediation between parent-reported conflict andHbA1c levels via BGM adherence was partially supported (conflict predicting HbA1c in the zero-order equation, β=−0.24 [P=.004]; multivariate equation, β=0.06 [P=.02]), and BGM frequency explained 16% of the conflict-HbA1c link. Conclusions Diabetes-specific family conflict in adolescence predicts deteriorations in BGM and subsequent glycemic control for at least 1 year. Results support ongoing intervention research designed to reduce family conflict and thus prevent a trajectory of declining adherence and glycemic control across adolescence. PMID:21727273

Predicting the preferences for involvement in medical decision making among patients with mental disorders

PubMed Central

Michaelis, Svea; Kriston, Levente; Härter, Martin; Watzke, Birgit; Schulz, Holger; Melchior, Hanne

2017-01-01

Background The involvement of patients in medical decision making has been investigated widely in somatic diseases. However, little is known about the preferences for involvement and variables that could predict these preferences in patients with mental disorders. Objective This study aims to determine what roles mentally ill patients actually want to assume when making medical decisions and to identify the variables that could predict this role, including patients’ self-efficacy. Method Demographic and clinical data of 798 patients with mental disorders from three psychotherapeutic units in Germany were elicited using self-report questionnaires. Control preference was measured using the Control Preferences Scale, and patients’ perceived self-efficacy was assessed using the Self-Efficacy Scale. Bivariate and multivariate regression analyses were conducted to investigate the associations between patient variables and control preference. Results Most patients preferred a collaborative role (57.5%), followed by a semi passive (21.2%), a partly autonomous (16.2%), an autonomous (2.8%) and a fully passive (2.3%) role when making medical decisions. Age, sex, diagnosis, employment status, medical pretreatment and perceived self-efficacy were associated with the preference for involvement in the multivariate logistic model. Conclusion Our results confirm the preferences for involvement in medical decisions of mentally ill patients. We reconfirmed previous findings that older patients prefer a shared role over an autonomous role and that subjects with a high qualification prefer a more autonomous role over a shared role. The knowledge about predictors may help strengthen treatment effectiveness because matching the preferred and actual role preferences has been shown to improve clinical outcome. PMID:28837621

Predicting the preferences for involvement in medical decision making among patients with mental disorders.

PubMed

Michaelis, Svea; Kriston, Levente; Härter, Martin; Watzke, Birgit; Schulz, Holger; Melchior, Hanne

2017-01-01

The involvement of patients in medical decision making has been investigated widely in somatic diseases. However, little is known about the preferences for involvement and variables that could predict these preferences in patients with mental disorders. This study aims to determine what roles mentally ill patients actually want to assume when making medical decisions and to identify the variables that could predict this role, including patients' self-efficacy. Demographic and clinical data of 798 patients with mental disorders from three psychotherapeutic units in Germany were elicited using self-report questionnaires. Control preference was measured using the Control Preferences Scale, and patients' perceived self-efficacy was assessed using the Self-Efficacy Scale. Bivariate and multivariate regression analyses were conducted to investigate the associations between patient variables and control preference. Most patients preferred a collaborative role (57.5%), followed by a semi passive (21.2%), a partly autonomous (16.2%), an autonomous (2.8%) and a fully passive (2.3%) role when making medical decisions. Age, sex, diagnosis, employment status, medical pretreatment and perceived self-efficacy were associated with the preference for involvement in the multivariate logistic model. Our results confirm the preferences for involvement in medical decisions of mentally ill patients. We reconfirmed previous findings that older patients prefer a shared role over an autonomous role and that subjects with a high qualification prefer a more autonomous role over a shared role. The knowledge about predictors may help strengthen treatment effectiveness because matching the preferred and actual role preferences has been shown to improve clinical outcome.

Determination of fragrance content in perfume by Raman spectroscopy and multivariate calibration.

PubMed

Godinho, Robson B; Santos, Mauricio C; Poppi, Ronei J

2016-03-15

An alternative methodology is herein proposed for determination of fragrance content in perfumes and their classification according to the guidelines established by fine perfume manufacturers. The methodology is based on Raman spectroscopy associated with multivariate calibration, allowing the determination of fragrance content in a fast, nondestructive, and sustainable manner. The results were considered consistent with the conventional method, whose standard error of prediction values was lower than the 1.0%. This result indicates that the proposed technology is a feasible analytical tool for determination of the fragrance content in a hydro-alcoholic solution for use in manufacturing, quality control and regulatory agencies. Copyright © 2015 Elsevier B.V. All rights reserved.

Neuropsychologic predictors of competency in Alzheimer's disease using a rational reasons legal standard.

PubMed

Marson, D C; Cody, H A; Ingram, K K; Harrell, L E

1995-10-01

To identify neuropsychologic predictors of competency performance and status in Alzheimer's disease (AD) using a specific legal standard (LS). This study is a follow-up to the competency assessment research reported in this issue of the archives. Univariate and multivariate analyses of independent neuropsychologic test measures with a dependent measure of competency to consent to treatment. University medical center. Fifteen normal older control subjects and 29 patients with probable AD. Subjects were administered a battery of neuropsychologic measures theoretically linked to competency function, as well as two clinical vignettes testing their capacity to consent to medical treatment under five different LSs. The present study focused on one specific LS: the capacity to provide "rational reasons" for a treatment choice (LS4). Neuropsychologic test scores were correlated with scores on LS4 for the normal control group and the AD group. The resulting univariate predictors were then analyzed using stepwise regression and discriminant function to identify the key multivariate predictors of competency performance and status under LS4. Measures of word fluency predicted the LS4 scores of controls (R2 = .33) and the AD group (R2 = .36). A word fluency measure also emerged as the best single predictor of competency status for the full subject sample (n = 44), correctly classifying 82% of cases. Dementia severity (Mini-Mental State Examination score) did not emerge as a multivariate predictor of competency performance or status. Interestingly, measures of verbal reasoning and memory were not strongly associated with LS4. Word fluency measures predicted the normative performance and intact competency status of older control subjects and the declining performance and compromised competency status of patients with AD on a "rational reasons" standard of competency to consent to treatment. Cognitive capacities related to frontal lobe function appear to underlie the capacity to formulate rational reasons for a treatment choice. Neuropsychologic studies of competency function have important theoretical and clinical value.

Predicting volumes in four Hawaii hardwoods...first multivariate equations developed

Treesearch

David A. Sharpnack

1966-01-01

Multivariate regression equations were developed for predicting board-foot (Int. 1/ 4-inch log rule ) and cubic-foot volumes in each 8.15-foot section of trees of four Hawaii hardwood species. The species are koa (Acacia koa), ohia (Metrosideros polymorpha), robusta eucalyptus (Eucalyptus robusta), and...

Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture

PubMed Central

Pauli, Duke; Ziegler, Greg; Ren, Min; Jenks, Matthew A.; Hunsaker, Douglas J.; Zhang, Min; Baxter, Ivan; Gore, Michael A.

2018-01-01

To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions. PMID:29437829

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

Preoperative Electrocardiogram Score for Predicting New-Onset Postoperative Atrial Fibrillation in Patients Undergoing Cardiac Surgery.

PubMed

Gu, Jiwei; Andreasen, Jan J; Melgaard, Jacob; Lundbye-Christensen, Søren; Hansen, John; Schmidt, Erik B; Thorsteinsson, Kristinn; Graff, Claus

2017-02-01

To investigate if electrocardiogram (ECG) markers from routine preoperative ECGs can be used in combination with clinical data to predict new-onset postoperative atrial fibrillation (POAF) following cardiac surgery. Retrospective observational case-control study. Single-center university hospital. One hundred consecutive adult patients (50 POAF, 50 without POAF) who underwent coronary artery bypass grafting, valve surgery, or combinations. Retrospective review of medical records and registration of POAF. Clinical data and demographics were retrieved from the Western Denmark Heart Registry and patient records. Paper tracings of preoperative ECGs were collected from patient records, and ECG measurements were read by two independent readers blinded to outcome. A subset of four clinical variables (age, gender, body mass index, and type of surgery) were selected to form a multivariate clinical prediction model for POAF and five ECG variables (QRS duration, PR interval, P-wave duration, left atrial enlargement, and left ventricular hypertrophy) were used in a multivariate ECG model. Adding ECG variables to the clinical prediction model significantly improved the area under the receiver operating characteristic curve from 0.54 to 0.67 (with cross-validation). The best predictive model for POAF was a combined clinical and ECG model with the following four variables: age, PR-interval, QRS duration, and left atrial enlargement. ECG markers obtained from a routine preoperative ECG may be helpful in predicting new-onset POAF in patients undergoing cardiac surgery. Copyright © 2017 Elsevier Inc. All rights reserved.

A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus.

PubMed

Sweeting, Arianne N; Wong, Jencia; Appelblom, Heidi; Ross, Glynis P; Kouru, Heikki; Williams, Paul F; Sairanen, Mikko; Hyett, Jon A

2018-06-13

Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11-13+6 weeks' gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks' gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89-0.94) overall, and 0.93 (95% CI 0.89-0.96) for early GDM, in a combined multivariate prediction model. A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM. © 2018 S. Karger AG, Basel.

Socio-emotional skills, behavior problems, and Spanish competence predict the acquisition of English among English language learners in poverty.

PubMed

Winsler, Adam; Kim, Yoon Kyong; Richard, Erin R

2014-09-01

This article analyzes the role that individual differences in children's cognitive, Spanish competence, and socio-emotional and behavioral skills play in predicting the concurrent and longitudinal acquisition of English among a large sample of ethnically diverse, low-income, Hispanic preschool children. Participants assessed at age 4 for language, cognitive, socio-emotional, and behavioral skills were followed through kindergarten. Multivariate analyses demonstrated that Spanish-speaking preschoolers with greater initiative, self-control, and attachment and fewer behavior problems at age 4 were more successful in obtaining English proficiency by the end of kindergarten compared to those initially weaker in these skills, even after controlling for cognitive/language skills and demographic variables. Also, greater facility in Spanish at age 4 predicted the attainment of English proficiency. Social and behavioral skills and proficiency in Spanish are valuable resources for low-income English language learners during their transition to school.

HIV-1 DNA predicts disease progression and post-treatment virological control

PubMed Central

Williams, James P; Hurst, Jacob; Stöhr, Wolfgang; Robinson, Nicola; Brown, Helen; Fisher, Martin; Kinloch, Sabine; Cooper, David; Schechter, Mauro; Tambussi, Giuseppe; Fidler, Sarah; Carrington, Mary; Babiker, Abdel; Weber, Jonathan

2014-01-01

In HIV-1 infection, a population of latently infected cells facilitates viral persistence despite antiretroviral therapy (ART). With the aim of identifying individuals in whom ART might induce a period of viraemic control on stopping therapy, we hypothesised that quantification of the pool of latently infected cells in primary HIV-1 infection (PHI) would predict clinical progression and viral replication following ART. We measured HIV-1 DNA in a highly characterised randomised population of individuals with PHI. We explored associations between HIV-1 DNA and immunological and virological markers of clinical progression, including viral rebound in those interrupting therapy. In multivariable analyses, HIV-1 DNA was more predictive of disease progression than plasma viral load and, at treatment interruption, predicted time to plasma virus rebound. HIV-1 DNA may help identify individuals who could safely interrupt ART in future HIV-1 eradication trials. Clinical trial registration: ISRCTN76742797 and EudraCT2004-000446-20 DOI: http://dx.doi.org/10.7554/eLife.03821.001 PMID:25217531

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

The Effect of Visual Information on the Manual Approach and Landing

NASA Technical Reports Server (NTRS)

Wewerinke, P. H.

1982-01-01

The effect of visual information in combination with basic display information on the approach performance. A pre-experimental model analysis was performed in terms of the optimal control model. The resulting aircraft approach performance predictions were compared with the results of a moving base simulator program. The results illustrate that the model provides a meaningful description of the visual (scene) perception process involved in the complex (multi-variable, time varying) manual approach task with a useful predictive capability. The theoretical framework was shown to allow a straight-forward investigation of the complex interaction of a variety of task variables.

Implementation Challenges for Multivariable Control: What You Did Not Learn in School

NASA Technical Reports Server (NTRS)

Garg, Sanjay

2008-01-01

Multivariable control allows controller designs that can provide decoupled command tracking and robust performance in the presence of modeling uncertainties. Although the last two decades have seen extensive development of multivariable control theory and example applications to complex systems in software/hardware simulations, there are no production flying systems aircraft or spacecraft, that use multivariable control. This is because of the tremendous challenges associated with implementation of such multivariable control designs. Unfortunately, the curriculum in schools does not provide sufficient time to be able to provide an exposure to the students in such implementation challenges. The objective of this paper is to share the lessons learned by a practitioner of multivariable control in the process of applying some of the modern control theory to the Integrated Flight Propulsion Control (IFPC) design for an advanced Short Take-Off Vertical Landing (STOVL) aircraft simulation.

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Prediction of wastewater treatment plants performance based on artificial fish school neural network

NASA Astrophysics Data System (ADS)

Zhang, Ruicheng; Li, Chong

2011-10-01

A reliable model for wastewater treatment plant is essential in providing a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, an artificial fish school neural network prediction model is established standing on actual operation data in the wastewater treatment system. The model overcomes several disadvantages of the conventional BP neural network. The results of model calculation show that the predicted value can better match measured value, played an effect on simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provides a simple and practical way for the operation and management in wastewater treatment plant, and has good research and engineering practical value.

The role of parental perceptions of tic frequency and intensity in predicting tic-related functional impairment in youth with chronic tic disorders.

PubMed

Espil, Flint M; Capriotti, Matthew R; Conelea, Christine A; Woods, Douglas W

2014-12-01

Tic severity is composed of several dimensions. Tic frequency and intensity are two such dimensions, but little empirical data exist regarding their relative contributions to functional impairment in those with chronic tic disorders (CTD). The present study examined the relative contributions of these dimensions in predicting tic-related impairment across several psychosocial domains. Using data collected from parents of youth with CTD, multivariate regression analyses revealed that both tic frequency and intensity predicted tic-related impairment in several areas; including family and peer relationships, school interference, and social endeavors, even when controlling for the presence of comorbid anxiety symptoms and Attention Deficit Hyperactivity Disorder diagnostic status. Results showed that tic intensity predicted more variance across more domains than tic frequency.

Personality and Defense Styles: Clinical Specificities and Predictive Factors of Alcohol Use Disorder in Women.

PubMed

Ribadier, Aurélien; Dorard, Géraldine; Varescon, Isabelle

2016-01-01

This study investigated personality traits and defense styles in order to determine clinical specificities and predictive factors of alcohol use disorders (AUDs) in women. A female sample, composed of AUD outpatients (n = 48) and a control group (n = 50), completed a sociodemographic self-report and questionnaires assessing personality traits (BFI), defense mechanisms and defense styles (DSQ-40). Comparative and correlational analyses, as well as univariate and multivariate logistic regressions, were performed. AUD women presented with higher neuroticism and lower extraversion and conscientiousness. They used less mature and more neurotic and immature defense styles than the control group. Concerning personality traits, high neuroticism and lower conscientiousness were predictive of AUD, as well as low mature, high neurotic, and immature defense styles. Including personality traits and defense styles in a logistic model, high neuroticism was the only AUD predictive factor. AUD women presented clinical specificities and predictive factors in personality traits and defense styles that must be taken into account in AUD studies. Implications for specific treatment for women are discussed.

Predicting carotid artery disease and plaque instability from cell-derived microparticles.

PubMed

Wekesa, A L; Cross, K S; O'Donovan, O; Dowdall, J F; O'Brien, O; Doyle, M; Byrne, L; Phelan, J P; Ross, M D; Landers, R; Harrison, M

2014-11-01

Cell-derived microparticles (MPs) are small plasma membrane-derived vesicles shed from circulating blood cells and may act as novel biomarkers of vascular disease. We investigated the potential of circulating MPs to predict (a) carotid plaque instability and (b) the presence of advanced carotid disease. This pilot study recruited carotid disease patients (aged 69.3 ± 1.2 years [mean ± SD], 69% male, 90% symptomatic) undergoing endarterectomy (n = 42) and age- and sex-matched controls (n = 73). Plaques were classified as stable (n = 25) or unstable (n = 16) post surgery using immunohistochemistry. Blood samples were analysed for MP subsets and molecular biomarkers. Odds ratios (OR) are expressed per standard deviation biomarker increase. Endothelial MP (EMP) subsets, but not any vascular, inflammatory, or proteolytic molecular biomarker, were higher (p < .05) in the unstable than the stable plaque patients. The area under the receiver operator characteristic curve for CD31(+)41(-) EMP in discriminating an unstable plaque was 0.73 (0.56-0.90, p < .05). CD31(+)41(-) EMP predicted plaque instability (OR = 2.19, 1.08-4.46, p < .05) and remained significant in a multivariable model that included transient ischaemic attack symptom status. Annexin V(+) MP, platelet MP (PMP) subsets, and C-reactive protein were higher (p < .05) in cases than controls. Annexin V(+) MP (OR = 3.15, 1.49-6.68), soluble vascular cell adhesion molecule-1 (OR = 1.64, 1.03-2.59), and previous smoking history (OR = 3.82, 1.38-10.60) independently (p < .05) predicted the presence of carotid disease in a multivariable model. EMP may have utility in predicting plaque instability in carotid patients and annexin V(+) MPs may predict the presence of advanced carotid disease in aging populations, independent of established biomarkers. Copyright © 2014 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.

Long working hours and skipping breakfast concomitant with late evening meals are associated with suboptimal glycemic control among young male Japanese patients with type 2 diabetes.

PubMed

Azami, Yasushi; Funakoshi, Mitsuhiko; Matsumoto, Hisashi; Ikota, Akemi; Ito, Koichi; Okimoto, Hisashi; Shimizu, Nobuaki; Tsujimura, Fumihiro; Fukuda, Hiroshi; Miyagi, Chozi; Osawa, Sayaka; Osawa, Ryo; Miura, Jiro

2018-04-17

To assess the associations of working conditions, eating habits and glycemic control among young Japanese workers with type 2 diabetes. This hospital- and clinic-based prospective study included 352 male and 126 female working patients with diabetes aged 20-40 years. Data were obtained from June to July 2012 and June to July 2013. Logistic regression analysis was used to estimate multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for suboptimal glycemic control (glycosylated hemoglobin level of ≥7%) obtained from June to July 2013. Multivariable logistic regression analysis showed that disease duration of ≥10 years (OR 2.43, 95% CI 1.02-5.80), glycosylated hemoglobin level of ≥7% in 2012 (OR 8.50, 95% CI 4.90-14.80), skipping breakfast and late evening meals (OR 2.50, 95% CI 1.25-5.00) and working ≥60 h/week (OR 2.92, 95% CI 1.16-7.40) were predictive of suboptimal glycemic control in male workers, whereas a glycosylated hemoglobin level of ≥7% in 2012 (OR 17.96, 95% CI 5.93-54.4), oral hyperglycemic agent therapy (OR 12.49, 95% CI 2.75-56.86) and insulin therapy (OR 11.60, 95% CI 2.35-57.63) were predictive of suboptimal glycemic control in female workers. Working ≥60 h/week and habitual skipping breakfast concomitant with late evening meals might affect the ability of young male workers with type 2 diabetes to achieve and maintain glycemic control. © 2018 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.

An elevated gap between admission and A1C-derived average glucose levels is associated with adverse outcomes in diabetic patients with pyogenic liver abscess.

PubMed

Liao, Wen-I; Sheu, Wayne Huey-Herng; Chang, Wei-Chou; Hsu, Chin-Wang; Chen, Yu-Long; Tsai, Shih-Hung

2013-01-01

To assess whether chronic glycemic control and stress-induced hyperglycemia, determined by the gap between admission glucose levels and A1C-derived average glucose (ADAG) levels adversely affects outcomes in diabetic patients with pyogenic liver abscess (PLA). Clinical, laboratory, and multi-detector computed tomography (MDCT) findings of 329 PLA patients (2004-2010) were retrospectively reviewed. HbA1C levels were used to determine long-term glycemic control status, which were then converted to estimated average glucose values. For the gap between admission glucose levels and ADAG levels, we used receiver operating characteristic (ROC) curve to determine the optimal cut-off values predicting adverse outcomes. Univariate and multivariate logistic regressions were used to identify predictors of adverse outcomes. Diabetic PLA patients with poorer glycemic control had significantly higher Klebsiella pneumoniae (KP) infection rates, lower albumin levels, and longer hospital stays than those with suboptimal and good glycemic control. The ROC curve showed that a glycemic gap of 72 mg/dL was the optimal cut-off value for predicting adverse outcomes and showed a 22.3% relative increase in adverse outcomes compared with a glycemic gap<72 mg/dL. Multivariate analysis revealed that an elevated glycemic gap≥72 mg/dL was important predictor of adverse outcomes. A glycemic gap≥72 mg/dL, rather than admission hyperglycemia or chronic glycemic control, was significantly correlated with adverse outcomes in diabetic PLA patients. Poorer chronic glycemic control in diabetic PLA patients is associated with high incidence of KP infection, hypoalbuminemia and longer hospital stay.

Data mining for water resource management part 2 - methods and approaches to solving contemporary problems

USGS Publications Warehouse

Roehl, Edwin A.; Conrads, Paul

2010-01-01

This is the second of two papers that describe how data mining can aid natural-resource managers with the difficult problem of controlling the interactions between hydrologic and man-made systems. Data mining is a new science that assists scientists in converting large databases into knowledge, and is uniquely able to leverage the large amounts of real-time, multivariate data now being collected for hydrologic systems. Part 1 gives a high-level overview of data mining, and describes several applications that have addressed major water resource issues in South Carolina. This Part 2 paper describes how various data mining methods are integrated to produce predictive models for controlling surface- and groundwater hydraulics and quality. The methods include: - signal processing to remove noise and decompose complex signals into simpler components; - time series clustering that optimally groups hundreds of signals into "classes" that behave similarly for data reduction and (or) divide-and-conquer problem solving; - classification which optimally matches new data to behavioral classes; - artificial neural networks which optimally fit multivariate data to create predictive models; - model response surface visualization that greatly aids in understanding data and physical processes; and, - decision support systems that integrate data, models, and graphics into a single package that is easy to use.

Interleukin-6 predicts recurrence and survival among head and neck cancer patients.

PubMed

Duffy, Sonia A; Taylor, Jeremy M G; Terrell, Jeffrey E; Islam, Mozaffarul; Li, Yun; Fowler, Karen E; Wolf, Gregory T; Teknos, Theodoros N

2008-08-15

Increased pretreatment serum interleukin (IL)-6 levels among patients with head and neck squamous cell carcinoma (HNSCC) have been shown to correlate with poor prognosis, but sample sizes in prior studies have been small and thus unable to control for other known prognostic variables. A longitudinal, prospective cohort study determined the correlation between pretreatment serum IL-6 levels, and tumor recurrence and all-cause survival in a large population (N = 444) of previously untreated HNSCC patients. Control variables included age, sex, smoking, cancer site and stage, and comorbidities. Kaplan-Meier plots and univariate and multivariate Cox proportional hazards models were used to study the association between IL-6 levels, control variables, and time to recurrence and survival. The median serum IL-6 level was 13 pg/mL (range, 0-453). The 2-year recurrence rate was 35.2% (standard error, 2.67%). The 2-year death rate was 26.5% (standard error, 2.26%). Multivariate analyses showed that serum IL-6 levels independently predicted recurrence at significant levels [hazard ratio (HR) = 1.32; 95% confidence interval (CI), 1.11 to 1.58; P = .002] as did cancer site (oral/sinus). Serum IL-6 level was also a significant independent predictor of poor survival (HR = 1.22; 95% CI, 1.02 to 1.46; P = .03), as were older age, smoking, cancer site (oral/sinus), higher cancer stage, and comorbidities. Pretreatment serum IL-6 could be a valuable biomarker for predicting recurrence and overall survival among HNSCC patients. Using IL-6 as a biomarker for recurrence and survival may allow for earlier identification and treatment of disease relapse. 2008 American Cancer Society

A multivariate model for predicting segmental body composition.

PubMed

Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice

2013-12-01

The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.

Predictors of Individual Tumor Local Control After Stereotactic Radiosurgery for Non-Small Cell Lung Cancer Brain Metastases

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

Garsa, Adam A.; Badiyan, Shahed N.; DeWees, Todd

2014-10-01

Purpose: To evaluate local control rates and predictors of individual tumor local control for brain metastases from non-small cell lung cancer (NSCLC) treated with stereotactic radiosurgery (SRS). Methods and Materials: Between June 1998 and May 2011, 401 brain metastases in 228 patients were treated with Gamma Knife single-fraction SRS. Local failure was defined as an increase in lesion size after SRS. Local control was estimated using the Kaplan-Meier method. The Cox proportional hazards model was used for univariate and multivariate analysis. Receiver operating characteristic analysis was used to identify an optimal cutpoint for conformality index relative to local control. Amore » P value <.05 was considered statistically significant. Results: Median age was 60 years (range, 27-84 years). There were 66 cerebellar metastases (16%) and 335 supratentorial metastases (84%). The median prescription dose was 20 Gy (range, 14-24 Gy). Median overall survival from time of SRS was 12.1 months. The estimated local control at 12 months was 74%. On multivariate analysis, cerebellar location (hazard ratio [HR] 1.94, P=.009), larger tumor volume (HR 1.09, P<.001), and lower conformality (HR 0.700, P=.044) were significant independent predictors of local failure. Conformality index cutpoints of 1.4-1.9 were predictive of local control, whereas a cutpoint of 1.75 was the most predictive (P=.001). The adjusted Kaplan-Meier 1-year local control for conformality index ≥1.75 was 84% versus 69% for conformality index <1.75, controlling for tumor volume and location. The 1-year adjusted local control for cerebellar lesions was 60%, compared with 77% for supratentorial lesions, controlling for tumor volume and conformality index. Conclusions: Cerebellar tumor location, lower conformality index, and larger tumor volume were significant independent predictors of local failure after SRS for brain metastases from NSCLC. These results warrant further investigation in a prospective setting.« less

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

PubMed Central

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP. PMID:29312134

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

F100 multivariable control synthesis program: Evaluation of a multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Soeder, J. F.; Seldner, K.; Cwynar, D. S.

1977-01-01

The design, evaluation, and testing of a practical, multivariable, linear quadratic regulator control for the F100 turbofan engine were accomplished. NASA evaluation of the multivariable control logic and implementation are covered. The evaluation utilized a real time, hybrid computer simulation of the engine. Results of the evaluation are presented, and recommendations concerning future engine testing of the control are made. Results indicated that the engine testing of the control should be conducted as planned.

Barriers to health-care and psychological distress among mothers living with HIV in Quebec (Canada).

PubMed

Blais, Martin; Fernet, Mylène; Proulx-Boucher, Karène; Lebouché, Bertrand; Rodrigue, Carl; Lapointe, Normand; Otis, Joanne; Samson, Johanne

2015-01-01

Health-care providers play a major role in providing good quality care and in preventing psychological distress among mothers living with HIV (MLHIV). The objectives of this study are to explore the impact of health-care services and satisfaction with care providers on psychological distress in MLHIV. One hundred MLHIV were recruited from community and clinical settings in the province of Quebec (Canada). Prevalence estimation of clinical psychological distress and univariate and multivariable logistic regression models were performed to predict clinical psychological distress. Forty-five percent of the participants reported clinical psychological distress. In the multivariable regression, the following variables were significantly associated with psychological distress while controlling for sociodemographic variables: resilience, quality of communication with the care providers, resources, and HIV disclosure concerns. The multivariate results support the key role of personal, structural, and medical resources in understanding psychological distress among MLHIV. Interventions that can support the psychological health of MLHIV are discussed.

Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration.

PubMed

Dabkiewicz, Vanessa Emídio; de Mello Pereira Abrantes, Shirley; Cassella, Ricardo Jorgensen

2018-08-05

Near infrared spectroscopy (NIR) with diffuse reflectance associated to multivariate calibration has as main advantage the replacement of the physical separation of interferents by the mathematical separation of their signals, rapidly with no need for reagent consumption, chemical waste production or sample manipulation. Seeking to optimize quality control analyses, this spectroscopic analytical method was shown to be a viable alternative to the classical Kjeldahl method for the determination of protein nitrogen in yellow fever vaccine. The most suitable multivariate calibration was achieved by the partial least squares method (PLS) with multiplicative signal correction (MSC) treatment and data mean centering (MC), using a minimum number of latent variables (LV) equal to 1, with the lower value of the square root of the mean squared prediction error (0.00330) associated with the highest percentage value (91%) of samples. Accuracy ranged 95 to 105% recovery in the 4000-5184 cm -1 region. Copyright © 2018 Elsevier B.V. All rights reserved.

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

The Preoperative Controlling Nutritional Status Score Predicts Survival After Curative Surgery in Patients with Pathological Stage I Non-small Cell Lung Cancer.

PubMed

Shoji, Fumihiro; Haratake, Naoki; Akamine, Takaki; Takamori, Shinkichi; Katsura, Masakazu; Takada, Kazuki; Toyokawa, Gouji; Okamoto, Tatsuro; Maehara, Yoshihiko

2017-02-01

The prognostic Controlling Nutritional Status (CONUT) score is used to evaluate immuno-nutritional conditions and is a predictive factor of postoperative survival in patients with digestive tract cancer. We retrospectively analyzed clinicopathological features of patients with pathological stage I non-small cell lung cancer (NSCLC) to identify predictors or prognostic factors of postoperative survival and to investigate the role of preoperative CONUT score in predicting survival. We selected 138 consecutive patients with pathological stage I NSCLC treated from August 2005 to August 2010. We measured their preoperative CONUT score in uni- and multivariate Cox regression analyses of postoperative survival. A high CONUT score was positively associated with preoperative serum carcinoembryonic antigen level (p=0.0100) and postoperative recurrence (p=0.0767). In multivariate analysis, the preoperative CONUT score [relative risk (RR)=6.058; 95% confidence interval (CI)=1.068-113.941; p=0.0407), increasing age (RR=7.858; 95% CI=2.034-36.185; p=0.0029), and pleural invasion (RR=36.615; 95% CI=5.900-362.620; p<0.0001) were independent prognostic factors. In Kaplan-Meier analysis of recurrence-free survival (RFS), cancer-specific survival (CS), and overall survival (OS), the group with high CONUT score had a significantly shorter RFS, CS, and OS than did the low-CONUT score group by log-rank test (p=0.0458, p=0.0104 and p=0.0096, respectively). The preoperative CONUT score is both a predictive and prognostic factor in patients with pathological stage I NSCLC. This immuno-nutritional score can indicate patients at high risk of postoperative recurrence and death. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Results of third-generation epirubicin/cisplatin/xeloda adjuvant chemotherapy in patients with radically resected gastric cancer.

PubMed

Cainap, Calin; Nagy, Viorica; Seicean, Andrada; Gherman, Alexandra; Laszlo, Istvan; Lisencu, Cosmin; Nadim, Al Hajar; Constantin, Anne-Marie; Cainap, Simona

2016-01-01

The purpose of this study was to evaluate the efficacy and toxicity of a third-generation chemotherapy regimen in the adjuvant setting to radically operated patients with gastric cancer. This proposed new adjuvant regimen was also compared with a consecutive retrospective cohort of patients treated with the classic McDonald regimen. Starting in 2006, a non-randomized prospective phase II study was conducted at the Institute of Oncology of Cluj-Napoca on 40 patients with stage IB-IV radically resected gastric adenocarcinoma. These patients were administered a chemotherapy regimen already considered to be standard treatment in the metastatic setting: ECX (epirubicin, cisplatin, xeloda) and were compared to a retrospective control group consisting of 54 patients, treated between 2001 and 2006 according to McDonald's trial. In a previous paper, we reported toxicities and the possible predictive factors for these toxicities; in the present article, we report on the results concerning predictive factors on overall survival (OS) and disease free survival (DFS). The proposed ECX treatment was not less effective than the standard suggested by McDonald's trial. Age was an independent prognostic factor in multivariate analysis. N3 stage was an independent prognostic factor for OS and DFS. N ratio >70% was an independent predictive factor for OS and locoregional disease control. The resection margins were independent prognostic factors for OS and DFS. The proposed treatment is not less effective compared with the McDonald's trial. Age was an independent prognostic factor in multivariate analysis. N3 stage represented an independent prognostic factor and N ratio >70% was a predictive factor for OS and DFS. The resection margins were proven to be independent prognostic factors for OS and DFS.

DIVERGT screening procedure predicts general cognitive functioning in adult long-term survivors of pediatric acute lymphoblastic leukemia: A PETALE study.

PubMed

Boulet-Craig, Aubree; Robaey, Philippe; Laniel, Julie; Bertout, Laurence; Drouin, Simon; Krajinovic, Maja; Laverdière, Caroline; Sinnett, Daniel; Sultan, Serge; Lippé, Sarah

2018-05-24

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. Because of major improvements in treatment protocols, the survival rate now exceeds 80%. However, ALL treatments can cause long-term neurocognitive sequelae, which negatively impact academic achievement and quality of life. Therefore, cognitive sequelae need to be carefully evaluated. The DIVERGT is a battery of tests proposed as a screening tool, sensitive to executive function impairments in children and adolescent cancer survivors. Our study aimed at verifying the predictive value of the DIVERGT on general cognitive functioning in adult long-term survivors of ALL. ALL survivors completed the DIVERGT 13.4 years, on average, after remission (N = 247). In addition, 49 of these survivors (equally selected amongst those with low, average, and high DIVERGT scores) as well as 29 controls completed a more comprehensive neuropsychological evaluation within a 3-year period from DIVERGT administration. Multivariate regression analysis was used to assess the predictive value of the DIVERGT on general intelligence, mathematics, verbal memory, and working memory. As a follow-up analysis, three performance groups were created based on the DIVERGT results. Multivariate analysis of variance (MANOVA) assessed neuropsychological differences between groups. The DIVERGT accurately predicted General Ability Index (GAI) (P < 0.0001), mathematics (P < 0.0001) and verbal memory (P = 0.045). Moreover, the low-performance group consistently had poorer performance than the high-performance and control groups on the neuropsychological tests. The DIVERGT is a useful, time-effective screening battery for broader neurocognitive impairments identification in long-term adult ALL survivors. It could be implemented as routine examination in cancer follow-up clinics. © 2018 Wiley Periodicals, Inc.

Machine learning and predictive data analytics enabling metrology and process control in IC fabrication

NASA Astrophysics Data System (ADS)

Rana, Narender; Zhang, Yunlin; Wall, Donald; Dirahoui, Bachir; Bailey, Todd C.

2015-03-01

Integrate circuit (IC) technology is going through multiple changes in terms of patterning techniques (multiple patterning, EUV and DSA), device architectures (FinFET, nanowire, graphene) and patterning scale (few nanometers). These changes require tight controls on processes and measurements to achieve the required device performance, and challenge the metrology and process control in terms of capability and quality. Multivariate data with complex nonlinear trends and correlations generally cannot be described well by mathematical or parametric models but can be relatively easily learned by computing machines and used to predict or extrapolate. This paper introduces the predictive metrology approach which has been applied to three different applications. Machine learning and predictive analytics have been leveraged to accurately predict dimensions of EUV resist patterns down to 18 nm half pitch leveraging resist shrinkage patterns. These patterns could not be directly and accurately measured due to metrology tool limitations. Machine learning has also been applied to predict the electrical performance early in the process pipeline for deep trench capacitance and metal line resistance. As the wafer goes through various processes its associated cost multiplies. It may take days to weeks to get the electrical performance readout. Predicting the electrical performance early on can be very valuable in enabling timely actionable decision such as rework, scrap, feedforward, feedback predicted information or information derived from prediction to improve or monitor processes. This paper provides a general overview of machine learning and advanced analytics application in the advanced semiconductor development and manufacturing.

Use of chemometrics to compare NIR and HPLC for the simultaneous determination of drug levels in fixed-dose combination tablets employed in tuberculosis treatment.

PubMed

Teixeira, Kelly Sivocy Sampaio; da Cruz Fonseca, Said Gonçalves; de Moura, Luís Carlos Brigido; de Moura, Mario Luís Ribeiro; Borges, Márcia Herminia Pinheiro; Barbosa, Euzébio Guimaraes; De Lima E Moura, Túlio Flávio Accioly

2018-02-05

The World Health Organization recommends that TB treatment be administered using combination therapy. The methodologies for quantifying simultaneously associated drugs are highly complex, being costly, extremely time consuming and producing chemical residues harmful to the environment. The need to seek alternative techniques that minimize these drawbacks is widely discussed in the pharmaceutical industry. Therefore, the objective of this study was to develop and validate a multivariate calibration model in association with the near infrared spectroscopy technique (NIR) for the simultaneous determination of rifampicin, isoniazid, pyrazinamide and ethambutol. These models allow the quality control of these medicines to be optimized using simple, fast, low-cost techniques that produce no chemical waste. In the NIR - PLS method, spectra readings were acquired in the 10,000-4000cm -1 range using an infrared spectrophotometer (IRPrestige - 21 - Shimadzu) with a resolution of 4cm -1 , 20 sweeps, under controlled temperature and humidity. For construction of the model, the central composite experimental design was employed on the program Statistica 13 (StatSoft Inc.). All spectra were treated by computational tools for multivariate analysis using partial least squares regression (PLS) on the software program Pirouette 3.11 (Infometrix, Inc.). Variable selections were performed by the QSAR modeling program. The models developed by NIR in association with multivariate analysis provided good prediction of the APIs for the external samples and were therefore validated. For the tablets, however, the slightly different quantitative compositions of excipients compared to the mixtures prepared for building the models led to results that were not statistically similar, despite having prediction errors considered acceptable in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

On Restructurable Control System Theory

NASA Technical Reports Server (NTRS)

Athans, M.

1983-01-01

The state of stochastic system and control theory as it impacts restructurable control issues is addressed. The multivariable characteristics of the control problem are addressed. The failure detection/identification problem is discussed as a multi-hypothesis testing problem. Control strategy reconfiguration, static multivariable controls, static failure hypothesis testing, dynamic multivariable controls, fault-tolerant control theory, dynamic hypothesis testing, generalized likelihood ratio (GLR) methods, and adaptive control are discussed.

Ten problems and solutions when predicting individual outcome from lesion site after stroke.

PubMed

Price, Cathy J; Hope, Thomas M; Seghier, Mohamed L

2017-01-15

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients. Copyright © 2016 Elsevier Inc. All rights reserved.

Ten problems and solutions when predicting individual outcome from lesion site after stroke

PubMed Central

Price, Cathy J.; Hope, Thomas M.; Seghier, Mohamed L.

2016-01-01

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual’s functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients. PMID:27502048

Analysis of Prognostic Factors and Patterns of Recurrence in Patients With Pathologic Stage III Endometrial Cancer

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

Patel, Samir; Portelance, Lorraine; Gilbert, Lucy

2007-08-01

Purpose: To retrospectively assess prognostic factors and patterns of recurrence in patients with pathologic Stage III endometrial cancer. Methods and Materials: Between 1989 and 2003, 107 patients with pathologic International Federation of Gynecology and Obstetrics Stage III endometrial adenocarcinoma confined to the pelvis were treated at our institution. Adjuvant radiotherapy (RT) was delivered to 68 patients (64%). The influence of multiple patient- and treatment-related factors on pelvic and distant control and overall survival (OS) was evaluated. Results: Median follow-up for patients at risk was 41 months. Five-year actuarial OS was significantly improved in patients treated with adjuvant RT (68%) comparedmore » with those with resection alone (50%; p = 0.029). Age, histology, grade, uterine serosal invasion, adnexal involvement, number of extrauterine sites, and treatment with adjuvant RT predicted for improved survival in univariate analysis. Multivariate analysis revealed that grade, uterine serosal invasion, and treatment with adjuvant RT were independent predictors of survival. Five-year actuarial pelvic control was improved significantly with the delivery of adjuvant RT (74% vs. 49%; p = 0.011). Depth of myometrial invasion and treatment with adjuvant RT were independent predictors of pelvic control in multivariate analysis. Conclusions: Multiple prognostic factors predicting for the outcome of pathologic Stage III endometrial cancer patients were identified in this analysis. In particular, delivery of adjuvant RT seems to be a significant independent predictor for improved survival and pelvic control, suggesting that pelvic RT should be routinely considered in the management of these patients.« less

Prediction of outcome of bright light treatment in patients with seasonal affective disorder: Discarding the early response, confirming a higher atypical balance, and uncovering a higher body mass index at baseline as predictors of endpoint outcome.

PubMed

Dimitrova, Tzvetelina D; Reeves, Gloria M; Snitker, Soren; Lapidus, Manana; Sleemi, Aamar R; Balis, Theodora G; Manalai, Partam; Tariq, Muhammad M; Cabassa, Johanna A; Karim, Naila N; Johnson, Mary A; Langenberg, Patricia; Rohan, Kelly J; Miller, Michael; Stiller, John W; Postolache, Teodor T

2017-11-01

We tested the hypothesis that the early improvement in mood after the first hour of bright light treatment compared to control dim-red light would predict the outcome at six weeks of bright light treatment for depressed mood in patients with Seasonal Affective Disorder (SAD). We also analyzed the value of Body Mass Index (BMI) and atypical symptoms of depression at baseline in predicting treatment outcome. Seventy-eight adult participants were enrolled. The first treatment was controlled crossover, with randomized order, and included one hour of active bright light treatment and one hour of control dim-red light, with one-hour washout. Depression was measured on the Structured Interview Guide for the Hamilton Rating Scale for Depression-SAD version (SIGH-SAD). The predictive association of depression scores changes after the first session. BMI and atypical score balance with treatment outcomes at endpoint were assessed using multivariable linear and logistic regressions. No significant prediction by changes in depression scores after the first session was found. However, higher atypical balance scores and BMI positively predicted treatment outcome. Absence of a control intervention for the six-weeks of treatment (only the first session in the laboratory was controlled). Exclusion of patients with comorbid substance abuse, suicidality and bipolar I disorder, and patients on antidepressant medications, reducing the generalizability of the study. Prediction of outcome by early response to light treatment was not replicated, and the previously reported prediction of baseline atypical balance was confirmed. BMI, a parameter routinely calculated in primary care, was identified as a novel predictor, and calls for replication and then exploration of possible mediating mechanisms. Published by Elsevier B.V.

A systems theoretic approach to analysis and control of mammalian circadian dynamics

PubMed Central

Abel, John H.; Doyle, Francis J.

2016-01-01

The mammalian circadian clock is a complex multi-scale, multivariable biological control system. In the past two decades, methods from systems engineering have led to numerous insights into the architecture and functionality of this system. In this review, we examine the mammalian circadian system through a process systems lens. We present a mathematical framework for examining the cellular circadian oscillator, and show recent extensions for understanding population-scale dynamics. We provide an overview of the routes by which the circadian system can be systemically manipulated, and present in silico proof of concept results for phase resetting of the clock via model predictive control. PMID:28496287

The explanatory role of relationship power and control in domestic violence against women in Nicaragua: a feminist psychology analysis.

PubMed

Grose, Rose Grace; Grabe, Shelly

2014-08-01

This study offers a feminist psychology analysis of various aspects of relationship power and control and their relative explanatory contribution to understanding physical, psychological, and sexual violence against women. Findings from structured interviews with 345 women from rural Nicaragua (M age = 44) overwhelmingly demonstrate that measures of power and control reflecting interpersonal relationship dynamics have the strongest predictive power for explaining violence when compared in multivariate analyses to several of the more commonly used measures. These findings have implications for future research and the evaluation of interventions designed to decrease levels of violence against women. © The Author(s) 2014.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

Neonatal Pulmonary MRI of Bronchopulmonary Dysplasia Predicts Short-term Clinical Outcomes.

PubMed

Higano, Nara S; Spielberg, David R; Fleck, Robert J; Schapiro, Andrew H; Walkup, Laura L; Hahn, Andrew D; Tkach, Jean A; Kingma, Paul S; Merhar, Stephanie L; Fain, Sean B; Woods, Jason C

2018-05-23

Bronchopulmonary dysplasia (BPD) is a serious neonatal pulmonary condition associated with premature birth, but the underlying parenchymal disease and trajectory are poorly characterized. The current NICHD/NHLBI definition of BPD severity is based on degree of prematurity and extent of oxygen requirement. However, no clear link exists between initial diagnosis and clinical outcomes. We hypothesized that magnetic resonance imaging (MRI) of structural parenchymal abnormalities will correlate with NICHD-defined BPD disease severity and predict short-term respiratory outcomes. Forty-two neonates (20 severe BPD, 6 moderate, 7 mild, 9 non-BPD controls; 40±3 weeks post-menstrual age) underwent quiet-breathing structural pulmonary MRI (ultrashort echo-time and gradient echo) in a NICU-sited, neonatal-sized 1.5T scanner, without sedation or respiratory support unless already clinically prescribed. Disease severity was scored independently by two radiologists. Mean scores were compared to clinical severity and short-term respiratory outcomes. Outcomes were predicted using univariate and multivariable models including clinical data and scores. MRI scores significantly correlated with severities and predicted respiratory support at NICU discharge (P<0.0001). In multivariable models, MRI scores were by far the strongest predictor of respiratory support duration over clinical data, including birth weight and gestational age. Notably, NICHD severity level was not predictive of discharge support. Quiet-breathing neonatal pulmonary MRI can independently assess structural abnormalities of BPD, describe disease severity, and predict short-term outcomes more accurately than any individual standard clinical measure. Importantly, this non-ionizing technique can be implemented to phenotype disease and has potential to serially assess efficacy of individualized therapies.

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study.

PubMed

Hamilton, C A; Miller, A; Casablanca, Y; Horowitz, N S; Rungruang, B; Krivak, T C; Richard, S D; Rodriguez, N; Birrer, M J; Backes, F J; Geller, M A; Quinn, M; Goodheart, M J; Mutch, D G; Kavanagh, J J; Maxwell, G L; Bookman, M A

2018-02-01

To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). The analysis dataset included 3010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. Published by Elsevier Inc.

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study

PubMed Central

Hamilton, C. A.; Miller, A.; Casablanca, Y.; Horowitz, N. S.; Rungruang, B.; Krivak, T. C.; Richard, S. D.; Rodriguez, N.; Birrer, M.J.; Backes, F.J.; Geller, M.A.; Quinn, M.; Goodheart, M.J.; Mutch, D.G.; Kavanagh, J.J.; Maxwell, G. L.; Bookman, M. A.

2018-01-01

Objective To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Methods Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10 years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). Results The analysis dataset included 3,010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. Conclusions The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. PMID:29195926

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.; ...

2017-11-21

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

PubMed Central

Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

2014-01-01

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

Corneal Deformation Response and Ocular Geometry: A Noninvasive Diagnostic Strategy in Marfan Syndrome.

PubMed

Beene, Lauren C; Traboulsi, Elias I; Seven, Ibrahim; Ford, Matthew R; Sinha Roy, Abhijit; Butler, Robert S; Dupps, William J

2016-01-01

To evaluate corneal air-puff deformation responses and ocular geometry as predictors of Marfan syndrome. Prospective observational clinical study. Sixteen investigator-derived, 4 standard Ocular Response Analyzer (ORA), and geometric variables from corneal tomography and optical biometry using Oculus Pentacam and IOL Master were assessed for discriminative value in Marfan syndrome, measuring right eyes of 24 control and 13 Marfan syndrome subjects. Area under the receiver operating characteristic (AUROC) curve was assessed in univariate and multivariate analyses. Six investigator-derived ORA variables successfully discriminated Marfan syndrome. The best lone disease predictor was Concavity Min (Marfan syndrome 47.5 ± 20, control 69 ± 14, P = .003; AUROC = 0.80). Corneal hysteresis (CH) and corneal resistance factor (CRF) were decreased (Marfan syndrome CH 9.45 ± 1.62, control CH 11.24 ± 1.21, P = .01; Marfan syndrome CRF 9.77 ± 1.65, control CRF 11.03 ± 1.72, P = .01) and corneas were flatter in Marfan syndrome (Marfan syndrome Kmean 41.25 ± 2.09 diopter, control Kmean 42.70 ± 1.81 diopter, P = .046). No significant differences were observed in central corneal thickness, axial eye length, or intraocular pressure. A multivariate regression model incorporating corneal curvature and hysteresis loop area (HLA) provided the best predictive value for Marfan syndrome (AUROC = 0.85). This study describes novel biodynamic features of corneal deformation responses in Marfan syndrome, including increased deformation, decreased bending resistance, and decreased energy dissipation capacity. A predictive model incorporating HLA and corneal curvature shows greatest potential for noninvasive clinical diagnosis of Marfan syndrome. Copyright © 2016 Elsevier Inc. All rights reserved.

Corneal Deformation Response and Ocular Geometry: A Non-invasive Diagnostic Strategy in Marfan Syndrome

PubMed Central

Beene, Lauren C.; Traboulsi, Elias I.; Seven, Ibrahim; Ford, Matthew R.; Roy, Abhijit Sinha; Butler, Robert S.; Dupps, William J.

2015-01-01

Purpose To evaluate corneal air-puff deformation responses and ocular geometry as predictors of Marfan syndrome. Design Prospective observational clinical study Methods Sixteen investigator-derived, 4 standard Ocular Response Analyzer (ORA), and geometric variables from corneal tomography and optical biometry using Oculus Pentacam and IOL Master were assessed for discriminative value in Marfan syndrome, measuring right eyes of 24 control and 13 Marfan syndrome subjects. Area under the receiver operating characteristic (AUROC) curve was assessed in univariate and multivariate analyses Results Six investigator-derived ORA variables successfully discriminated Marfan syndrome. The best lone disease predictor was Concavity Min (Marfan syndrome 47.5 ± 20, control 69 ± 14, p = 0.003; AUROC = 0.80). Corneal hysteresis and corneal resistance factor were decreased (Marfan syndrome CH 9.45 ± 1.62, control CH 11.24 ± 1.21, p = 0.01; Marfan syndrome CRF 9.77 ± 1.65, control CRF 11.03 ± 1.72, p = 0.01) and corneas were flatter in Marfan syndrome (Marfan syndrome Kmean 41.25 ± 2.09 D, control Kmean 42.70 ± 1.81 D, p = 0.046). No significant differences were observed in central corneal thickness, axial eye length, or intraocular pressure. A multivariate regression model incorporating corneal curvature and hysteresis loop area (HLA) provided the best predictive value for Marfan syndrome (AUROC = 0.85). Conclusions This study describes novel biodynamic features of corneal deformation responses in Marfan syndrome, including increased deformation, decreased bending resistance, and decreased energy dissipation capacity. A predictive model incorporating HLA and corneal curvature shows greatest potential for non-invasive clinical diagnosis of Marfan syndrome. PMID:26432567

Discordance between net analyte signal theory and practical multivariate calibration.

PubMed

Brown, Christopher D

2004-08-01

Lorber's concept of net analyte signal is reviewed in the context of classical and inverse least-squares approaches to multivariate calibration. It is shown that, in the presence of device measurement error, the classical and inverse calibration procedures have radically different theoretical prediction objectives, and the assertion that the popular inverse least-squares procedures (including partial least squares, principal components regression) approximate Lorber's net analyte signal vector in the limit is disproved. Exact theoretical expressions for the prediction error bias, variance, and mean-squared error are given under general measurement error conditions, which reinforce the very discrepant behavior between these two predictive approaches, and Lorber's net analyte signal theory. Implications for multivariate figures of merit and numerous recently proposed preprocessing treatments involving orthogonal projections are also discussed.

Motivation is not enough: prediction of risk behavior following diagnosis of coronary heart disease from the theory of planned behavior.

PubMed

Johnston, Derek W; Johnston, Marie; Pollard, Beth; Kinmonth, Ann-Louise; Mant, David

2004-09-01

Perceived behavioral control (PBC) and intention, the proximal predictors from the theory of planned behavior (TPB), were used to predict cardiovascular risk behaviors in 597 patients 1 year after diagnosis with coronary heart disease. The outcome measures were self-report measures of exercise plus objective measures of fitness (distance walked in 6 min) and cotinine-confirmed smoking cessation. In multivariate analyses incorporating both PBC and intention, PBC predicted exercise, distance walked, and smoking cessation, but intention was not a reliable independent predictor of any health behavior measured. Thus, the effective theoretical component of the TPB was PBC. Similar predictions could derive from social-cognitive theory. In coronary patients, behavioral change needs to address issues of action implementation rather than motivational factors alone. ((c) 2004 APA, all rights reserved)

The Role of Parental Perceptions of Tic Frequency and Intensity in Predicting Tic-Related Functional Impairment in Youth with Chronic Tic Disorders

PubMed Central

Espil, Flint M.; Capriotti, Matthew R.; Conelea, Christine A.; Woods, Douglas W.

2014-01-01

Tic severity is composed of several dimensions. Tic frequency and intensity are two such dimensions, but little empirical data exist regarding their relative contributions to functional impairment in those with Chronic Tic Disorders (CTD). The present study examined the relative contributions of these dimensions in predicting tic-related impairment across several psychosocial domains. Using data collected from parents of youth with CTD, multivariate regression analyses revealed that both tic frequency and intensity predicted tic-related impairment in several areas; including family and peer relationships, school interference, and social endeavors, even when controlling for the presence of comorbid anxiety symptoms and Attention Deficit Hyperactivity Disorder diagnostic status. Results showed that tic intensity predicted more variance across more domains than tic frequency. PMID:24395287

Looking beyond patients: Can parents' quality of life predict asthma control in children?

PubMed

Cano-Garcinuño, Alfredo; Mora-Gandarillas, Isabel; Bercedo-Sanz, Alberto; Callén-Blecua, María Teresa; Castillo-Laita, José Antonio; Casares-Alonso, Irene; Forns-Serrallonga, Dolors; Tauler-Toro, Eulàlia; Alonso-Bernardo, Luz María; García-Merino, Águeda; Moneo-Hernández, Isabel; Cortés-Rico, Olga; Carvajal-Urueña, Ignacio; Morell-Bernabé, Juan José; Martín-Ibáñez, Itziar; Rodríguez-Fernández-Oliva, Carmen Rosa; Asensi-Monzó, María Teresa; Fernández-Carazo, Carmen; Murcia-García, José; Durán-Iglesias, Catalina; Montón-Álvarez, José Luis; Domínguez-Aurrecoechea, Begoña; Praena-Crespo, Manuel

2016-07-01

Social and family factors may influence the probability of achieving asthma control in children. Parents' quality of life has been insufficiently explored as a predictive factor linked to the probability of achieving disease control in asthmatic children. Determine whether the parents' quality of life predicts medium-term asthma control in children. Longitudinal study of children between 4 and 14 years of age, with active asthma. The parents' quality of life was evaluated using the specific IFABI-R instrument, in which scores were higher for poorer quality of life. Its association with asthma control measures in the child 16 weeks later was analyzed using multivariate methods, adjusting the effect for disease, child and family factors. The data from 452 children were analyzed (median age 9.6 years, 63.3% males). The parents' quality of life was predictive for asthma control; each point increase on the initial IFABI-R score was associated with an adjusted odds ratio (95% confidence interval) of 0.56 (0.37-0.86) for good control of asthma on the second visit, 2.58 (1.62-4.12) for asthma exacerbation, 2.12 (1.33-3.38) for an unscheduled visit to the doctor, and 2.46 (1.18-5.13) for going to the emergency room. The highest quartile for the IFABI-R score had a sensitivity of 34.5% and a specificity of 82.2% to predict poorly controlled asthma. Parents' poorer quality of life is related to poor, medium-term asthma control in children. Assessing the parents' quality of life could aid disease management decisions. Pediatr Pulmonol. 2016;51:670-677. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

F100 Multivariable Control Synthesis Program. Computer Implementation of the F100 Multivariable Control Algorithm

NASA Technical Reports Server (NTRS)

Soeder, J. F.

1983-01-01

As turbofan engines become more complex, the development of controls necessitate the use of multivariable control techniques. A control developed for the F100-PW-100(3) turbofan engine by using linear quadratic regulator theory and other modern multivariable control synthesis techniques is described. The assembly language implementation of this control on an SEL 810B minicomputer is described. This implementation was then evaluated by using a real-time hybrid simulation of the engine. The control software was modified to run with a real engine. These modifications, in the form of sensor and actuator failure checks and control executive sequencing, are discussed. Finally recommendations for control software implementations are presented.

Advanced modelling, monitoring, and process control of bioconversion systems

NASA Astrophysics Data System (ADS)

Schmitt, Elliott C.

Production of fuels and chemicals from lignocellulosic biomass is an increasingly important area of research and industrialization throughout the world. In order to be competitive with fossil-based fuels and chemicals, maintaining cost-effectiveness is critical. Advanced process control (APC) and optimization methods could significantly reduce operating costs in the biorefining industry. Two reasons APC has previously proven challenging to implement for bioprocesses include: lack of suitable online sensor technology of key system components, and strongly nonlinear first principal models required to predict bioconversion behavior. To overcome these challenges batch fermentations with the acetogen Moorella thermoacetica were monitored with Raman spectroscopy for the conversion of real lignocellulosic hydrolysates and a kinetic model for the conversion of synthetic sugars was developed. Raman spectroscopy was shown to be effective in monitoring the fermentation of sugarcane bagasse and sugarcane straw hydrolysate, where univariate models predicted acetate concentrations with a root mean square error of prediction (RMSEP) of 1.9 and 1.0 g L-1 for bagasse and straw, respectively. Multivariate partial least squares (PLS) models were employed to predict acetate, xylose, glucose, and total sugar concentrations for both hydrolysate fermentations. The PLS models were more robust than univariate models, and yielded a percent error of approximately 5% for both sugarcane bagasse and sugarcane straw. In addition, a screening technique was discussed for improving Raman spectra of hydrolysate samples prior to collecting fermentation data. Furthermore, a mechanistic model was developed to predict batch fermentation of synthetic glucose, xylose, and a mixture of the two sugars to acetate. The models accurately described the bioconversion process with an RMSEP of approximately 1 g L-1 for each model and provided insights into how kinetic parameters changed during dual substrate fermentation with diauxic growth. Model predictive control (MPC), an advanced process control strategy, is capable of utilizing nonlinear models and sensor feedback to provide optimal input while ensuring critical process constraints are met. Using the microorganism Saccharomyces cerevisiae, a commonly used microorganism for biofuel production, and work performed with M. thermoacetica, a nonlinear MPC was implemented on a continuous membrane cell-recycle bioreactor (MCRB) for the conversion of glucose to ethanol. The dilution rate was used to control the ethanol productivity of the system will maintaining total substrate conversion above the constraint of 98%. PLS multivariate models for glucose (RMSEP 1.5 g L-1) and ethanol (RMSEP 0.4 g L-1) were robust in predicting concentrations and a mechanistic kinetic model built accurately predicted continuous fermentation behavior. A setpoint trajectory, ranging from 2 - 4.5 g L-1 h-1 for productivity was closely tracked by the fermentation system using Raman measurements and an extended Kalman filter to estimate biomass concentrations. Overall, this work was able to demonstrate an effective approach for real-time monitoring and control of a complex fermentation system.

Characteristics of Perimenstrual Asthma and Its Relation to Asthma Severity and Control

PubMed Central

Rao, Chitra K.; Moore, Charity G.; Bleecker, Eugene; Busse, William W.; Calhoun, William; Castro, Mario; Chung, Kian Fan; Erzurum, Serpil C.; Israel, Elliot; Curran-Everett, Douglas

2013-01-01

Background: Although perimenstrual asthma (PMA) has been associated with severe and difficult-to-control asthma, it remains poorly characterized and understood. The objectives of this study were to identify clinical, demographic, and inflammatory factors associated with PMA and to assess the association of PMA with asthma severity and control. Methods: Women with asthma recruited to the National Heart, Lung, and Blood Institute Severe Asthma Research Program who reported PMA symptoms on a screening questionnaire were analyzed in relation to basic demographics, clinical questionnaire data, immunoinflammatory markers, and physiologic parameters. Univariate comparisons between PMA and non-PMA groups were performed. A severity-adjusted model predicting PMA was created. Additional models addressed the role of PMA in asthma control. Results: Self-identified PMA was reported in 17% of the subjects (n = 92) and associated with higher BMI, lower FVC % predicted, and higher gastroesophageal reflux disease rates. Fifty-two percent of the PMA group met criteria for severe asthma compared with 30% of the non-PMA group. In multivariable analyses controlling for severity, aspirin sensitivity and lower FVC % predicted were associated with the presence of PMA. Furthermore, after controlling for severity and confounders, PMA remained associated with more asthma symptoms and urgent health-care utilization. Conclusions: PMA is common in women with severe asthma and associated with poorly controlled disease. Aspirin sensitivity and lower FVC % predicted are associated with PMA after adjusting for multiple factors, suggesting that alterations in prostaglandins may contribute to this phenotype. PMID:23632943

Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach

NASA Astrophysics Data System (ADS)

Rish, Irina; Bashivan, Pouya; Cecchi, Guillermo A.; Goldstein, Rita Z.

2016-03-01

The objective of this study is to investigate effects of methylphenidate on brain activity in individuals with cocaine use disorder (CUD) using functional MRI (fMRI). Methylphenidate hydrochloride (MPH) is an indirect dopamine agonist commonly used for treating attention deficit/hyperactivity disorders; it was also shown to have some positive effects on CUD subjects, such as improved stop signal reaction times associated with better control/inhibition,1 as well as normalized task-related brain activity2 and resting-state functional connectivity in specific areas.3 While prior fMRI studies of MPH in CUDs have focused on mass-univariate statistical hypothesis testing, this paper evaluates multivariate, whole-brain effects of MPH as captured by the generalization (prediction) accuracy of different classification techniques applied to features extracted from resting-state functional networks (e.g., node degrees). Our multivariate predictive results based on resting-state data from3 suggest that MPH tends to normalize network properties such as voxel degrees in CUD subjects, thus providing additional evidence for potential benefits of MPH in treating cocaine addiction.

Prediction of Gestational Diabetes through NMR Metabolomics of Maternal Blood.

PubMed

Pinto, Joana; Almeida, Lara M; Martins, Ana S; Duarte, Daniela; Barros, António S; Galhano, Eulália; Pita, Cristina; Almeida, Maria do Céu; Carreira, Isabel M; Gil, Ana M

2015-06-05

Metabolic biomarkers of pre- and postdiagnosis gestational diabetes mellitus (GDM) were sought, using nuclear magnetic resonance (NMR) metabolomics of maternal plasma and corresponding lipid extracts. Metabolite differences between controls and disease were identified through multivariate analysis of variable selected (1)H NMR spectra. For postdiagnosis GDM, partial least squares regression identified metabolites with higher dependence on normal gestational age evolution. Variable selection of NMR spectra produced good classification models for both pre- and postdiagnostic GDM. Prediagnosis GDM was accompanied by cholesterol increase and minor increases in lipoproteins (plasma), fatty acids, and triglycerides (extracts). Small metabolite changes comprised variations in glucose (up regulated), amino acids, betaine, urea, creatine, and metabolites related to gut microflora. Most changes were enhanced upon GDM diagnosis, in addition to newly observed changes in low-Mw compounds. GDM prediction seems possible exploiting multivariate profile changes rather than a set of univariate changes. Postdiagnosis GDM is successfully classified using a 26-resonance plasma biomarker. Plasma and extracts display comparable classification performance, the former enabling direct and more rapid analysis. Results and putative biochemical hypotheses require further confirmation in larger cohorts of distinct ethnicities.

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes.

PubMed

Achana, Felix A; Cooper, Nicola J; Bujkiewicz, Sylwia; Hubbard, Stephanie J; Kendrick, Denise; Jones, David R; Sutton, Alex J

2014-07-21

Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations. Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on outcomes not directly considered by the studies included in the analysis. Accounting for the dependency between outcomes in a multivariate meta-analysis may or may not improve the precision of effect estimates from a network meta-analysis compared to analysing each outcome separately.

Do race, neglect, and childhood poverty predict physical health in adulthood? A multilevel prospective analysis

PubMed Central

Nikulina, Valentina

2015-01-01

Childhood neglect and poverty often co-occur and both have been linked to poor physical health outcomes. In addition, Blacks have higher rates of childhood poverty and tend to have worse health than Whites. This paper examines the unique and interacting effects of childhood neglect, race, and family and neighborhood poverty on adult physical health outcomes. This prospective cohort design study uses a sample (N = 675) of court-substantiated cases of childhood neglect and matched controls followed into adulthood (Mage = 41). Health indicators (C-Reactive Protein [CRP], hypertension, and pulmonary functioning) were assessed through blood collection and measurements by a registered nurse. Data were analyzed using hierarchical linear models to control for clustering of participants in childhood neighborhoods. Main effects showed that growing up Black predicted CRP and hypertension elevations, despite controlling for neglect and childhood family and neighborhood poverty and their interactions. Multivariate results showed that race and childhood adversities interacted to predict adult health outcomes. Childhood family poverty predicted increased risk for hypertension for Blacks, not Whites. In contrast, among Whites, childhood neglect predicted elevated CRP. Childhood neighborhood poverty interacted with childhood family poverty to predict pulmonary functioning in adulthood. Gender differences in health indicators were also observed. The effects of childhood neglect, childhood poverty, and growing up Black in the United States are manifest in physical health outcomes assessed 30 years later. Implications are discussed. PMID:24189205

Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis.

PubMed

Oosterhof, Nikolaas N; Wiggett, Alison J; Cross, Emily S

2014-04-01

Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.

Can venous ProBNP levels predict placenta accreta?

PubMed

Ersoy, Ali Ozgur; Oztas, Efser; Ozler, Sibel; Ersoy, Ebru; Erkenekli, Kudret; Uygur, Dilek; Caglar, Ali Turhan; Danisman, Nuri

2016-12-01

Placenta previa (PP) is a potential life-threatening pregnancy complication. Pro-brain natriuretic peptide (ProBNP), creatine kinase (CK), cardiac form of CK (CK-MB) and Troponin I are circulatory biomarkers related to cardiac functions. We aimed to determine whether these biomarkers are related to PP and placenta accreta. In this case-control study, fifty-four pregnant women who attended our tertiary care center for perinatology with the diagnosis of PP totalis, and of them, 14 patients with placenta accreta were recruited as the study groups. Forty-six uncomplicated control patients who were matched for age, BMI were also included. Maternal venous ProBNP, CK, CK-MB and Troponin I levels were compared between the three groups. Obstetric history characteristics were comparable among groups, generally. CK and CK-MB levels were similar among three groups. Troponin I levels in the previa and accreta groups were significantly higher than the controls. ProBNP levels in the accreta group were significantly higher than other two groups. The multivariate regression model revealed that ProBNP could predict placental adhesion anomalies. Troponin I and ProBNP levels in PP cases were higher than controls and ProBNP could predict placenta accreta.

Quality-by-design III: application of near-infrared spectroscopy to monitor roller compaction in-process and product quality attributes of immediate release tablets.

PubMed

Kona, Ravikanth; Fahmy, Raafat M; Claycamp, Gregg; Polli, James E; Martinez, Marilyn; Hoag, Stephen W

2015-02-01

The objective of this study is to use near-infrared spectroscopy (NIRS) coupled with multivariate chemometric models to monitor granule and tablet quality attributes in the formulation development and manufacturing of ciprofloxacin hydrochloride (CIP) immediate release tablets. Critical roller compaction process parameters, compression force (CFt), and formulation variables identified from our earlier studies were evaluated in more detail. Multivariate principal component analysis (PCA) and partial least square (PLS) models were developed during the development stage and used as a control tool to predict the quality of granules and tablets. Validated models were used to monitor and control batches manufactured at different sites to assess their robustness to change. The results showed that roll pressure (RP) and CFt played a critical role in the quality of the granules and the finished product within the range tested. Replacing binder source did not statistically influence the quality attributes of the granules and tablets. However, lubricant type has significantly impacted the granule size. Blend uniformity, crushing force, disintegration time during the manufacturing was predicted using validated PLS regression models with acceptable standard error of prediction (SEP) values, whereas the models resulted in higher SEP for batches obtained from different manufacturing site. From this study, we were able to identify critical factors which could impact the quality attributes of the CIP IR tablets. In summary, we demonstrated the ability of near-infrared spectroscopy coupled with chemometrics as a powerful tool to monitor critical quality attributes (CQA) identified during formulation development.

Serum Levels of M2BPGi as Short-Term Predictors of Hepatocellular Carcinoma in Untreated Chronic Hepatitis B Patients.

PubMed

Liu, Jessica; Hu, Hui-Han; Lee, Mei-Hsuan; Korenaga, Masaaki; Jen, Chin-Lan; Batrla-Utermann, Richard; Lu, Sheng-Nan; Wang, Li-Yu; Mizokami, Masashi; Chen, Chien-Jen; Yang, Hwai-I

2017-10-30

This study examines the role of M2BPGi, a novel seromarker for chronic hepatitis, in predicting hepatocellular carcinoma (HCC) among untreated chronic hepatitis B (CHB) patients. In this nested case-control study, 1070 samples were assayed for M2BPGi, including 357 samples from HCC cases, and 713 samples from non-HCC controls, collected at various times throughout follow-up. HCC case samples were stratified according to years prior to diagnosis. Associations between M2BPGi and HCC were examined with multivariate logistic regression. M2BPGi, α-fetoprotein (AFP), and hepatitis B surface antigen (HBsAg) levels were significant independent short-term predictors of HCC, while M2BPGi was insignificant in long-term analyses. Compared to M2BPGi levels <1.0 cut-off index (COI), those with levels ≥2.0 COI had multivariate odds ratios (95% CI) for HCC of 7.40 (2.40-22.78), 6.46 (2.58-16.18), and 2.24 (0.97-5.15), respectively, for prediction of HCC within 1-2, 2-5, and ≥5 years. Higher proportions of individuals had M2BPGi levels ≥2.0 COI in samples closer to HCC diagnosis. Areas under receiver operating characteristic curves for models with M2BPGi, AFP, and HBsAg levels predicting HCC within 1-2, 2-5, and >5 years were 0.84, 0.81, and 0.75. M2BPGi is a strong and independent short-term predictor of HCC in CHB patients.

Predictors of shoulder dystocia at the time of operative vaginal delivery.

PubMed

Palatnik, Anna; Grobman, William A; Hellendag, Madeline G; Janetos, Timothy M; Gossett, Dana R; Miller, Emily S

2016-11-01

It remains uncertain whether clinical factors known prior to delivery can predict which women are more likely to experience shoulder dystocia in the setting of operative vaginal delivery. We sought to identify whether shoulder dystocia can be accurately predicted among women undergoing an operative vaginal delivery. This was a case-control study of women undergoing a low or outlet operative vaginal delivery from 2005 through 2014 in a single tertiary care center. Cases were defined as women who experienced a shoulder dystocia at the time of operative vaginal delivery. Controls consisted of women without a shoulder dystocia at the time of operative vaginal delivery. Variables previously identified to be associated with shoulder dystocia that could be known prior to delivery were abstracted from the medical records. Bivariable analyses and multivariable logistic regression were used to identify factors independently associated with shoulder dystocia. A receiver operating characteristic curve was created to evaluate the predictive value of the model for shoulder dystocia. Of the 4080 women who met inclusion criteria, shoulder dystocia occurred in 162 (4.0%) women. In bivariable analysis, maternal age, parity, body mass index, diabetes, chorioamnionitis, arrest disorder as an indication for an operative vaginal delivery, vacuum use, and estimated fetal weight >4 kg were significantly associated with shoulder dystocia. In multivariable analysis, parity, diabetes, chorioamnionitis, arrest disorder as an indication for operative vaginal delivery, vacuum use, and estimated fetal weight >4 kg remained independently associated with shoulder dystocia. The area under the curve for the generated receiver operating characteristic curve was 0.73 (95% confidence interval, 0.69-0.77), demonstrating only a modest ability to predict shoulder dystocia before performing an operative vaginal delivery. While risk factors for shoulder dystocia at the time of operative vaginal delivery can be identified, reliable prediction of shoulder dystocia in this setting cannot be attained. Copyright © 2016 Elsevier Inc. All rights reserved.

Predicting Failure of Glyburide Therapy in Gestational Diabetes

PubMed Central

Harper, Lorie M.; Glover, Angelica V.; Biggio, Joseph R.; Tita, Alan

2016-01-01

Objective We sought to develop a prediction model to identify women with gestational diabetes (GDM) who require insulin to achieve glycemic control. Study Design Retrospective cohort of all singletons with GDM treated with glyburide 2007–2013. Glyburide failure was defined as reaching glyburide 20 mg/day and receiving insulin. Glyburide success was defined as any glyburide dose without insulin and >70% of visits with glycemic control. Multivariable logistic regression analysis was performed to create a prediction model. Results Of 360 women, 63 (17.5%) qualified as glyburide failure and 157 (43.6%) glyburide success. The final prediction model for glyburide failure included prior GDM, GDM diagnosis ≤26 weeks, 1-hour GCT ≥228 mg/dL, 3-hour GTT 1-hour value ≥221 mg/dL, ≥7 post-prandial blood sugars >120 mg/dL in the week glyburide started, and ≥1 blood sugar >200 mg/dL. The model accurately classified 81% of subjects. Conclusions Women with GDM who will require insulin can be identified at initiation of pharmacologic therapy. PMID:26796130

Predicting failure of glyburide therapy in gestational diabetes.

PubMed

Harper, L M; Glover, A V; Biggio, J R; Tita, A

2016-05-01

We sought to develop a prediction model to identify women with gestational diabetes (GDM) who require insulin to achieve glycemic control. Retrospective cohort of all singletons with GDM treated with glyburide from 2007 to 2013. Glyburide failure was defined as reaching glyburide 20 mg day(-1) and receiving insulin. Glyburide success was defined as any glyburide dose without insulin and >70% of visits with glycemic control. Multivariable logistic regression analysis was performed to create a prediction model. Of the 360 women, 63 (17.5%) qualified as glyburide failure and 157 (43.6%) as glyburide success. The final prediction model for glyburide failure included prior GDM, GDM diagnosis ⩽26 weeks, 1-h glucose challenge test ⩾228 mg dl(-1), 3-h glucose tolerance test 1-h value ⩾221 mg dl(-1), ⩾7 postprandial blood sugars >120 mg dl(-1) in the week glyburide started and ⩾1 blood sugar >200 mg dl(-1). The model accurately classified 81% of subjects. Women with GDM who will require insulin can be identified at the initiation of pharmacological therapy.

Differential associations of threat and deprivation with emotion regulation and cognitive control in adolescence

PubMed Central

Lambert, Hilary K; King, kevin M; Monahan, kathryn C; Mclaughlin, Katie A

2016-01-01

Research on childhood adversity has traditionally focused on single types of adversity, which is limited because of high co-occurrence, or on the total number of adverse experiences, which assumes that diverse experiences influence development similarly. Identifying dimensions of environmental experience that are common to multiple types of adversity may be a more effective strategy. We examined the unique associations of two such dimensions (threat and cognitive deprivation) with automatic emotion regulation and cognitive control using a multivariate approach that simultaneously examined both dimensions of adversity. Data were drawn from a community sample of adolescents (N = 287) with variability in exposure to violence, an indicator of threat, and poverty, which is associated with cognitive deprivation. Adolescents completed tasks measuring automatic emotion regulation and cognitive control in neutral and emotional contexts. Violence was associated with automatic emotion regulation deficits, but not cognitive control; poverty was associated with poor cognitive control, but not automatic emotion regulation. Both violence and poverty predicted poor inhibition in an emotional context. Utilizing an approach focused on either single types of adversity or cumulative risk obscured specificity in the associations of violence and poverty with emotional and cognitive outcomes. These findings suggest that different dimensions of childhood adversity have distinct influences on development and highlight the utility of a differentiated multivariate approach. PMID:27424571

Modeling in the quality by design environment: Regulatory requirements and recommendations for design space and control strategy appointment.

PubMed

Djuris, Jelena; Djuric, Zorica

2017-11-30

Mathematical models can be used as an integral part of the quality by design (QbD) concept throughout the product lifecycle for variety of purposes, including appointment of the design space and control strategy, continual improvement and risk assessment. Examples of different mathematical modeling techniques (mechanistic, empirical and hybrid) in the pharmaceutical development and process monitoring or control are provided in the presented review. In the QbD context, mathematical models are predominantly used to support design space and/or control strategies. Considering their impact to the final product quality, models can be divided into the following categories: high, medium and low impact models. Although there are regulatory guidelines on the topic of modeling applications, review of QbD-based submission containing modeling elements revealed concerns regarding the scale-dependency of design spaces and verification of models predictions at commercial scale of manufacturing, especially regarding real-time release (RTR) models. Authors provide critical overview on the good modeling practices and introduce concepts of multiple-unit, adaptive and dynamic design space, multivariate specifications and methods for process uncertainty analysis. RTR specification with mathematical model and different approaches to multivariate statistical process control supporting process analytical technologies are also presented. Copyright © 2017 Elsevier B.V. All rights reserved.

Differential associations of threat and deprivation with emotion regulation and cognitive control in adolescence.

PubMed

Lambert, Hilary K; King, Kevin M; Monahan, Kathryn C; McLaughlin, Katie A

2017-08-01

Research on childhood adversity has traditionally focused on single types of adversity, which is limited because of high co-occurrence, or on the total number of adverse experiences, which assumes that diverse experiences influence development similarly. Identifying dimensions of environmental experience that are common to multiple types of adversity may be a more effective strategy. We examined the unique associations of two such dimensions (threat and cognitive deprivation) with automatic emotion regulation and cognitive control using a multivariate approach that simultaneously examined both dimensions of adversity. Data were drawn from a community sample of adolescents (N = 287) with variability in exposure to violence, an indicator of threat, and poverty, which is associated with cognitive deprivation. Adolescents completed tasks measuring automatic emotion regulation and cognitive control in neutral and emotional contexts. Violence was associated with automatic emotion regulation deficits, but not cognitive control; poverty was associated with poor cognitive control, but not automatic emotion regulation. Both violence and poverty predicted poor inhibition in an emotional context. Utilizing an approach focused on either single types of adversity or cumulative risk obscured specificity in the associations of violence and poverty with emotional and cognitive outcomes. These findings suggest that different dimensions of childhood adversity have distinct influences on development and highlight the utility of a differentiated multivariate approach.

PHI and PCA3 improve the prognostic performance of PRIAS and Epstein criteria in predicting insignificant prostate cancer in men eligible for active surveillance.

PubMed

Cantiello, Francesco; Russo, Giorgio Ivan; Cicione, Antonio; Ferro, Matteo; Cimino, Sebastiano; Favilla, Vincenzo; Perdonà, Sisto; De Cobelli, Ottavio; Magno, Carlo; Morgia, Giuseppe; Damiano, Rocco

2016-04-01

To assess the performance of prostate health index (PHI) and prostate cancer antigen 3 (PCA3) when added to the PRIAS or Epstein criteria in predicting the presence of pathologically insignificant prostate cancer (IPCa) in patients who underwent radical prostatectomy (RP) but eligible for active surveillance (AS). An observational retrospective study was performed in 188 PCa patients treated with laparoscopic or robot-assisted RP but eligible for AS according to Epstein or PRIAS criteria. Blood and urinary specimens were collected before initial prostate biopsy for PHI and PCA3 measurements. Multivariate logistic regression analyses and decision curve analysis were carried out to identify predictors of IPCa using the updated ERSPC definition. At the multivariate analyses, the inclusion of both PCA3 and PHI significantly increased the accuracy of the Epstein multivariate model in predicting IPCa with an increase of 17 % (AUC = 0.77) and of 32 % (AUC = 0.92), respectively. The inclusion of both PCA3 and PHI also increased the predictive accuracy of the PRIAS multivariate model with an increase of 29 % (AUC = 0.87) and of 39 % (AUC = 0.97), respectively. DCA revealed that the multivariable models with the addition of PHI or PCA3 showed a greater net benefit and performed better than the reference models. In a direct comparison, PHI outperformed PCA3 performance resulting in higher net benefit. In a same cohort of patients eligible for AS, the addition of PHI and PCA3 to Epstein or PRIAS models improved their prognostic performance. PHI resulted in greater net benefit in predicting IPCa compared to PCA3.

Prediction of concurrent endometrial carcinoma in women with endometrial hyperplasia.

PubMed

Matsuo, Koji; Ramzan, Amin A; Gualtieri, Marc R; Mhawech-Fauceglia, Paulette; Machida, Hiroko; Moeini, Aida; Dancz, Christina E; Ueda, Yutaka; Roman, Lynda D

2015-11-01

Although a fraction of endometrial hyperplasia cases have concurrent endometrial carcinoma, patient characteristics associated with concurrent malignancy are not well described. The aim of our study was to identify predictive clinico-pathologic factors for concurrent endometrial carcinoma among patients with endometrial hyperplasia. A case-control study was conducted to compare endometrial hyperplasia in both preoperative endometrial biopsy and hysterectomy specimens (n=168) and endometrial carcinoma in hysterectomy specimen but endometrial hyperplasia in preoperative endometrial biopsy (n=43). Clinico-pathologic factors were examined to identify independent risk factors of concurrent endometrial carcinoma in a multivariate logistic regression model. The most common histologic subtype in preoperative endometrial biopsy was complex hyperplasia with atypia [CAH] (n=129) followed by complex hyperplasia without atypia (n=58) and simple hyperplasia with or without atypia (n=24). The majority of endometrial carcinomas were grade 1 (86.0%) and stage I (83.7%). In multivariate analysis, age 40-59 (odds ratio [OR] 3.07, p=0.021), age≥60 (OR 6.65, p=0.005), BMI≥35kg/m(2) (OR 2.32, p=0.029), diabetes mellitus (OR 2.51, p=0.019), and CAH (OR 9.01, p=0.042) were independent predictors of concurrent endometrial carcinoma. The risk of concurrent endometrial carcinoma rose dramatically with increasing number of risk factors identified in multivariate model (none 0%, 1 risk factor 7.0%, 2 risk factors 17.6%, 3 risk factors 35.8%, and 4 risk factors 45.5%, p<0.001). Hormonal treatment was associated with decreased risk of concurrent endometrial cancer in those with ≥3 risk factors. Older age, obesity, diabetes mellitus, and CAH are predictive of concurrent endometrial carcinoma in endometrial hyperplasia patients. Copyright © 2015 Elsevier Inc. All rights reserved.

The structural equation analysis of childhood abuse, adult stressful life events, and temperaments in major depressive disorders and their influence on refractoriness

PubMed Central

Toda, Hiroyuki; Inoue, Takeshi; Tsunoda, Tomoya; Nakai, Yukiei; Tanichi, Masaaki; Tanaka, Teppei; Hashimoto, Naoki; Nakato, Yasuya; Nakagawa, Shin; Kitaichi, Yuji; Mitsui, Nobuyuki; Boku, Shuken; Tanabe, Hajime; Nibuya, Masashi; Yoshino, Aihide; Kusumi, Ichiro

2015-01-01

Background Previous studies have shown the interaction between heredity and childhood stress or life events on the pathogenesis of a major depressive disorder (MDD). In this study, we tested our hypothesis that childhood abuse, affective temperaments, and adult stressful life events interact and influence the diagnosis of MDD. Patients and methods A total of 170 healthy controls and 98 MDD patients were studied using the following self-administered questionnaire surveys: the Patient Health Questionnaire-9 (PHQ-9), the Life Experiences Survey, the Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire, and the Child Abuse and Trauma Scale (CATS). The data were analyzed with univariate analysis, multivariable analysis, and structural equation modeling. Results The neglect scores of the CATS indirectly predicted the diagnosis of MDD through cyclothymic and anxious temperament scores of the Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire in the structural equation modeling. Two temperaments – cyclothymic and anxious – directly predicted the diagnosis of MDD. The validity of this result was supported by the results of the stepwise multivariate logistic regression analysis as follows: three factors – neglect, cyclothymic, and anxious temperaments – were significant predictors of MDD. Neglect and the total CATS scores were also predictors of remission vs treatment-resistance in MDD patients independently of depressive symptoms. Limitations The sample size was small for the comparison between the remission and treatment-resistant groups in MDD patients in multivariable analysis. Conclusion This study suggests that childhood abuse, especially neglect, indirectly predicted the diagnosis of MDD through increased affective temperaments. The important role as a mediator of affective temperaments in the effect of childhood abuse on MDD was suggested. PMID:26316754

Fault tolerant control of multivariable processes using auto-tuning PID controller.

PubMed

Yu, Ding-Li; Chang, T K; Yu, Ding-Wen

2005-02-01

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.

Estimating the decomposition of predictive information in multivariate systems

NASA Astrophysics Data System (ADS)

Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

2015-03-01

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

Simple and Multivariate Relationships Between Spiritual Intelligence with General Health and Happiness.

PubMed

Amirian, Mohammad-Elyas; Fazilat-Pour, Masoud

2016-08-01

The present study examined simple and multivariate relationships of spiritual intelligence with general health and happiness. The employed method was descriptive and correlational. King's Spiritual Quotient scales, GHQ-28 and Oxford Happiness Inventory, are filled out by a sample consisted of 384 students, which were selected using stratified random sampling from the students of Shahid Bahonar University of Kerman. Data are subjected to descriptive and inferential statistics including correlations and multivariate regressions. Bivariate correlations support positive and significant predictive value of spiritual intelligence toward general health and happiness. Further analysis showed that among the Spiritual Intelligence' subscales, Existential Critical Thinking Predicted General Health and Happiness, reversely. In addition, happiness was positively predicted by generation of personal meaning and transcendental awareness. The findings are discussed in line with the previous studies and the relevant theoretical background.

The insertion of human dynamics models in the flight control loops of V/STOL research aircraft. Appendix 2: The optimal control model of a pilot in V/STOL aircraft control loops

NASA Technical Reports Server (NTRS)

Zipf, Mark E.

1989-01-01

An overview is presented of research work focussed on the design and insertion of classical models of human pilot dynamics within the flight control loops of V/STOL aircraft. The pilots were designed and configured for use in integrated control system research and design. The models of human behavior that were considered are: McRuer-Krendel (a single variable transfer function model); and Optimal Control Model (a multi-variable approach based on optimal control and stochastic estimation theory). These models attempt to predict human control response characteristics when confronted with compensatory tracking and state regulation tasks. An overview, mathematical description, and discussion of predictive limitations of the pilot models is presented. Design strategies and closed loop insertion configurations are introduced and considered for various flight control scenarios. Models of aircraft dynamics (both transfer function and state space based) are developed and discussed for their use in pilot design and application. Pilot design and insertion are illustrated for various flight control objectives. Results of pilot insertion within the control loops of two V/STOL research aricraft (Sikorski Black Hawk UH-60A, McDonnell Douglas Harrier II AV-8B) are presented and compared against actual pilot flight data. Conclusions are reached on the ability of the pilot models to adequately predict human behavior when confronted with similar control objectives.

Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

PubMed

Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

2017-03-01

The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

Planned secondary wound closure at the circular stapler insertion site after laparoscopic gastric bypass reduces postoperative morbidity, costs, and hospital stay.

PubMed

Vetter, Diana; Raptis, Dimitri Aristotle; Giama, Mira; Hosa, Hanna; Muller, Markus K; Nocito, Antonio; Schiesser, Marc; Moos, Rudolf; Bueter, Marco

2017-12-01

The aims of the present study were to assess whether planned secondary wound closure at the insertion site of the circular stapler reduces wound infection rate and postoperative morbidity after laparoscopic Roux-en-Y gastric bypass (RYGB) and to identify independent predictive factors increasing the risk for wound infections after RYGB. This paper is a retrospective single-center analysis of a prospectively collected database of 1400 patients undergoing RYGB surgery in circular technique between June 2000 and June 2016. Planned secondary wound closure at the circular stapler introduction site was performed at postoperative day 3 in 291 (20.8%) consecutive patients and compared to a historical control of 1109 (79.2%) consecutive patients with primary wound closure. Independent predictive factors for wound infection were assessed by multivariable analysis. Secondary wound closure significantly decreased wound infection rate from 9.3% (103/1109) to 1% (3/291) (p < 0.001) leading to a shorter hospital stay (mean 9 (SD8) vs. 7 days (SD2), p < 0.001), lower costs (p = 0.039), and reduced postoperative morbidity (mean 90-day Comprehensive Complication Index (CCI) 7.4 (SD14.0) vs. 5.1 (SD11.1) p = 0.008) when compared to primary wound closure. Primary wound closure, dyslipidemia, and preoperative gastritis were independent predictive risk factors for developing wound infections both in the univariate (p < 0.001; p = 0.048; p = 0.003) and multivariable analysis (p < 0.001; p = 0.040; p = 0.012). Further, on multivariable analysis, the female gender was a predictive factor (p = 0.034) for wound infection development. Secondary wound closure at the circular stapler introduction site in laparoscopic RYGB significantly reduces the overall wound infection rate as well as postoperative morbidity, costs, and hospital stay when compared to primary wound closure.

Detectable end of radiation prostate specific antigen assists in identifying men with unfavorable intermediate-risk prostate cancer at high risk of distant recurrence and cancer-specific mortality.

PubMed

Hayman, Jonathan; Phillips, Ryan; Chen, Di; Perin, Jamie; Narang, Amol K; Trieu, Janson; Radwan, Noura; Greco, Stephen; Deville, Curtiland; McNutt, Todd; Song, Daniel Y; DeWeese, Theodore L; Tran, Phuoc T

2018-06-01

Undetectable End of Radiation PSA (EOR-PSA) has been shown to predict improved survival in prostate cancer (PCa). While validating the unfavorable intermediate-risk (UIR) and favorable intermediate-risk (FIR) stratifications among Johns Hopkins PCa patients treated with radiotherapy, we examined whether EOR-PSA could further risk stratify UIR men for survival. A total of 302 IR patients were identified in the Johns Hopkins PCa database (178 UIR, 124 FIR). Kaplan-Meier curves and multivariable analysis was performed via Cox regression for biochemical recurrence free survival (bRFS), distant metastasis free survival (DMFS), and overall survival (OS), while a competing risks model was used for PCa specific survival (PCSS). Among the 235 patients with known EOR-PSA values, we then stratified by EOR-PSA and performed the aforementioned analysis. The median follow-up time was 11.5 years (138 months). UIR was predictive of worse DMFS and PCSS (P = 0.008 and P = 0.023) on multivariable analysis (MVA). Increased radiation dose was significant for improved DMFS (P = 0.016) on MVA. EOR-PSA was excluded from the models because it did not trend towards significance as a continuous or binary variable due to interaction with UIR, and we were unable to converge a multivariable model with a variable to control for this interaction. However, when stratifying by detectable versus undetectable EOR-PSA, UIR had worse DMFS and PCSS among detectable EOR-PSA patients, but not undetectable patients. UIR was significant on MVA among detectable EOR-PSA patients for DMFS (P = 0.021) and PCSS (P = 0.033), while RT dose also predicted PCSS (P = 0.013). EOR-PSA can assist in predicting DMFS and PCSS among UIR patients, suggesting a clinically meaningful time point for considering intensification of treatment in clinical trials of intermediate-risk men. © 2018 Wiley Periodicals, Inc.

Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model.

PubMed

Wang, Ming; Li, Zheng; Lee, Eun Young; Lewis, Mechelle M; Zhang, Lijun; Sterling, Nicholas W; Wagner, Daymond; Eslinger, Paul; Du, Guangwei; Huang, Xuemei

2017-09-25

It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

The Smoking Consequences Questionnaire: Factor structure and predictive validity among Spanish-speaking Latino smokers in the United States.

PubMed

Vidrine, Jennifer Irvin; Vidrine, Damon J; Costello, Tracy J; Mazas, Carlos; Cofta-Woerpel, Ludmila; Mejia, Luz Maria; Wetter, David W

2009-11-01

Much of the existing research on smoking outcome expectancies has been guided by the Smoking Consequences Questionnaire (SCQ ). Although the original version of the SCQ has been modified over time for use in different populations, none of the existing versions have been evaluated for use among Spanish-speaking Latino smokers in the United States. The present study evaluated the factor structure and predictive validity of the 3 previously validated versions of the SCQ--the original, the SCQ-Adult, and the SCQ-Spanish, which was developed with Spanish-speaking smokers in Spain--among Spanish-speaking Latino smokers in Texas. The SCQ-Spanish represented the least complex solution. Each of the SCQ-Spanish scales had good internal consistency, and the predictive validity of the SCQ-Spanish was partially supported. Nearly all the SCQ-Spanish scales predicted withdrawal severity even after controlling for demographics and dependence. Boredom Reduction predicted smoking relapse across the 5- and 12-week follow-up assessments in a multivariate model that also controlled for demographics and dependence. Our results support use of the SCQ-Spanish with Spanish-speaking Latino smokers in the United States.

Sensory cortex hyperexcitability predicts short survival in amyotrophic lateral sclerosis.

PubMed

Shimizu, Toshio; Bokuda, Kota; Kimura, Hideki; Kamiyama, Tsutomu; Nakayama, Yuki; Kawata, Akihiro; Isozaki, Eiji; Ugawa, Yoshikazu

2018-05-01

To investigate somatosensory cortex excitability and its relationship to survival prognosis in patients with amyotrophic lateral sclerosis (ALS). A total of 145 patients with sporadic ALS and 73 healthy control participants were studied. We recorded compound muscle action potential and sensory nerve action potential of the median nerve and the median nerve somatosensory evoked potential (SEP), and we measured parameters, including onset-to-peak amplitude of N13 and N20 and peak-to-peak amplitude between N20 and P25 (N20p-P25p). Clinical prognostic factors, including ALS Functional Rating Scale-Revised, were evaluated. We followed up patients until the endpoints (death or tracheostomy) and analyzed factors associated with survival using multivariate analysis in the Cox proportional hazard model. Compared to controls, patients with ALS showed a larger amplitude of N20p-P25p in the median nerve SEP. Median survival time after examination was shorter in patients with N20p-P25p ≥8 μV (0.82 years) than in those with N20p-P25p <8 μV (1.68 years, p = 0.0002, log-rank test). Multivariate analysis identified a larger N20p-P25p amplitude as a factor that was independently associated with shorter survival ( p = 0.002). Sensory cortex hyperexcitability predicts short survival in patients with ALS. © 2018 American Academy of Neurology.

Multivariate Models of Parent-Late Adolescent Gender Dyads: The Importance of Parenting Processes in Predicting Adjustment

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2008-01-01

Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample…

Study and classification of the abdominal adiposity throughout the application of the two-dimensional predictive equation Garaulet et al., in the clinical practice.

PubMed

Piernas Sánchez, C M; Morales Falo, E M; Zamora Navarro, S; Garaulet Aza, M

2010-01-01

The excess of visceral abdominal adipose tissue is one of the major concerns in obesity and its clinical treatment. To apply the two-dimensional predictive equation proposed by Garaulet et al. to determine the abdominal fat distribution and to compare the results with the body composition obtained by multi-frequency bioelectrical impedance analysis (M-BIA). We studied 230 women, who underwent anthropometry and M-BIA. The predictive equation was applied. Multivariate lineal and partial correlation analyses were performed with control for BMI and % body fat, using SPSS 15.0 with statistical significance P < 0.05. Overall, women were considered as having subcutaneous distribution of abdominal fat. Truncal fat, regional fat and muscular mass were negatively associated with VA/SA(predicted), while the visceral index obtained by M-BIA was positively correlated with VA/SA(predicted). The predictive equation may be useful in the clinical practice to obtain an accurate, costless and safe classification of abdominal obesity.

Evaluation of an F100 multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Skira, C.; Soeder, J. F.

1977-01-01

A multivariable control design for the F100 turbofan engine was evaluated, as part of the F100 multivariable control synthesis (MVCS) program. The evaluation utilized a real-time, hybrid computer simulation of the engine and a digital computer implementation of the control. Significant results of the evaluation are presented and recommendations concerning future engine testing of the control are made.

Pretreatment health behaviors predict survival among patients with head and neck squamous cell carcinoma.

PubMed

Duffy, Sonia A; Ronis, David L; McLean, Scott; Fowler, Karen E; Gruber, Stephen B; Wolf, Gregory T; Terrell, Jeffrey E

2009-04-20

Our prior work has shown that the health behaviors of head and neck cancer patients are interrelated and are associated with quality of life; however, other than smoking, the relationship between health behaviors and survival is unclear. A prospective cohort study was conducted to determine the relationship between five pretreatment health behaviors (smoking, alcohol, diet, physical activity, and sleep) and all-cause survival among 504 head and neck cancer patients. Smoking status was the strongest predictor of survival, with both current smokers (hazard ratio [HR] = 2.4; 95% CI, 1.3 to 4.4) and former smokers (HR = 2.0; 95% CI, 1.2 to 3.5) showing significant associations with poor survival. Problem drinking was associated with survival in the univariate analysis (HR = 1.4; 95% CI, 1.0 to 2.0) but lost significance when controlling for other factors. Low fruit intake was negatively associated with survival in the univariate analysis only (HR = 1.6; 95% CI, 1.1 to 2.1), whereas vegetable intake was not significant in either univariate or multivariate analyses. Although physical activity was associated with survival in the univariate analysis (HR = 0.95; 95% CI, 0.93 to 0.97), it was not significant in the multivariate model. Sleep was not significantly associated with survival in either univariate or multivariate analysis. Control variables that were also independently associated with survival in the multivariate analysis were age, education, tumor site, cancer stage, and surgical treatment. Variation in selected pretreatment health behaviors (eg, smoking, fruit intake, and physical activity) in this population is associated with variation in survival.

Job characteristics and safety climate: the role of effort-reward and demand-control-support models.

PubMed

Phipps, Denham L; Malley, Christine; Ashcroft, Darren M

2012-07-01

While safety climate is widely recognized as a key influence on organizational safety, there remain questions about the nature of its antecedents. One potential influence on safety climate is job characteristics (that is, psychosocial features of the work environment). This study investigated the relationship between two job characteristics models--demand-control-support (Karasek & Theorell, 1990) and effort-reward imbalance (Siegrist, 1996)--and safety climate. A survey was conducted with a random sample of 860 British retail pharmacists, using the job contents questionnaire (JCQ), effort-reward imbalance indicator (ERI) and a measure of safety climate in pharmacies. Multivariate data analyses found that: (a) both models contributed to the prediction of safety climate ratings, with the demand-control-support model making the largest contribution; (b) there were some interactions between demand, control and support from the JCQ in the prediction of safety climate scores. The latter finding suggests the presence of "active learning" with respect to safety improvement in high demand, high control settings. The findings provide further insight into the ways in which job characteristics relate to safety, both individually and at an aggregated level.

Early identification of patients requiring massive transfusion, embolization, or hemostatic surgery for traumatic hemorrhage: a systematic review protocol.

PubMed

Tran, Alexandre; Matar, Maher; Steyerberg, Ewout W; Lampron, Jacinthe; Taljaard, Monica; Vaillancourt, Christian

2017-04-13

Hemorrhage is a major cause of early mortality following a traumatic injury. The progression and consequences of significant blood loss occur quickly as death from hemorrhagic shock or exsanguination often occurs within the first few hours. The mainstay of treatment therefore involves early identification of patients at risk for hemorrhagic shock in order to provide blood products and control of the bleeding source if necessary. The intended scope of this review is to identify and assess combinations of predictors informing therapeutic decision-making for clinicians during the initial trauma assessment. The primary objective of this systematic review is to identify and critically assess any existing multivariable models predicting significant traumatic hemorrhage that requires intervention, defined as a composite outcome comprising massive transfusion, surgery for hemostasis, or angiography with embolization for the purpose of external validation or updating in other study populations. If no suitable existing multivariable models are identified, the secondary objective is to identify candidate predictors to inform the development of a new prediction rule. We will search the EMBASE and MEDLINE databases for all randomized controlled trials and prospective and retrospective cohort studies developing or validating predictors of intervention for traumatic hemorrhage in adult patients 16 years of age or older. Eligible predictors must be available to the clinician during the first hour of trauma resuscitation and may be clinical, lab-based, or imaging-based. Outcomes of interest include the need for surgical intervention, angiographic embolization, or massive transfusion within the first 24 h. Data extraction will be performed independently by two reviewers. Items for extraction will be based on the CHARMS checklist. We will evaluate any existing models for relevance, quality, and the potential for external validation and updating in other populations. Relevance will be described in terms of appropriateness of outcomes and predictors. Quality criteria will include variable selection strategies, adequacy of sample size, handling of missing data, validation techniques, and measures of model performance. This systematic review will describe the availability of multivariable prediction models and summarize evidence regarding predictors that can be used to identify the need for intervention in patients with traumatic hemorrhage. PROSPERO CRD42017054589.

Predictors of health-related quality of life and costs in adults with epilepsy: a systematic review.

PubMed

Taylor, Rod S; Sander, Josemir W; Taylor, Rebecca J; Baker, Gus A

2011-12-01

Given the high burden of epilepsy on both health-related quality of life (HRQoL) and costs, identification of factors that are predictive of either reduced HRQoL or increased expenditure is central to the better future targeting and optimization of existing and emerging interventions and management strategies for epilepsy. Searches of Medline, Embase, and Cochrane Library (up to July 2010) to identify studies examining the association between demographic, psychosocial, and condition-related factors and HRQoL, resource utilization or costs in adults with epilepsy. For each study, predictor factor associations were summarized on the basis of statistical significance and direction; the results were then combined across studies. Ninety-three HRQoL and 16 resource utilization/cost studies were included. Increases in seizure frequency, seizure severity, level of depression, and level of anxiety and presence of comorbidity were strongly associated with reduced HRQoL. The majority of studies were cross-sectional in design and had an overall methodologic quality that was judged to be "moderate" for HRQoL studies and "poor" for health care resource or costs studies. In the 53 multivariate studies, age, gender, marital status, type of seizure, age at diagnosis, and duration of epilepsy did not appear to be associated with HRQoL, whereas the predictive influence of educational and employment status, number of antiepileptic drugs (AEDs) and AED side effects was unclear. The association between predictive factors and HRQoL appeared to be consistent across individuals whether refractory or seizures controlled or managed by AEDs. There were insufficient multivariate studies (five) to reliably comment on the predictors of resource utilization or cost in epilepsy. In addition to seizure control, effective epilepsy management requires the early detection of those most at risk of psychological dysfunction and comorbidity, and the targeting of appropriate interventions. There is need for more rigorous studies with appropriate multivariate statistical methods that prospectively investigate the predictors of HRQoL, resource utilization, and costs in epilepsy. Wiley Periodicals, Inc. © 2011 International League Against Epilepsy.

Bad nights or bad bars? Multi-level analysis of environmental predictors of aggression in late-night large-capacity bars and clubs.

PubMed

Graham, Kathryn; Bernards, Sharon; Osgood, D Wayne; Wells, Samantha

2006-11-01

To clarify environmental predictors of bar-room aggression by differentiating relationships due to nightly variations versus across bar variations, frequency versus severity of aggression and patron versus staff aggression. Male-female pairs of researcher-observers conducted 1334 observations in 118 large capacity (> 300) bars and clubs in Toronto, Canada. Observers independently rated aspects of the environment (e.g. crowding) at every visit and wrote detailed narratives of each incident of aggression that occurred. Measures of severity of aggression for the visit were calculated by aggregating ratings for each person in aggressive incidents. Although bivariate analyses confirmed the significance of most environmental predictors of aggression identified in previous research, multivariate analyses identified the following key visit-level predictors (controlling for bar-level relationships): rowdiness/permissive environment and people hanging around after closing predicted both frequency and severity of aggression; sexual activity, contact and competition and people with two or more drinks at closing predicted frequency but not severity of aggression; lack of staff monitoring predicted more severe patron aggression, while having more and better coordinated staff predicted more severe staff aggression. Intoxication of patrons was significantly associated with more frequent and severe patron aggression at the bar level (but not at the visit level) in the multivariate analyses and negatively associated with severity of staff aggression at the visit level. The results demonstrate clearly the importance of the immediate environment (not just the type of bar or characteristics of usual patrons) and the importance of specific environmental factors, including staff behaviour, in predicting both frequency and severity of aggression.

Multivariate Analysis of Seismic Field Data

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

Alam, M. Kathleen

1999-06-01

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present datamore » sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.« less

Impact of Study Design on Reported Incidences of Acute Mountain Sickness: A Systematic Review.

PubMed

Waeber, Baptiste; Kayser, Bengt; Dumont, Lionel; Lysakowski, Christopher; Tramèr, Martin R; Elia, Nadia

2015-09-01

Published incidences of acute mountain sickness (AMS) vary widely. Reasons for this variation, and predictive factors of AMS, are not well understood. We aimed to identify predictive factors that are associated with the occurrence of AMS, and to test the hypothesis that study design is an independent predictive factor of AMS incidence. We did a systematic search (Medline, bibliographies) for relevant articles in English or French, up to April 28, 2013. Studies of any design reporting on AMS incidence in humans without prophylaxis were selected. Data on incidence and potential predictive factors were extracted by two reviewers and crosschecked by four reviewers. Associations between predictive factors and AMS incidence were sought through bivariate and multivariate analyses for different study designs separately. Association between AMS incidence and study design was assessed using multiple linear regression. We extracted data from 53,603 subjects from 34 randomized controlled trials, 44 cohort studies, and 33 cross-sectional studies. In randomized trials, the median of AMS incidences without prophylaxis was 60% (range, 16%-100%); mode of ascent and population were significantly associated with AMS incidence. In cohort studies, the median of AMS incidences was 51% (0%-100%); geographical location was significantly associated with AMS incidence. In cross-sectional studies, the median of AMS incidences was 32% (0%-68%); mode of ascent and maximum altitude were significantly associated with AMS incidence. In a multivariate analysis, study design (p=0.012), mode of ascent (p=0.003), maximum altitude (p<0.001), population (p=0.002), and geographical location (p<0.001) were significantly associated with AMS incidence. Age, sex, speed of ascent, duration of exposure, or history of AMS were inconsistently reported and therefore not further analyzed. Reported incidences and identifiable predictive factors of AMS depend on study design.

Evaluating the predictive power of multivariate tensor-based morphometry in Alzheimer's disease progression via convex fused sparse group Lasso

NASA Astrophysics Data System (ADS)

Tsao, Sinchai; Gajawelli, Niharika; Zhou, Jiayu; Shi, Jie; Ye, Jieping; Wang, Yalin; Lepore, Natasha

2014-03-01

Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end we combine a predictive multi-task machine learning method1 with novel MR-based multivariate morphometric surface map of the hippocampus2 to predict future cognitive scores of patients. Previous work by Zhou et al.1 has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness. They showed that this can be used in predicting cognitive outcomes of Alzheimers Disease Neuroimaging Initiative (ADNI) subjects based on FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied Shi et al.2s recently developed multivariate tensor-based (mTBM) parametric surface analysis method to extract features from the hippocampal surface. We show that by combining the power of the multi-task framework with the sensitivity of mTBM features of the hippocampus surface, we are able to improve significantly improve predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

Independent Predictors of Prognosis Based on Oral Cavity Squamous Cell Carcinoma Surgical Margins.

PubMed

Buchakjian, Marisa R; Ginader, Timothy; Tasche, Kendall K; Pagedar, Nitin A; Smith, Brian J; Sperry, Steven M

2018-05-01

Objective To conduct a multivariate analysis of a large cohort of oral cavity squamous cell carcinoma (OCSCC) cases for independent predictors of local recurrence (LR) and overall survival (OS), with emphasis on the relationship between (1) prognosis and (2) main specimen permanent margins and intraoperative tumor bed frozen margins. Study Design Retrospective cohort study. Setting Tertiary academic head and neck cancer program. Subjects and Methods This study included 426 patients treated with OCSCC resection between 2005 and 2014 at University of Iowa Hospitals and Clinics. Patients underwent excision of OCSCC with intraoperative tumor bed frozen margin sampling and main specimen permanent margin assessment. Multivariate analysis of the data set to predict LR and OS was performed. Results Independent predictors of LR included nodal involvement, histologic grade, and main specimen permanent margin status. Specifically, the presence of a positive margin (odds ratio, 6.21; 95% CI, 3.3-11.9) or <1-mm/carcinoma in situ margin (odds ratio, 2.41; 95% CI, 1.19-4.87) on the main specimen was an independent predictor of LR, whereas intraoperative tumor bed margins were not predictive of LR on multivariate analysis. Similarly, independent predictors of OS on multivariate analysis included nodal involvement, extracapsular extension, and a positive main specimen margin. Tumor bed margins did not independently predict OS. Conclusion The main specimen margin is a strong independent predictor of LR and OS on multivariate analysis. Intraoperative tumor bed frozen margins do not independently predict prognosis. We conclude that emphasis should be placed on evaluating the main specimen margins when estimating prognosis after OCSCC resection.

Drinking and Parenting Practices as Predictors of Impaired Driving Behaviors Among U.S. Adolescents

PubMed Central

Li, Kaigang; Simons-Morton, Bruce G; Brooks-Russell, Ashley; Ehsani, Johnathon; Hingson, Ralph

2014-01-01

Objective: The purpose of this study was to identify the extent to which 10th-grade substance use and parenting practices predicted 11th-grade teenage driving while alcohol-/other drug–impaired (DWI) and riding with alcohol-/other drug–impaired drivers (RWI). Method: The data were from Waves 1 and 2 of the NEXT Generation study, with longitudinal assessment of a nationally representative sample of 10th graders starting in 2009–2010. Multivariate logistic regression analysis was used to examine the prospective associations between proposed predictors (heavy episodic drinking, illicit drug use, parental monitoring knowledge and control) in Wave 1 and DWI/RWI. Results: Heavy episodic drinking at Wave 1 predicted Wave 2 DWI (odds ratio [OR] = 3.73, p < .001) and RWI (OR = 3.92, p < .001) after controlling for parenting practices and selected covariates. Father’s monitoring knowledge predicted lower DWI prevalence at Wave 2 when controlling for covariates and teenage substance use (OR = 0.66, p < .001). In contrast, mother’s monitoring knowledge predicted lower RWI prevalence at Wave 2 when controlling for covariates only (OR = 0.67, p < .05), but the effect was reduced to nonsignificance when controlling for teen substance use. Conclusions: Heavy episodic drinking predicted DWI and RWI. In addition, parental monitoring knowledge, particularly by fathers, was protective against DWI, independent of the effect of substance use. This suggests that the enhancement of parenting practices could potentially discourage adolescent DWI. The findings suggest that the parenting practices of fathers and mothers may have differential effects on adolescent impaired-driving behaviors. PMID:24411792

Drinking and parenting practices as predictors of impaired driving behaviors among U.S. adolescents.

PubMed

Li, Kaigang; Simons-Morton, Bruce G; Brooks-Russell, Ashley; Ehsani, Johnathon; Hingson, Ralph

2014-01-01

The purpose of this study was to identify the extent to which 10th-grade substance use and parenting practices predicted 11th-grade teenage driving while alcohol-/other drug-impaired (DWI) and riding with alcohol-/other drug-impaired drivers (RWI). The data were from Waves 1 and 2 of the NEXT Generation study, with longitudinal assessment of a nationally representative sample of 10th graders starting in 2009-2010. Multivariate logistic regression analysis was used to examine the prospective associations between proposed predictors (heavy episodic drinking, illicit drug use, parental monitoring knowledge and control) in Wave 1 and DWI/RWI. Heavy episodic drinking at Wave 1 predicted Wave 2 DWI (odds ratio [OR] = 3.73, p < .001) and RWI (OR = 3.92, p < .001) after controlling for parenting practices and selected covariates. Father's monitoring knowledge predicted lower DWI prevalence at Wave 2 when controlling for covariates and teenage substance use (OR = 0.66, p < .001). In contrast, mother's monitoring knowledge predicted lower RWI prevalence at Wave 2 when controlling for covariates only (OR = 0.67, p < .05), but the effect was reduced to nonsignificance when controlling for teen substance use. Heavy episodic drinking predicted DWI and RWI. In addition, parental monitoring knowledge, particularly by fathers, was protective against DWI, independent of the effect of substance use. This suggests that the enhancement of parenting practices could potentially discourage adolescent DWI. The findings suggest that the parenting practices of fathers and mothers may have differential effects on adolescent impaired-driving behaviors.

Predictive value of hippocampal MR imaging-based high-dimensional mapping in mesial temporal epilepsy: preliminary findings.

PubMed

Hogan, R E; Wang, L; Bertrand, M E; Willmore, L J; Bucholz, R D; Nassif, A S; Csernansky, J G

2006-01-01

We objectively assessed surface structural changes of the hippocampus in mesial temporal sclerosis (MTS) and assessed the ability of large-deformation high-dimensional mapping (HDM-LD) to demonstrate hippocampal surface symmetry and predict group classification of MTS in right and left MTS groups compared with control subjects. Using eigenvector field analysis of HDM-LD segmentations of the hippocampus, we compared the symmetry of changes in the right and left MTS groups with a group of 15 matched controls. To assess the ability of HDM-LD to predict group classification, eigenvectors were selected by a logistic regression procedure when comparing the MTS group with control subjects. Multivariate analysis of variance on the coefficients from the first 9 eigenvectors accounted for 75% of the total variance between groups. The first 3 eigenvectors showed the largest differences between the control group and each of the MTS groups, but with eigenvector 2 showing the greatest difference in the MTS groups. Reconstruction of the hippocampal deformation vector fields due solely to eigenvector 2 shows symmetrical patterns in the right and left MTS groups. A "leave-one-out" (jackknife) procedure correctly predicted group classification in 14 of 15 (93.3%) left MTS subjects and all 15 right MTS subjects. Analysis of principal dimensions of hippocampal shape change suggests that MTS, after accounting for normal right-left asymmetries, affects the right and left hippocampal surface structure very symmetrically. Preliminary analysis using HDM-LD shows it can predict group classification of MTS and control hippocampi in this well-defined population of patients with MTS and mesial temporal lobe epilepsy (MTLE).

Multivariate analysis of volatile compounds detected by headspace solid-phase microextraction/gas chromatography: A tool for sensory classification of cork stoppers.

PubMed

Prat, Chantal; Besalú, Emili; Bañeras, Lluís; Anticó, Enriqueta

2011-06-15

The volatile fraction of aqueous cork macerates of tainted and non-tainted agglomerate cork stoppers was analysed by headspace solid-phase microextraction (HS-SPME)/gas chromatography. Twenty compounds containing terpenoids, aliphatic alcohols, lignin-related compounds and others were selected and analysed in individual corks. Cork stoppers were previously classified in six different classes according to sensory descriptions including, 2,4,6-trichloroanisole taint and other frequent, non-characteristic odours found in cork. A multivariate analysis of the chromatographic data of 20 selected chemical compounds using linear discriminant analysis models helped in the differentiation of the a priori made groups. The discriminant model selected five compounds as the best combination. Selected compounds appear in the model in the following order; 2,4,6 TCA, fenchyl alcohol, 1-octen-3-ol, benzyl alcohol and benzothiazole. Unfortunately, not all six a priori differentiated sensory classes were clearly discriminated in the model, probably indicating that no measurable differences exist in the chromatographic data for some categories. The predictive analyses of a refined model in which two sensory classes were fused together resulted in a good classification. Prediction rates of control (non-tainted), TCA, musty-earthy-vegetative, vegetative and chemical descriptions were 100%, 100%, 85%, 67.3% and 100%, respectively, when the modified model was used. The multivariate analysis of chromatographic data will help in the classification of stoppers and provide a perfect complement to sensory analyses. Copyright © 2010 Elsevier Ltd. All rights reserved.

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Self-rated health and mortality: could clinical and performance-based measures of health and functioning explain the association?

PubMed

Lyyra, Tiina-Mari; Heikkinen, Eino; Lyyra, Anna-Liisa; Jylhä, Marja

2006-01-01

It is well established that self-rated health (SRH) predicts mortality even when other indicators of health status are taken into account. It has been suggested that SRH measures a wide array of mortality-related physiological and pathological characteristics not captured by the covariates included in the analyses. Our aim was to test this hypothesis by examining the predictive value of SRH on mortality controlling for different measurements of body structure, performance-based functioning and diagnosed diseases with a population-based, prospective study over an 18-year follow-up. Subjects consisted of 257 male residents of the city of Jyväskylä, central Finland, aged 51-55 and 71-75 years. Among the 71-75-year-olds the association between SRH and mortality was weaker over the longer compared to shorter follow-up period. In the multivariate Cox regression models with an 18-year follow-up time for middle-aged and a10-year follow-up time for older men, SRH predicted mortality even when the anthropometrics, clinical chemistry and performance-based measures of functioning were controlled for, but not when the number of chronic diseases was included. Although our results confirm the hypothesis that the predictive value of SRH can be explained by diagnosed diseases, its predictive power remained, when the clinical and performance-based measures of health and functioning were controlled.

Traits, States, and Attentional Gates: Temperament and Threat Relevance as Predictors of Attentional Bias to Social Threat

PubMed Central

Helzer, Erik G.; Connor-Smith, Jennifer K.; Reed, Marjorie A.

2009-01-01

This study investigated the influence of situational and dispositional factors on attentional biases toward social threat, and the impact of these attentional biases on distress in a sample of adolescents. Results suggest greater biases for personally-relevant threat cues, as individuals reporting high social stress were vigilant to subliminal social threat cues, but not physical threat cues, and those reporting low social stress showed no attentional biases. Individual differences in fearful temperament and attentional control interacted to influence attentional biases, with fearful temperament predicting biases to supraliminal social threat only for individuals with poor attentional control. Multivariate analyses exploring relations between attentional biases for social threat and symptoms of anxiety and depression revealed that attentional biases alone were rarely related to symptoms. However, biases did interact with social stress, fearful temperament, and attentional control to predict distress. Results are discussed in terms of automatic and effortful cognitive mechanisms underlying threat cue processing. PMID:18791905

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; (Peter) He, Qinghua; Lillard, James W.

2015-01-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables. PMID:25664257

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

Does tip-of-the-tongue for proper names discriminate amnestic mild cognitive impairment?

PubMed

Juncos-Rabadán, Onésimo; Facal, David; Lojo-Seoane, Cristina; Pereiro, Arturo X

2013-04-01

Difficulty in retrieving people's names is very common in the early stages of Alzheimer's disease and mild cognitive impairment. Such difficulty is often observed as the tip-of-the-tongue (TOT) phenomenon. The main aim of this study was to explore whether a famous people's naming task that elicited the TOT state can be used to discriminate between amnestic mild cognitive impairment (aMCI) patients and normal controls. Eighty-four patients with aMCI and 106 normal controls aged over 50 years performed a task involving naming 50 famous people shown in pictures. Univariate and multivariate regression analyses were used to study the relationships between aMCI and semantic and phonological measures in the TOT paradigm. Univariate regression analyses revealed that all TOT measures significantly predicted aMCI. Multivariate analysis of all these measures correctly classified 70% of controls (specificity) and 71.6% of aMCI patients (sensitivity), with an AUC (area under curve ROC) value of 0.74, but only the phonological measure remained significant. This classification value was similar to that obtained with the Semantic verbal fluency test. TOTs for proper names may effectively discriminate aMCI patients from normal controls through measures that represent one of the naming processes affected, that is, phonological access.

Do race, neglect, and childhood poverty predict physical health in adulthood? A multilevel prospective analysis.

PubMed

Nikulina, Valentina; Widom, Cathy Spatz

2014-03-01

Childhood neglect and poverty often co-occur and both have been linked to poor physical health outcomes. In addition, Blacks have higher rates of childhood poverty and tend to have worse health than Whites. This paper examines the unique and interacting effects of childhood neglect, race, and family and neighborhood poverty on adult physical health outcomes. This prospective cohort design study uses a sample (N=675) of court-substantiated cases of childhood neglect and matched controls followed into adulthood (M(age)=41). Health indicators (C-Reactive Protein [CRP], hypertension, and pulmonary functioning) were assessed through blood collection and measurements by a registered nurse. Data were analyzed using hierarchical linear models to control for clustering of participants in childhood neighborhoods. Main effects showed that growing up Black predicted CRP and hypertension elevations, despite controlling for neglect and childhood family and neighborhood poverty and their interactions. Multivariate results showed that race and childhood adversities interacted to predict adult health outcomes. Childhood family poverty predicted increased risk for hypertension for Blacks, not Whites. In contrast, among Whites, childhood neglect predicted elevated CRP. Childhood neighborhood poverty interacted with childhood family poverty to predict pulmonary functioning in adulthood. Gender differences in health indicators were also observed. The effects of childhood neglect, childhood poverty, and growing up Black in the United States are manifest in physical health outcomes assessed 30 years later. Implications are discussed. Copyright © 2013 Elsevier Ltd. All rights reserved.

LASSO NTCP predictors for the incidence of xerostomia in patients with head and neck squamous cell carcinoma and nasopharyngeal carcinoma

PubMed Central

Lee, Tsair-Fwu; Liou, Ming-Hsiang; Huang, Yu-Jie; Chao, Pei-Ju; Ting, Hui-Min; Lee, Hsiao-Yi

2014-01-01

To predict the incidence of moderate-to-severe patient-reported xerostomia among head and neck squamous cell carcinoma (HNSCC) and nasopharyngeal carcinoma (NPC) patients treated with intensity-modulated radiotherapy (IMRT). Multivariable normal tissue complication probability (NTCP) models were developed by using quality of life questionnaire datasets from 152 patients with HNSCC and 84 patients with NPC. The primary endpoint was defined as moderate-to-severe xerostomia after IMRT. The numbers of predictive factors for a multivariable logistic regression model were determined using the least absolute shrinkage and selection operator (LASSO) with bootstrapping technique. Four predictive models were achieved by LASSO with the smallest number of factors while preserving predictive value with higher AUC performance. For all models, the dosimetric factors for the mean dose given to the contralateral and ipsilateral parotid gland were selected as the most significant predictors. Followed by the different clinical and socio-economic factors being selected, namely age, financial status, T stage, and education for different models were chosen. The predicted incidence of xerostomia for HNSCC and NPC patients can be improved by using multivariable logistic regression models with LASSO technique. The predictive model developed in HNSCC cannot be generalized to NPC cohort treated with IMRT without validation and vice versa. PMID:25163814

Long-term prediction of prostate cancer diagnosis and death using PSA and obesity related anthropometrics at early middle age: data from the malmö preventive project

PubMed Central

Assel, Melissa J.; Gerdtsson, Axel; Thorek, Daniel L.J.; Carlsson, Sigrid V.; Malm, Johan; Scardino, Peter T.; Vickers, Andrew; Lilja, Hans; Ulmert, David

2018-01-01

Objectives To evaluate whether anthropometric parameters add to PSA measurements in middle-aged men for risk assessment of prostate cancer (PCa) diagnosis and death. Results After adjusting for PSA, both BMI and weight were significantly associated with an increased risk of PCa death with the odds of a death corresponding to a 10 kg/m2 or 10 kg increase being 1.58 (95% CI 1.10, 2.28; p = 0.013) and 1.14 (95% CI 1.02, 1.26; p = 0.016) times greater, respectively. AUCs did not meaningfully increase with the addition of weight or BMI to prediction models including PSA. Materials and Methods In 1974 to 1986, 22,444 Swedish men aged 44 to 50 enrolled in Malmö Preventive Project, Sweden, and provided blood samples and anthropometric data. Rates of PSA screening in the cohort were very low. Documentation of PCa diagnosis and disease-specific death up to 2014 was retrieved through national registries. Among men with anthropometric measurements available at baseline, a total of 1692 men diagnosed with PCa were matched to 4190 controls, and 464 men who died of disease were matched to 1390 controls. Multivariable conditional logistic regression was used to determine whether diagnosis or death from PCa were associated with weight and body mass index (BMI) at adulthood after adjusting for PSA. Conclusions Men with higher BMI and weight at early middle age have an increased risk of PCa diagnosis and death after adjusting for PSA. However, in a multi-variable numerical statistical model, BMI and weight do not importantly improve the predictive accuracy of PSA. Risk-stratification of screening should be based on PSA without reference to anthropometrics. PMID:29464033

Cytologic anaplasia is a prognostic factor in osteosarcoma biopsies, but mitotic rate or extent of spontaneous tumor necrosis are not: a critique of the College of American Pathologists Bone Biopsy template.

PubMed

Cates, Justin Mm; Dupont, William D

2017-01-01

The current College of American Pathologists cancer template for reporting biopsies of bone tumors recommends including information that is of unproven prognostic significance for osteosarcoma, such as the presence of spontaneous tumor necrosis and mitotic rate. Conversely, the degree of cytologic anaplasia (degree of differentiation) is not reported in this template. This retrospective cohort study of 125 patients with high-grade osteosarcoma was performed to evaluate the prognostic impact of these factors in diagnostic biopsy specimens in predicting the clinical outcome and response to neoadjuvant chemotherapy. Multivariate Cox regression was performed to adjust survival analyses for well-established prognostic factors. Multivariate logistic regression was used to determine odds ratios for good chemotherapy response (≥90% tumor necrosis). Osteosarcomas with severe anaplasia were independently associated with increased overall and disease-free survival, but mitotic rate and spontaneous necrosis had no prognostic impact after controlling for other confounding factors. Mitotic rate showed a trend towards increased odds of a good histologic response, but this effect was diminished after controlling for other predictive factors. Neither spontaneous necrosis nor the degree of cytologic anaplasia observed in biopsy specimens was predictive of a good response to chemotherapy. Mitotic rate and spontaneous tumor necrosis observed in pretreatment biopsy specimens of high-grade osteosarcoma are not strong independent prognostic factors for clinical outcome or predictors of response to neoadjuvant chemotherapy. Therefore, reporting these parameters for osteosarcoma, as recommended in the College of American Pathologists Bone Biopsy template, does not appear to have clinical utility. In contrast, histologic grading schemes for osteosarcoma based on the degree of cytologic anaplasia may have independent prognostic value and should continue to be evaluated.

Relation of Monocyte/High-Density Lipoprotein Cholesterol Ratio with Coronary Artery Disease in Type 2 Diabetes Mellitus.

PubMed

Ya, Gao; Qiu, Zhang; Tianrong, Pan

2018-06-01

Atherosclerotic cardiovascular disease is the leading cause of mortality of patients with type 2 diabetes mellitus, and both coronary artery disease (CAD) and diabetes mellitus are associated with inflammation. Emerging evidence suggests a relationship of the monocyte to high-density lipoprotein cholesterol ratio (MHR) with the incidence and severity of CAD. The aim of the present study was to examine the association of MHR with CAD in patients with type 2 diabetes mellitus. A total of 458 consecutive individuals were enrolled, comprising 178 type 2 diabetic patients, 124 type 2 diabetes with CAD, and 156 healthy volunteers as the controls. A multivariable logistic regression model was used to evaluate the relationship between the MHR and CAD in type 2 diabetes, and the receiver operating characteristic (ROC) curve of MHR was used for predicting the presence of CAD in type 2 diabetic patients. Values of MHR were significantly higher in type 2 diabetic patients with CAD compared with those without CAD and the control group. Moreover, multivariate logistic regression analysis showed that MHR was an independent predictor of the presence of CAD in type 2 diabetic patients (OR = 1.361, 95% CI 1.245 - 1.487, p < 0.0001). Based on the receiver operating characteristic (ROC) curve, the cutoff value of MHR (> 8.2) in predicting the presence of CAD in type 2 diabetic patients yields a sensitivity and specificity of 83.74% and 62.15%, respectively, with an area under the curve of 0.795 (95% CI: 0.745 - 0.840). The MHR is strongly associated with CAD in type 2 diabetes and might be a potential biomarker to predict the presence of CAD in type 2 diabetic patients.

Long-term prediction of prostate cancer diagnosis and death using PSA and obesity related anthropometrics at early middle age: data from the malmö preventive project.

PubMed

Assel, Melissa J; Gerdtsson, Axel; Thorek, Daniel L J; Carlsson, Sigrid V; Malm, Johan; Scardino, Peter T; Vickers, Andrew; Lilja, Hans; Ulmert, David

2018-01-19

To evaluate whether anthropometric parameters add to PSA measurements in middle-aged men for risk assessment of prostate cancer (PCa) diagnosis and death. After adjusting for PSA, both BMI and weight were significantly associated with an increased risk of PCa death with the odds of a death corresponding to a 10 kg/m2 or 10 kg increase being 1.58 (95% CI 1.10, 2.28; p = 0.013) and 1.14 (95% CI 1.02, 1.26; p = 0.016) times greater, respectively. AUCs did not meaningfully increase with the addition of weight or BMI to prediction models including PSA. In 1974 to 1986, 22,444 Swedish men aged 44 to 50 enrolled in Malmö Preventive Project, Sweden, and provided blood samples and anthropometric data. Rates of PSA screening in the cohort were very low. Documentation of PCa diagnosis and disease-specific death up to 2014 was retrieved through national registries. Among men with anthropometric measurements available at baseline, a total of 1692 men diagnosed with PCa were matched to 4190 controls, and 464 men who died of disease were matched to 1390 controls. Multivariable conditional logistic regression was used to determine whether diagnosis or death from PCa were associated with weight and body mass index (BMI) at adulthood after adjusting for PSA. Men with higher BMI and weight at early middle age have an increased risk of PCa diagnosis and death after adjusting for PSA. However, in a multi-variable numerical statistical model, BMI and weight do not importantly improve the predictive accuracy of PSA. Risk-stratification of screening should be based on PSA without reference to anthropometrics.

Blood glucose control for patients with acute coronary syndromes in Qatar.

PubMed

Wilby, Kyle John; Elmekaty, Eman; Abdallah, Ibtihal; Habra, Masa; Al-Siyabi, Khalid

2016-01-01

Blood glucose is known to be elevated in patients presenting with acute coronary syndromes. However a gap in knowledge exists regarding effective management strategies once admitted to acute care units. It is also unknown what factors (if any) predict elevated glucose values during initial presentation. OBJECTIVES of the study were to characterize blood glucose control in patients admitted to the cardiac care unit (CCU) in Qatar and to determine predictive factors associated with high glucose levels (>10 mmol/l) on admission to the CCU. All data for this study were obtained from the CCU at Heart Hospital in Doha, Qatar. A retrospective chart review was completed for patients admitted to the CCU in Qatar from October 1st, 2012 to March 31st, 2013, of which 283 were included. Baseline characteristics (age, gender, nationality, medical history, smoking status, type of acute coronary syndrome), capillary and lab blood glucose measurements, and use of insulin were extracted. Time spent in glucose ranges of <4, 4 to <8, 8 to <10, and >10 mmol/1 was calculated manually. Univariate and multivariate logistic regression were performed to assess factors associated with high glucose on admission. The primary analysis was completed with capillary data and a sensitivity analysis was completed using laboratory data. Blood glucose values measured on admission and throughout length of stay in the CCU. Capillary blood glucose data showed majority of time was spent in the range of >10 mmol/l (41.95%), followed by 4-8 mmol/l (35.44%), then 8-10 mmol/l (21.45%), and finally <4 mmol/l (1.16%). As a sensitivity analysis, laboratory data showed very similar findings. Diabetes, hypertension, and non-smoker status predicted glucose values >10 mmol/l on admission (p < 0.05) in a univariate analysis but only diabetes remained significant in a multivariate model (OR 23.3; 95% CI, 11.5-47.3). Diabetes predicts high glucose values on hospital admission for patients with ACS and patients are not being adequately controlled throughout CCU stay.

The role of steroids in the prediction of affective disorders in adult men.

PubMed

Šrámková, Monika; Dušková, Michaela; Hill, Martin; Bičíková, Marie; Řípová, Daniela; Mohr, Pavel; Stárka, Luboslav

2017-05-01

Anxiety and mood disorders (AMD) are the most frequent mental disorders in the human population. They have recently shown increasing prevalence, and commonly disrupt personal and working lives. The aim of our study was to analyze the spectrum of circulating steroids in order to discover differences that could potentially be markers of affective depression or anxiety, and identify which steroids could be a predictive component for these diseases. We studied the steroid metabolome including 47 analytes in 20 men with depression (group D), 20 men with anxiety (group AN) and 30 healthy controls. OPLS and multivariate regression models were used for statistical analysis. Discrimination of group D from controls by the OPLS method was absolute, as was group AN from controls (sensitivity=1.000 (0.839, 1.000), specificity=1.000 (0.887, 1.000)). Relatively good predictivity was also found for discrimination between group D from AN (sensitivity=0.850 (0.640, 0.948), specificity=0.900 (0.699, 0.972)). Selected circulating steroids, including those that are neuroactive and neuroprotective, can be useful tools for discriminating between these affective diseases in adult men. Copyright © 2016. Published by Elsevier Inc.

Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling.

PubMed

Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt

2017-03-01

Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Multivariate analysis of risk factors for long-term urethroplasty outcome.

PubMed

Breyer, Benjamin N; McAninch, Jack W; Whitson, Jared M; Eisenberg, Michael L; Mehdizadeh, Jennifer F; Myers, Jeremy B; Voelzke, Bryan B

2010-02-01

We studied the patient risk factors that promote urethroplasty failure. Records of patients who underwent urethroplasty at the University of California, San Francisco Medical Center between 1995 and 2004 were reviewed. Cox proportional hazards regression analysis was used to identify multivariate predictors of urethroplasty outcome. Between 1995 and 2004, 443 patients of 495 who underwent urethroplasty had complete comorbidity data and were included in analysis. Median patient age was 41 years (range 18 to 90). Median followup was 5.8 years (range 1 month to 10 years). Stricture recurred in 93 patients (21%). Primary estimated stricture-free survival at 1, 3 and 5 years was 88%, 82% and 79%. After multivariate analysis smoking (HR 1.8, 95% CI 1.0-3.1, p = 0.05), prior direct vision internal urethrotomy (HR 1.7, 95% CI 1.0-3.0, p = 0.04) and prior urethroplasty (HR 1.8, 95% CI 1.1-3.1, p = 0.03) were predictive of treatment failure. On multivariate analysis diabetes mellitus showed a trend toward prediction of urethroplasty failure (HR 2.0, 95% CI 0.8-4.9, p = 0.14). Length of urethral stricture (greater than 4 cm), prior urethroplasty and failed endoscopic therapy are predictive of failure after urethroplasty. Smoking and diabetes mellitus also may predict failure potentially secondary to microvascular damage. Copyright 2010 American Urological Association. Published by Elsevier Inc. All rights reserved.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

Factors Predictive of Healing in Large Rotator Cuff Tears: Is It Possible to Predict Retear Preoperatively?

PubMed

Jeong, Ho Yeon; Kim, Hwan Jin; Jeon, Yoon Sang; Rhee, Yong Girl

2018-03-01

Many studies have identified risk factors that cause retear after rotator cuff repair. However, it is still questionable whether retears can be predicted preoperatively. To determine the risk factors related to retear after arthroscopic rotator cuff repair and to evaluate whether it is possible to predict the occurrence of retear preoperatively. Case-control study; Level of evidence, 3. This study enrolled 112 patients who underwent arthroscopic rotator cuff repair with single-row technique for a large-sized tear, defined as a tear with a mediolateral length of 3 to 5 cm. All patients underwent routine magnetic resonance imaging (MRI) at 9 months postoperatively to assess tendon integrity. The sample included 61 patients (54.5%) in the healed group and 51 (45.5%) in the retear group. In multivariate analysis, the independent predictors of retears were supraspinatus muscle atrophy ( P < .001) and fatty infiltration of the infraspinatus ( P = .027), which could be preoperatively measured by MRI. A significant difference was found between the two groups in sex, the acromiohumeral interval, tendon tension, and preoperative or intraoperative mediolateral tear length and musculotendinous junction position in univariate analysis. However, these variables were not independent predictors in multivariate analysis. The cutoff values of occupation ratio of supraspinatus and fatty infiltration of the infraspinatus were 43% and grade 2, respectively. The occupation ratio of supraspinatus <43% and grade ≥2 fatty infiltration of the infraspinatus were the strongest predictors of retear, with an area under the curve of 0.908, sensitivity of 98.0%, and specificity of 83.6% (accuracy = 90.2%). In patients with large rotator cuff tears, it was possible to predict the retear before rotator cuff repair regardless of intraoperative factors. The retear could be predicted most effectively when the occupation ratio of supraspinatus was <43% or the fatty infiltration of infraspinatus was grade ≥2. Predicting retear preoperatively may help surgeons determine proper treatment and predict the postoperative prognosis.

Pattern of tumour growth of the primary colon cancer predicts long-term outcome after resection of liver metastases.

PubMed

Spelt, Lidewij; Sasor, Agata; Ansari, Daniel; Andersson, Roland

2016-10-01

To identify significant predictive factors for overall survival (OS) and disease-free survival (DFS) after liver resection for colon cancer metastases, with special focus on features of the primary colon cancer, such as lymph node ratio (LNR), vascular invasion, and perineural invasion. Patients operated for colonic cancer liver metastases between 2006 and 2014 were included. Details on patient characteristics, the primary colon cancer operation and metastatic disease were collected. Multivariate analysis was performed to select predictive variables for OS and DFS. Median OS and DFS were 67 and 20 months, respectively. 1-, 3- and 5-year OS were 97, 76, and 52%. 1-, 3- and 5-year DFS were 65, 42, and 37%. Multivariate analysis showed LNR to be an independent predictive factor for DFS but not for OS. Other identified predictive factors were vascular and perineural invasion of the primary colon cancer, size of the largest metastasis and severe complications after liver surgery for OS, and perineural invasion, number of liver metastases and preoperative CEA-level for DFS. Traditional N-stage was also considered to be an independent predictive factor for DFS in a separate multivariate analysis. LNR and perineural invasion of the primary colon cancer can be used as a prognostic variable for DFS after a concomitant liver resection for colon cancer metastases. Vascular and perineural invasion of the primary colon cancer are predictive for OS.

Proteomic Biomarkers for Acute Interstitial Lung Disease in Gefitinib-Treated Japanese Lung Cancer Patients

PubMed Central

Kawakami, Takao; Nagasaka, Keiko; Takami, Sachiko; Wada, Kazuya; Tu, Hsiao-Kun; Otsuji, Makiko; Kyono, Yutaka; Dobashi, Tae; Komatsu, Yasuhiko; Kihara, Makoto; Akimoto, Shingo; Peers, Ian S.; South, Marie C.; Higenbottam, Tim; Fukuoka, Masahiro; Nakata, Koichiro; Ohe, Yuichiro; Kudoh, Shoji; Clausen, Ib Groth; Nishimura, Toshihide; Marko-Varga, György; Kato, Harubumi

2011-01-01

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10−25), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control. PMID:21799770

Insight and suicidality in psychosis: A cross-sectional study.

PubMed

Massons, Carmen; Lopez-Morinigo, Javier-David; Pousa, Esther; Ruiz, Ada; Ochoa, Susana; Usall, Judith; Nieto, Lourdes; Cobo, Jesus; David, Anthony S; Dutta, Rina

2017-06-01

We aimed to test whether specific insight dimensions are associated with suicidality in patients with psychotic disorders. 143 patients with schizophrenia spectrum disorders were recruited. Suicidality was assessed by item 8 of the Calgary Depression Scale for Schizophrenia (CDSS). Insight was measured by the Scale of Unawareness of Mental Disorder (SUMD) and the Markova and Berrios Insight Scale. Bivariate analyses and multivariable logistic regression models were conducted. Those subjects aware of having a mental illness and its social consequences had higher scores on suicidality than those with poor insight. Awareness of the need for treatment was not linked with suicidality. The Markova and Berrios Insight scale total score and two specific domains (awareness of "disturbed thinking and loss of control over the situation" and "having a vague feeling that something is wrong") were related to suicidality. However, no insight dimensions survived the multivariable regression model, which found depression and previous suicidal behaviour to predict suicidality. Suicidality in psychosis was linked with some insight dimensions: awareness of mental illness and awareness of social consequences, but not compliance. Depression and previous suicidal behaviour mediated the associations with insight; thus, predicting suicidality. Copyright © 2017. Published by Elsevier B.V.

A Course in... Multivariable Control Methods.

ERIC Educational Resources Information Center

Deshpande, Pradeep B.

1988-01-01

Describes an engineering course for graduate study in process control. Lists four major topics: interaction analysis, multiloop controller design, decoupling, and multivariable control strategies. Suggests a course outline and gives information about each topic. (MVL)

Assessing Multivariate Constraints to Evolution across Ten Long-Term Avian Studies

PubMed Central

Teplitsky, Celine; Tarka, Maja; Møller, Anders P.; Nakagawa, Shinichi; Balbontín, Javier; Burke, Terry A.; Doutrelant, Claire; Gregoire, Arnaud; Hansson, Bengt; Hasselquist, Dennis; Gustafsson, Lars; de Lope, Florentino; Marzal, Alfonso; Mills, James A.; Wheelwright, Nathaniel T.; Yarrall, John W.; Charmantier, Anne

2014-01-01

Background In a rapidly changing world, it is of fundamental importance to understand processes constraining or facilitating adaptation through microevolution. As different traits of an organism covary, genetic correlations are expected to affect evolutionary trajectories. However, only limited empirical data are available. Methodology/Principal Findings We investigate the extent to which multivariate constraints affect the rate of adaptation, focusing on four morphological traits often shown to harbour large amounts of genetic variance and considered to be subject to limited evolutionary constraints. Our data set includes unique long-term data for seven bird species and a total of 10 populations. We estimate population-specific matrices of genetic correlations and multivariate selection coefficients to predict evolutionary responses to selection. Using Bayesian methods that facilitate the propagation of errors in estimates, we compare (1) the rate of adaptation based on predicted response to selection when including genetic correlations with predictions from models where these genetic correlations were set to zero and (2) the multivariate evolvability in the direction of current selection to the average evolvability in random directions of the phenotypic space. We show that genetic correlations on average decrease the predicted rate of adaptation by 28%. Multivariate evolvability in the direction of current selection was systematically lower than average evolvability in random directions of space. These significant reductions in the rate of adaptation and reduced evolvability were due to a general nonalignment of selection and genetic variance, notably orthogonality of directional selection with the size axis along which most (60%) of the genetic variance is found. Conclusions These results suggest that genetic correlations can impose significant constraints on the evolution of avian morphology in wild populations. This could have important impacts on evolutionary dynamics and hence population persistence in the face of rapid environmental change. PMID:24608111

Multivariate functions for predicting the sorption of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils.

PubMed

Katseanes, Chelsea K; Chappell, Mark A; Hopkins, Bryan G; Durham, Brian D; Price, Cynthia L; Porter, Beth E; Miller, Lesley F

2016-11-01

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed a new solution for modeling the sorption and persistence of these munition constituents as multivariate mathematical functions correlating soil attribute data over a variety of taxonomically distinct soil types to contaminant behavior, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments measuring the sorption of TNT and RDX on taxonomically different soil types that were extensively physical and chemically characterized. Statistical decomposition of the log-transformed, and auto-scaled soil characterization data using the dimension-reduction technique PCA (principal component analysis) revealed a strong latent structure based in the multiple pairwise correlations among the soil properties. TNT and RDX sorption partitioning coefficients (KD-TNT and KD-RDX) were regressed against this latent structure using partial least squares regression (PLSR), generating a 3-factor, multivariate linear functions. Here, PLSR models predicted KD-TNT and KD-RDX values based on attributes contributing to endogenous alkaline/calcareous and soil fertility criteria, respectively, exhibited among the different soil types: We hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished soil types may provide the means for potentially predicting complex phenomena in soils. The development of predictive multivariate models tuned to a local soil's taxonomic designation would have direct benefit to military range managers seeking to anticipate the environmental risks of training activities on impact sites. Published by Elsevier Ltd.

Stability and Performance Robustness Assessment of Multivariable Control Systems

DTIC Science & Technology

1993-04-01

00- STABILITY AND PERFORMANCE ROBUSTNESS ASSESSMENT OF MULTIVARIABLE CONTROL SYSTEMS Asok Ray , Jenny I. Shen, and Chen-Kuo Weng Mechanical...Office of Naval Research Assessment of Multivariable Control Systems Grant No. N00014-90-J- 1513 6. AUTHOR(S) (Extension) Professor Asok Ray , Dr...20 The Pennsylvania State University University Park, PA 16802 (20 for Professor Asok Ray ) Naval Postgraduate School

Design of multivariable feedback control systems via spectral assignment. [as applied to aircraft flight control

NASA Technical Reports Server (NTRS)

Liberty, S. R.; Mielke, R. R.; Tung, L. J.

1981-01-01

Applied research in the area of spectral assignment in multivariable systems is reported. A frequency domain technique for determining the set of all stabilizing controllers for a single feedback loop multivariable system is described. It is shown that decoupling and tracking are achievable using this procedure. The technique is illustrated with a simple example.

An effective drift correction for dynamical downscaling of decadal global climate predictions

NASA Astrophysics Data System (ADS)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.

A Polymorphism within the Vitamin D Transporter Gene Predicts Outcome in Metastatic Colorectal Cancer Patients Treated with FOLFIRI/Bevacizumab or FOLFIRI/Cetuximab.

PubMed

Berger, Martin D; Stintzing, Sebastian; Heinemann, Volker; Cao, Shu; Yang, Dongyun; Sunakawa, Yu; Matsusaka, Satoshi; Ning, Yan; Okazaki, Satoshi; Miyamoto, Yuji; Suenaga, Mitsukuni; Schirripa, Marta; Hanna, Diana L; Soni, Shivani; Puccini, Alberto; Zhang, Wu; Cremolini, Chiara; Falcone, Alfredo; Loupakis, Fotios; Lenz, Heinz-Josef

2018-02-15

Purpose: Vitamin D exerts its inhibitory influence on colon cancer growth by inhibiting Wnt signaling and angiogenesis. We hypothesized that SNPs in genes involved in vitamin D transport, metabolism, and signaling are associated with outcome in metastatic colorectal cancer (mCRC) patients treated with first-line FOLFIRI and bevacizumab. Experimental Design: 522 mCRC patients enrolled in the FIRE-3 (discovery cohort) and TRIBE (validation set) trials treated with FOLFIRI/bevacizumab were included in this study. 278 patients receiving FOLFIRI and cetuximab (FIRE-3) served as a control cohort. Six SNPs in 6 genes ( GC, CYP24A1, CYP27B1, VDR, DKK1, CST5 ) were analyzed. Results: In the discovery cohort, AA carriers of the GC rs4588 SNP encoding for the vitamin D-binding protein, and treated with FOLFIRI/bevacizumab had a shorter overall survival (OS) than those harboring any C allele (15.9 vs. 25.1 months) in both univariable ( P = 0.001) and multivariable analyses ( P = 0.047). This association was confirmed in the validation cohort in multivariable analysis (OS 18.1 vs. 26.2 months, HR, 1.83; P = 0.037). Interestingly, AA carriers in the control set exhibited a longer OS (48.0 vs. 25.2 months, HR, 0.50; P = 0.021). This association was further confirmed in a second validation cohort comprising refractory mCRC patients treated with cetuximab ± irinotecan (PFS 8.7 vs. 3.7 months) in univariable ( P = 0.033) and multivariable analyses ( P = 0.046). Conclusions: GC rs4588 SNP might serve as a predictive marker in mCRC patients treated with FOLFIRI/bevacizumab or FOLFIRI/cetuximab. Whereas AA carriers derive a survival benefit with FOLFIRI/cetuximab, treatment with FOLFIRI/bevacizumab is associated with a worse outcome. Clin Cancer Res; 24(4); 784-93. ©2017 AACR . ©2017 American Association for Cancer Research.

Pretreatment Health Behaviors Predict Survival Among Patients With Head and Neck Squamous Cell Carcinoma

PubMed Central

Duffy, Sonia A.; Ronis, David L.; McLean, Scott; Fowler, Karen E.; Gruber, Stephen B.; Wolf, Gregory T.; Terrell, Jeffrey E.

2009-01-01

Purpose Our prior work has shown that the health behaviors of head and neck cancer patients are interrelated and are associated with quality of life; however, other than smoking, the relationship between health behaviors and survival is unclear. Patients and Methods A prospective cohort study was conducted to determine the relationship between five pretreatment health behaviors (smoking, alcohol, diet, physical activity, and sleep) and all-cause survival among 504 head and neck cancer patients. Results Smoking status was the strongest predictor of survival, with both current smokers (hazard ratio [HR] = 2.4; 95% CI, 1.3 to 4.4) and former smokers (HR = 2.0; 95% CI, 1.2 to 3.5) showing significant associations with poor survival. Problem drinking was associated with survival in the univariate analysis (HR = 1.4; 95% CI, 1.0 to 2.0) but lost significance when controlling for other factors. Low fruit intake was negatively associated with survival in the univariate analysis only (HR = 1.6; 95% CI, 1.1 to 2.1), whereas vegetable intake was not significant in either univariate or multivariate analyses. Although physical activity was associated with survival in the univariate analysis (HR = 0.95; 95% CI, 0.93 to 0.97), it was not significant in the multivariate model. Sleep was not significantly associated with survival in either univariate or multivariate analysis. Control variables that were also independently associated with survival in the multivariate analysis were age, education, tumor site, cancer stage, and surgical treatment. Conclusion Variation in selected pretreatment health behaviors (eg, smoking, fruit intake, and physical activity) in this population is associated with variation in survival. PMID:19289626

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1984-01-01

The objective of this investigation is to develop a state-of-the-art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies. A three-dimensional multivariate O/I analysis scheme has been developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

The development of a state of the art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies was investigated. A three dimensional multivariate O/I analysis scheme was developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Predictors of outcomes in patients with primary retroperitoneal dedifferentiated liposarcoma undergoing surgery.

PubMed

Keung, Emily Z; Hornick, Jason L; Bertagnolli, Monica M; Baldini, Elizabeth H; Raut, Chandrajit P

2014-02-01

Although sarcoma histology is recognized as a prognostic factor, most studies of retroperitoneal sarcomas report results combining multiple histologies and are inadequately powered to identify prognostic factors specific to a particular histology. We reviewed our experience with retroperitoneal dedifferentiated liposarcoma (RP DDLPS) to identify factors predictive of outcomes. All patients with RP DDLPS treated at our institution between 1998 and 2008 were reviewed. Multivariable Cox regression analyses were performed to identify factors predictive of progression-free survival (PFS), local recurrence-free survival (LRFS), distant recurrence-free survival (DRFS), and overall survival (OS). We identified 119 patients with primary DDLPS. Median tumor size was 20.5 cm; 21% were multifocal. French Federation of Cancer Centers Sarcoma Group tumor grades were intermediate in 53% of patients and high in 28% (unknown 19%). Resections were complete (R0/R1) in 80% of patients and incomplete (R2) in 11% (unknown 9%). Tumors were removed intact in 72% of patients and fragmented in 16% (unknown 12%). Median follow-up was 74.1 months. One hundred patients (84%) experienced recurrence or progression, with 92% occurring in the retroperitoneum. Median PFS, LRFS, DRFS, and OS were 21.1, 21.5, 45.8, and 59.0 months, respectively, and were significantly worse with R2 resection. On multivariate analysis, tumor integrity (intact vs fragmented) was predictive of PFS, multifocality predicted LRFS, and extent of resection (R0/R1 vs R2), grade, and tumor integrity predicted OS. In this cohort of primary RP DDLPS, factors under surgeon control (tumor integrity, extent of resection) and reflective of tumor biology (grade, multifocality) impact patient outcomes. Copyright © 2014 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

TGFbeta1 (Leu10Pro), p53 (Arg72Pro) can predict for increased risk for breast cancer in south Indian women and TGFbeta1 Pro (Leu10Pro) allele predicts response to neo-adjuvant chemo-radiotherapy.

PubMed

Rajkumar, Thangarajan; Samson, Mani; Rama, Ranganathan; Sridevi, Veluswami; Mahji, Urmila; Swaminathan, Rajaraman; Nancy, Nirmala K

2008-11-01

The breast cancer incidence has been increasing in the south Indian women. A case (n=250)-control (n=500) study was undertaken to investigate the role of Single Nucleotide Polymorphisms (SNP's) in GSTM1 (Present/Null); GSTP1 (Ile105Val), p53 (Arg72Pro), TGFbeta1 (Leu10Pro), c-erbB2 (Ile655Val), and GSTT1 (Null/Present) in breast cancer. In addition, the value of the SNP's in predicting primary tumor's pathologic response following neo-adjuvant chemo-radiotherapy was assessed. Genotyping was done using PCR (GSTM1, GSTT1), Taqman Allelic discrimination assay (GSTP1, c-erbB2) and PCR-CTPP (p53 and TGFbeta1). None of the gene SNP's studied were associated with a statistically significant increased risk for the breast cancer. However, combined analysis of the SNP's showed that p53 (Arg/Arg and Arg/Pro) with TGFbeta1 (Pro/Pro and Leu/Pro) were associated with greater than 2 fold increased risk for breast cancer in Univariate (P=0.01) and Multivariate (P=0.003) analysis. There was no statistically significant association for the GST family members with the breast cancer risk. TGFbeta1 (Pro/Pro) allele was found to predict complete pathologic response in the primary tumour following neo-adjuvant chemo-radiotherapy (OR=6.53 and 10.53 in Univariate and Multivariate analysis respectively) (P=0.004) and was independent of stage. This study suggests that SNP's can help predict breast cancer risk in south Indian women and that TGFbeta1 (Pro/Pro) allele is associated with a better pCR in the primary tumour.

Changes in beta cell function during the proximate post-diagnosis period in persons with type 1 diabetes.

PubMed

DiMeglio, Linda A; Cheng, Peiyao; Beck, Roy W; Kollman, Craig; Ruedy, Katrina J; Slover, Robert; Aye, Tandy; Weinzimer, Stuart A; Bremer, Andrew A; Buckingham, Bruce

2016-06-01

Prior studies examining beta-cell preservation in type 1 diabetes have predominantly assessed stimulated C-peptide concentrations approximately 10 wk after diagnosis. We examined whether earlier assessments might aid in prediction of beta cell function over time. Using data from a multi-center randomized trial assessing the effect of intensive diabetes management initiated within 1 wk of diagnosis, we assessed which clinical factors predicted 90-min mixed-meal tolerance test (MMTT) stimulated C-peptide values obtained 2 and 6 wk after diagnosis. We also studied associations of these factors with C-peptide values at 1- and 2-year post-diagnosis. Data from intervention and control groups were pooled. Among 67 study participants (mean age 13.3 ± 5.7 yr, range 7.8-45.7 yr) in multivariable analyses, C-peptide increased from baseline to 2 wks and then 6 wk. C-peptide levels at these times were significantly correlated with 1- and 2-yr C-peptide concentrations (all p < 0.001), with the strongest observed associations between 6-wk C-peptide and the 1- and 2-yr values (r = 0.66 and r = 0.61, respectively). In multivariable analyses, greater baseline and 6-wk C-peptide, and older age independently predicted greater 1- and 2-yr C-peptide concentrations. C-peptide assessments close to diagnosis were predictive of subsequent C-peptide production. Our data demonstrate a clear increase in C-peptide over the initial 6 wk after diabetes diagnosis followed by a plateau. Our data do not suggest that MMTT assessments performed closer to diagnosis than 6 wk would improve prediction of subsequent residual beta cell function. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Leptospirosis in American Samoa – Estimating and Mapping Risk Using Environmental Data

PubMed Central

Lau, Colleen L.; Clements, Archie C. A.; Skelly, Chris; Dobson, Annette J.; Smythe, Lee D.; Weinstein, Philip

2012-01-01

Background The recent emergence of leptospirosis has been linked to many environmental drivers of disease transmission. Accurate epidemiological data are lacking because of under-diagnosis, poor laboratory capacity, and inadequate surveillance. Predictive risk maps have been produced for many diseases to identify high-risk areas for infection and guide allocation of public health resources, and are particularly useful where disease surveillance is poor. To date, no predictive risk maps have been produced for leptospirosis. The objectives of this study were to estimate leptospirosis seroprevalence at geographic locations based on environmental factors, produce a predictive disease risk map for American Samoa, and assess the accuracy of the maps in predicting infection risk. Methodology and Principal Findings Data on seroprevalence and risk factors were obtained from a recent study of leptospirosis in American Samoa. Data on environmental variables were obtained from local sources, and included rainfall, altitude, vegetation, soil type, and location of backyard piggeries. Multivariable logistic regression was performed to investigate associations between seropositivity and risk factors. Using the multivariable models, seroprevalence at geographic locations was predicted based on environmental variables. Goodness of fit of models was measured using area under the curve of the receiver operating characteristic, and the percentage of cases correctly classified as seropositive. Environmental predictors of seroprevalence included living below median altitude of a village, in agricultural areas, on clay soil, and higher density of piggeries above the house. Models had acceptable goodness of fit, and correctly classified ∼84% of cases. Conclusions and Significance Environmental variables could be used to identify high-risk areas for leptospirosis. Environmental monitoring could potentially be a valuable strategy for leptospirosis control, and allow us to move from disease surveillance to environmental health hazard surveillance as a more cost-effective tool for directing public health interventions. PMID:22666516

Staphylococcal Enterotoxin P Predicts Bacteremia in Hospitalized Patients Colonized With Methicillin-Resistant Staphylococcus aureus

PubMed Central

Calderwood, Michael S.; Desjardins, Christopher A.; Sakoulas, George; Nicol, Robert; DuBois, Andrea; Delaney, Mary L.; Kleinman, Ken; Cosimi, Lisa A.; Feldgarden, Michael; Onderdonk, Andrew B.; Birren, Bruce W.; Platt, Richard; Huang, Susan S.

2014-01-01

Background. Methicillin-resistant Staphylococcus aureus (MRSA) colonization predicts later infection, with both host and pathogen determinants of invasive disease. Methods. This nested case-control study evaluates predictors of MRSA bacteremia in an 8–intensive care unit (ICU) prospective adult cohort from 1 September 2003 through 30 April 2005 with active MRSA surveillance and collection of ICU, post-ICU, and readmission MRSA isolates. We selected MRSA carriers who did (cases) and those who did not (controls) develop MRSA bacteremia. Generating assembled genome sequences, we evaluated 30 MRSA genes potentially associated with virulence and invasion. Using multivariable Cox proportional hazards regression, we assessed the association of these genes with MRSA bacteremia, controlling for host risk factors. Results. We collected 1578 MRSA isolates from 520 patients. We analyzed host and pathogen factors for 33 cases and 121 controls. Predictors of MRSA bacteremia included a diagnosis of cancer, presence of a central venous catheter, hyperglycemia (glucose level, >200 mg/dL), and infection with a MRSA strain carrying the gene for staphylococcal enterotoxin P (sep). Receipt of an anti-MRSA medication had a significant protective effect. Conclusions. In an analysis controlling for host factors, colonization with MRSA carrying sep increased the risk of MRSA bacteremia. Identification of risk-adjusted genetic determinants of virulence may help to improve prediction of invasive disease and suggest new targets for therapeutic intervention. PMID:24041793

Compassion Fatigue and Psychological Distress Among Social Workers: A Validation Study

PubMed Central

Adams, Richard E.; Boscarino, Joseph A.; Figley, Charles R.

2009-01-01

Few studies have focused on caring professionals and their emotional exhaustion from working with traumatized clients, referred to as compassion fatigue (CF). The present study had 2 goals: (a) to assess the psychometric properties of a CF scale, and (b) to examine the scale's predictive validity in a multivariate model. The data came from a survey of social workers living in New York City following the September 11, 2001, terrorist attacks on the World Trade Center. Factor analyses indicated that the CF scale measured multiple dimensions. After overlapping items were eliminated, the scale measured 2 key underlying dimensions—secondary trauma and job burnout. In a multivariate model, these dimensions were related to psychological distress, even after other risk factors were controlled. The authors discuss the results in light of increasing the ability of professional caregivers to meet the emotional needs of their clients within a stressful environment without experiencing CF. PMID:16569133

Pneumonia in multiple injured patients: a prospective controlled trial on early prediction using clinical and immunological parameters.

PubMed

Andermahr, J; Greb, A; Hensler, T; Helling, H J; Bouillon, B; Sauerland, S; Rehm, K E; Neugebauer, E

2002-05-01

In a prospective trial 266 multiple injured patients were included to evaluate clinical risk factors and immune parameters related to pneumonia. Clinical and humoral parameters were assessed and multivariate analysis performed. The multivariate analysis (odds ratio with 95% confidence interval (CI)) revealed male gender (3.65), traumatic brain injury (TBI) (2.52), thorax trauma (AIS(thorax) > or = 3) (2.05), antibiotic prophylaxis (1.30), injury severity score (ISS) (1.03 per ISS point) and the age (1.02 per year) as risk factors for pneumonia. The main pathogens were Acinetobacter Baumannii (40%) and Staphylococcus aureus (25%). A tendency towards higher Procalcitonin (PCT) and Interleukin (IL)-6 levels two days after trauma was observed for pneumonia patients. The immune parameters (PCT, IL-6, IL-10, soluble tumor necrosis factor p-55 and p-75) could not confirm the diagnosis of pneumonia earlier than the clinical parameters.

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes

PubMed Central

2014-01-01

Background Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. Methods The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations. Results Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on outcomes not directly considered by the studies included in the analysis. Conclusions Accounting for the dependency between outcomes in a multivariate meta-analysis may or may not improve the precision of effect estimates from a network meta-analysis compared to analysing each outcome separately. PMID:25047164

Evaluation of risk factors for perforated peptic ulcer.

PubMed

Yamamoto, Kazuki; Takahashi, Osamu; Arioka, Hiroko; Kobayashi, Daiki

2018-02-15

The aim of this study was to evaluate the prediction factors for perforated peptic ulcer (PPU). At St. Luke's International Hospital in Tokyo, Japan, a case control study was performed between August 2004 and March 2016. All patients diagnosed with PPU were included. As control subjects, patients with age, sex and date of CT scan corresponding to those of the PPU subjects were included in the study at a proportion of 2 controls for every PPU subject. All data such as past medical histories, physical findings, and laboratory data were collected through chart reviews. Univariate analyses and multivariate analyses with logistic regression were conducted, and receiver operating characteristic curves (ROCs) were calculated to show validity. Sensitivity analyses were performed to confirm results using a stepwise method and conditional logistic regression. A total of 408 patients were included in this study; 136 were a group of patients with PPU, and 272 were a control group. Univariate analysis showed statistical significance in many categories. Four different models of multivariate analyses were conducted, and significant differences were found for muscular defense and a history of peptic ulcer disease (PUD) in all models. The conditional forced-entry analysis of muscular defense showed an odds ratio (OR) of 23.8 (95% confidence interval [CI]: 5.70-100.0), and the analysis of PUD history showed an OR of 6.40 (95% CI: 1.13-36.2). The sensitivity analysis showed consistent results, with an OR of 23.8-366.2 for muscular defense and an OR of 3.67-7.81 for PUD history. The area under the curve (AUC) of all models was high enough to confirm the results. However, anticoagulants, known risk factors for PUD, did not increase the risk for PPU in our study. The conditional forced-entry analysis of anticoagulant use showed an OR of 0.85 (95% CI: 0.03-22.3). The evaluation of prediction factors and development of a prediction rule for PPU may help our decision making in performing a CT scan for patients with acute abdominal pain.

Novel immunological and nutritional-based prognostic index for gastric cancer.

PubMed

Sun, Kai-Yu; Xu, Jian-Bo; Chen, Shu-Ling; Yuan, Yu-Jie; Wu, Hui; Peng, Jian-Jun; Chen, Chuang-Qi; Guo, Pi; Hao, Yuan-Tao; He, Yu-Long

2015-05-21

To assess the prognostic significance of immunological and nutritional-based indices, including the prognostic nutritional index (PNI), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio in gastric cancer. We retrospectively reviewed 632 gastric cancer patients who underwent gastrectomy between 1998 and 2008. Areas under the receiver operating characteristic curve were calculated to compare the predictive ability of the indices, together with estimating the sensitivity, specificity and agreement rate. Univariate and multivariate analyses were performed to identify risk factors for overall survival (OS). Propensity score analysis was performed to adjust variables to control for selection bias. Each index could predict OS in gastric cancer patients in univariate analysis, but only PNI had independent prognostic significance in multivariate analysis before and after adjustment with propensity scoring (hazard ratio, 1.668; 95% confidence interval: 1.368-2.035). In subgroup analysis, a low PNI predicted a significantly shorter OS in patients with stage II-III disease (P = 0.019, P < 0.001), T3-T4 tumors (P < 0.001), or lymph node metastasis (P < 0.001). Canton score, a combination of PNI, NLR, and platelet, was a better indicator for OS than PNI, with the largest area under the curve for 12-, 36-, 60-mo OS and overall OS (P = 0.022, P = 0.030, P < 0.001, and P = 0.024, respectively). The maximum sensitivity, specificity, and agreement rate of Canton score for predicting prognosis were 84.6%, 34.9%, and 70.1%, respectively. PNI is an independent prognostic factor for OS in gastric cancer. Canton score can be a novel preoperative prognostic index in gastric cancer.

A predictive risk model for medical intractability in epilepsy.

PubMed

Huang, Lisu; Li, Shi; He, Dake; Bao, Weiqun; Li, Ling

2014-08-01

This study aimed to investigate early predictors (6 months after diagnosis) of medical intractability in epilepsy. All children <12 years of age having two or more unprovoked seizures 24 h apart at Xinhua Hospital between 1992 and 2006 were included. Medical intractability was defined as failure, due to lack of seizure control, of more than 2 antiepileptic drugs at maximum tolerated doses, with an average of more than 1 seizure per month for 24 months and no more than 3 consecutive months of seizure freedom during this interval. Univariate and multivariate logistic regression models were performed to determine the risk factors for developing medical intractability. Receiver operating characteristic curve was applied to fit the best compounded predictive model. A total of 649 patients were identified, out of which 119 (18%) met the study definition of intractable epilepsy at 2 years after diagnosis, and the rate of intractable epilepsy in patients with idiopathic syndromes was 12%. Multivariate logistic regression analysis revealed that neurodevelopmental delay, symptomatic etiology, partial seizures, and more than 10 seizures before diagnosis were significant and independent risk factors for intractable epilepsy. The best model to predict medical intractability in epilepsy comprised neurological physical abnormality, age at onset of epilepsy under 1 year, more than 10 seizures before diagnosis, and partial epilepsy, and the area under receiver operating characteristic curve was 0.7797. This model also fitted best in patients with idiopathic syndromes. A predictive model of medically intractable epilepsy composed of only four characteristics is established. This model is comparatively accurate and simple to apply clinically. Copyright © 2014 Elsevier Inc. All rights reserved.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Accuracies of univariate and multivariate genomic prediction models in African cassava.

PubMed

Okeke, Uche Godfrey; Akdemir, Deniz; Rabbi, Ismail; Kulakow, Peter; Jannink, Jean-Luc

2017-12-04

Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a single-environment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2). For these analyses, we used 16 years of public cassava breeding data for six target cassava traits and a fivefold cross-validation scheme with 10-repeat cycles to assess model prediction accuracies. In Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, we observed that the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model. We recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

Application of Fluorescence Spectrometry With Multivariate Calibration to the Enantiomeric Recognition of Fluoxetine in Pharmaceutical Preparations.

PubMed

Poláček, Roman; Májek, Pavel; Hroboňová, Katarína; Sádecká, Jana

2016-04-01

Fluoxetine is the most prescribed antidepressant chiral drug worldwide. Its enantiomers have a different duration of serotonin inhibition. A novel simple and rapid method for determination of the enantiomeric composition of fluoxetine in pharmaceutical pills is presented. Specifically, emission, excitation, and synchronous fluorescence techniques were employed to obtain the spectral data, which with multivariate calibration methods, namely, principal component regression (PCR) and partial least square (PLS), were investigated. The chiral recognition of fluoxetine enantiomers in the presence of β-cyclodextrin was based on diastereomeric complexes. The results of the multivariate calibration modeling indicated good prediction abilities. The obtained results for tablets were compared with those from chiral HPLC and no significant differences are shown by Fisher's (F) test and Student's t-test. The smallest residuals between reference or nominal values and predicted values were achieved by multivariate calibration of synchronous fluorescence spectral data. This conclusion is supported by calculated values of the figure of merit.

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

DSouza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz; Wismüller, Axel

2017-02-01

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Socio-Demographic and Clinical Characteristics are Not Clinically Useful Predictors of Refill Adherence in Patients with Hypertension

PubMed Central

Steiner, John F.; Ho, P. Michael; Beaty, Brenda L.; Dickinson, L. Miriam; Hanratty, Rebecca; Zeng, Chan; Tavel, Heather M.; Havranek, Edward P.; Davidson, Arthur J.; Magid, David J.; Estacio, Raymond O.

2009-01-01

Background Although many studies have identified patient characteristics or chronic diseases associated with medication adherence, the clinical utility of such predictors has rarely been assessed. We attempted to develop clinical prediction rules for adherence with antihypertensive medications in two health care delivery systems. Methods and Results Retrospective cohort studies of hypertension registries in an inner-city health care delivery system (N = 17176) and a health maintenance organization (N = 94297) in Denver, Colorado. Adherence was defined by acquisition of 80% or more of antihypertensive medications. A multivariable model in the inner-city system found that adherent patients (36.3% of the total) were more likely than non-adherent patients to be older, white, married, and acculturated in US society, to have diabetes or cerebrovascular disease, not to abuse alcohol or controlled substances, and to be prescribed less than three antihypertensive medications. Although statistically significant, all multivariate odds ratios were 1.7 or less, and the model did not accurately discriminate adherent from non-adherent patients (C-statistic = 0.606). In the health maintenance organization, where 72.1% of patients were adherent, significant but weak associations existed between adherence and older age, white race, the lack of alcohol abuse, and fewer antihypertensive medications. The multivariate model again failed to accurately discriminate adherent from non-adherent individuals (C-statistic = 0.576). Conclusions Although certain socio-demographic characteristics or clinical diagnoses are statistically associated with adherence to refills of antihypertensive medications, a combination of these characteristics is not sufficiently accurate to allow clinicians to predict whether their patients will be adherent with treatment. PMID:20031876

Sarcopenia predicts 1-year mortality in elderly patients undergoing curative gastrectomy for gastric cancer: a prospective study.

PubMed

Huang, Dong-Dong; Chen, Xiao-Xi; Chen, Xi-Yi; Wang, Su-Lin; Shen, Xian; Chen, Xiao-Lei; Yu, Zhen; Zhuang, Cheng-Le

2016-11-01

One-year mortality is vital for elderly oncologic patients undergoing surgery. Recent studies have demonstrated that sarcopenia can predict outcomes after major abdominal surgeries, but the association of sarcopenia and 1-year mortality has never been investigated in a prospective study. We conducted a prospective study of elderly patients (≥65 years) who underwent curative gastrectomy for gastric cancer from July 2014 to July 2015. Sarcopenia was determined by the measurements of muscle mass, handgrip strength, and gait speed. Univariate and multivariate analyses were used to identify the risk factors associated with 1-year mortality. A total of 173 patients were included, in which 52 (30.1 %) patients were identified as having sarcopenia. Twenty-four (13.9 %) patients died within 1 year of surgery. Multivariate analysis showed that sarcopenia was an independent risk factor for 1-year mortality. Area under the receiver operating characteristic curve demonstrated an increased predictive power for 1-year mortality with the inclusion of sarcopenia, from 0.835 to 0.868. Solely low muscle mass was not predictive of 1-year mortality in the multivariate analysis. Sarcopenia is predictive of 1-year mortality in elderly patients undergoing gastric cancer surgery. The measurement of muscle function is important for sarcopenia as a preoperative assessment tool.

Functionality of empirical model-based predictive analytics for the early detection of hemodynamic instabilty.

PubMed

Summers, Richard L; Pipke, Matt; Wegerich, Stephan; Conkright, Gary; Isom, Kristen C

2014-01-01

Background. Monitoring cardiovascular hemodynamics in the modern clinical setting is a major challenge. Increasing amounts of physiologic data must be analyzed and interpreted in the context of the individual patient’s pathology and inherent biologic variability. Certain data-driven analytical methods are currently being explored for smart monitoring of data streams from patients as a first tier automated detection system for clinical deterioration. As a prelude to human clinical trials, an empirical multivariate machine learning method called Similarity-Based Modeling (“SBM”), was tested in an In Silico experiment using data generated with the aid of a detailed computer simulator of human physiology (Quantitative Circulatory Physiology or “QCP”) which contains complex control systems with realistic integrated feedback loops. Methods. SBM is a kernel-based, multivariate machine learning method that that uses monitored clinical information to generate an empirical model of a patient’s physiologic state. This platform allows for the use of predictive analytic techniques to identify early changes in a patient’s condition that are indicative of a state of deterioration or instability. The integrity of the technique was tested through an In Silico experiment using QCP in which the output of computer simulations of a slowly evolving cardiac tamponade resulted in progressive state of cardiovascular decompensation. Simulator outputs for the variables under consideration were generated at a 2-min data rate (0.083Hz) with the tamponade introduced at a point 420 minutes into the simulation sequence. The functionality of the SBM predictive analytics methodology to identify clinical deterioration was compared to the thresholds used by conventional monitoring methods. Results. The SBM modeling method was found to closely track the normal physiologic variation as simulated by QCP. With the slow development of the tamponade, the SBM model are seen to disagree while the simulated biosignals in the early stages of physiologic deterioration and while the variables are still within normal ranges. Thus, the SBM system was found to identify pathophysiologic conditions in a timeframe that would not have been detected in a usual clinical monitoring scenario. Conclusion. In this study the functionality of a multivariate machine learning predictive methodology that that incorporates commonly monitored clinical information was tested using a computer model of human physiology. SBM and predictive analytics were able to differentiate a state of decompensation while the monitored variables were still within normal clinical ranges. This finding suggests that the SBM could provide for early identification of a clinical deterioration using predictive analytic techniques. predictive analytics, hemodynamic, monitoring.

Centralized PI control for high dimensional multivariable systems based on equivalent transfer function.

PubMed

Luan, Xiaoli; Chen, Qiang; Liu, Fei

2014-09-01

This article presents a new scheme to design full matrix controller for high dimensional multivariable processes based on equivalent transfer function (ETF). Differing from existing ETF method, the proposed ETF is derived directly by exploiting the relationship between the equivalent closed-loop transfer function and the inverse of open-loop transfer function. Based on the obtained ETF, the full matrix controller is designed utilizing the existing PI tuning rules. The new proposed ETF model can more accurately represent the original processes. Furthermore, the full matrix centralized controller design method proposed in this paper is applicable to high dimensional multivariable systems with satisfactory performance. Comparison with other multivariable controllers shows that the designed ETF based controller is superior with respect to design-complexity and obtained performance. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Higher prevalence of wheezing and lower FEV1 and FVC percent predicted in adults with sickle cell anaemia: A cross-sectional study.

PubMed

Musa, Baba M; Galadanci, Najibah A; Rodeghier, Mark; Debaun, Michael R

2017-02-01

Respiratory symptoms including wheezing are common in adults with sickle cell anaemia (SCA), even in the absence of asthma. However, the prevalence of spirometry changes and respiratory symptoms in adults with SCA is unknown. Using a cross-sectional study design, we tested the hypothesis that adults with SCA (cases) would have higher rates of lower airway obstruction and wheezing than those without SCA (controls) using the American Thoracic Society Division of Lung Diseases' questionnaire. Patients were adults with SCA aged between 18 and 65 years. Controls were consecutive unselected individuals without SCA who presented to an outpatient general medicine clinic. We enrolled 150 adults with SCA and 287 consecutive controls without SCA. The median age was 23.0 and 27.0 years for adults with and without SCA, respectively. Cases were more likely to report cough without a cold (35.0% vs 18.6%, P < 0.001), lower forced expiratory volume in 1 s (FEV 1 ) % predicted (70.1% vs 82.1%, P = 0.001) and lower forced vital capacity (FVC) % predicted (67.4% vs 74.9%, P = 0.001) than controls. In the multivariable model, wheezing was significantly associated with SCA status (OR = 1.69, 95% CI = 1.08-2.65, P = 0.024). Similarly, FEV 1 % predicted was significantly associated with SCA status and wheezing (P = 0.001 for both). Adults with SCA experience a higher rate of wheezing and impaired respiratory functions compared with controls from the same region. © 2016 Asian Pacific Society of Respirology.

Correlations between Preoperative Angle Parameters and Postoperative Unpredicted Refractive Errors after Cataract Surgery in Open Angle Glaucoma (AOD 500).

PubMed

Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun; Seong, Gong Je

2017-03-01

To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R²=0.404). Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors.

Correlations between Preoperative Angle Parameters and Postoperative Unpredicted Refractive Errors after Cataract Surgery in Open Angle Glaucoma (AOD 500)

PubMed Central

Lee, Wonseok; Bae, Hyoung Won; Lee, Si Hyung; Kim, Chan Yun

2017-01-01

Purpose To assess the accuracy of intraocular lens (IOL) power prediction for cataract surgery with open angle glaucoma (OAG) and to identify preoperative angle parameters correlated with postoperative unpredicted refractive errors. Materials and Methods This study comprised 45 eyes from 45 OAG subjects and 63 eyes from 63 non-glaucomatous cataract subjects (controls). We investigated differences in preoperative predicted refractive errors and postoperative refractive errors for each group. Preoperative predicted refractive errors were obtained by biometry (IOL-master) and compared to postoperative refractive errors measured by auto-refractometer 2 months postoperatively. Anterior angle parameters were determined using swept source optical coherence tomography. We investigated correlations between preoperative angle parameters [angle open distance (AOD); trabecular iris surface area (TISA); angle recess area (ARA); trabecular iris angle (TIA)] and postoperative unpredicted refractive errors. Results In patients with OAG, significant differences were noted between preoperative predicted and postoperative real refractive errors, with more myopia than predicted. No significant differences were recorded in controls. Angle parameters (AOD, ARA, TISA, and TIA) at the superior and inferior quadrant were significantly correlated with differences between predicted and postoperative refractive errors in OAG patients (-0.321 to -0.408, p<0.05). Superior quadrant AOD 500 was significantly correlated with postoperative refractive differences in multivariate linear regression analysis (β=-2.925, R2=0.404). Conclusion Clinically unpredicted refractive errors after cataract surgery were more common in OAG than in controls. Certain preoperative angle parameters, especially AOD 500 at the superior quadrant, were significantly correlated with these unpredicted errors. PMID:28120576

Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment

NASA Astrophysics Data System (ADS)

Faes, Luca; Marinazzo, Daniele; Stramaglia, Sebastiano; Jurysta, Fabrice; Porta, Alberto; Giandomenico, Nollo

2016-05-01

This work introduces a framework to study the network formed by the autonomic component of heart rate variability (cardiac process η) and the amplitude of the different electroencephalographic waves (brain processes δ, θ, α, σ, β) during sleep. The framework exploits multivariate linear models to decompose the predictability of any given target process into measures of self-, causal and interaction predictability reflecting respectively the information retained in the process and related to its physiological complexity, the information transferred from the other source processes, and the information modified during the transfer according to redundant or synergistic interaction between the sources. The framework is here applied to the η, δ, θ, α, σ, β time series measured from the sleep recordings of eight severe sleep apnoea-hypopnoea syndrome (SAHS) patients studied before and after long-term treatment with continuous positive airway pressure (CPAP) therapy, and 14 healthy controls. Results show that the full and self-predictability of η, δ and θ decreased significantly in SAHS compared with controls, and were restored with CPAP for δ and θ but not for η. The causal predictability of η and δ occurred through significantly redundant source interaction during healthy sleep, which was lost in SAHS and recovered after CPAP. These results indicate that predictability analysis is a viable tool to assess the modifications of complexity and causality of the cerebral and cardiac processes induced by sleep disorders, and to monitor the restoration of the neuroautonomic control of these processes during long-term treatment.

Salting-out assisted liquid-liquid extraction and partial least squares regression to assay low molecular weight polycyclic aromatic hydrocarbons leached from soils and sediments

NASA Astrophysics Data System (ADS)

Bressan, Lucas P.; do Nascimento, Paulo Cícero; Schmidt, Marcella E. P.; Faccin, Henrique; de Machado, Leandro Carvalho; Bohrer, Denise

2017-02-01

A novel method was developed to determine low molecular weight polycyclic aromatic hydrocarbons in aqueous leachates from soils and sediments using a salting-out assisted liquid-liquid extraction, synchronous fluorescence spectrometry and a multivariate calibration technique. Several experimental parameters were controlled and the optimum conditions were: sodium carbonate as the salting-out agent at concentration of 2 mol L- 1, 3 mL of acetonitrile as extraction solvent, 6 mL of aqueous leachate, vortexing for 5 min and centrifuging at 4000 rpm for 5 min. The partial least squares calibration was optimized to the lowest values of root mean squared error and five latent variables were chosen for each of the targeted compounds. The regression coefficients for the true versus predicted concentrations were higher than 0.99. Figures of merit for the multivariate method were calculated, namely sensitivity, multivariate detection limit and multivariate quantification limit. The selectivity was also evaluated and other polycyclic aromatic hydrocarbons did not interfere in the analysis. Likewise, high performance liquid chromatography was used as a comparative methodology, and the regression analysis between the methods showed no statistical difference (t-test). The proposed methodology was applied to soils and sediments of a Brazilian river and the recoveries ranged from 74.3% to 105.8%. Overall, the proposed methodology was suitable for the targeted compounds, showing that the extraction method can be applied to spectrofluorometric analysis and that the multivariate calibration is also suitable for these compounds in leachates from real samples.

Negative Events in Childhood Predict Trajectories of Internalizing Symptoms Up to Young Adulthood: An 18-Year Longitudinal Study

PubMed Central

Melchior, Maria; Touchette, Évelyne; Prokofyeva, Elena; Chollet, Aude; Fombonne, Eric; Elidemir, Gulizar; Galéra, Cédric

2014-01-01

Background Common negative events can precipitate the onset of internalizing symptoms. We studied whether their occurrence in childhood is associated with mental health trajectories over the course of development. Methods Using data from the TEMPO study, a French community-based cohort study of youths, we studied the association between negative events in 1991 (when participants were aged 4–16 years) and internalizing symptoms, assessed by the ASEBA family of instruments in 1991, 1999, and 2009 (n = 1503). Participants' trajectories of internalizing symptoms were estimated with semi-parametric regression methods (PROC TRAJ). Data were analyzed using multinomial regression models controlled for participants' sex, age, parental family status, socio-economic position, and parental history of depression. Results Negative childhood events were associated with an increased likelihood of concurrent internalizing symptoms which sometimes persisted into adulthood (multivariate ORs associated with > = 3 negative events respectively: high and decreasing internalizing symptoms: 5.54, 95% CI: 3.20–9.58; persistently high internalizing symptoms: 8.94, 95% CI: 2.82–28.31). Specific negative events most strongly associated with youths' persistent internalizing symptoms included: school difficulties (multivariate OR: 5.31, 95% CI: 2.24–12.59), parental stress (multivariate OR: 4.69, 95% CI: 2.02–10.87), serious illness/health problems (multivariate OR: 4.13, 95% CI: 1.76–9.70), and social isolation (multivariate OR: 2.24, 95% CI: 1.00–5.08). Conclusions Common negative events can contribute to the onset of children's lasting psychological difficulties. PMID:25485875

Combining microwave resonance technology to multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation.

PubMed

Lourenço, Vera; Herdling, Thorsten; Reich, Gabriele; Menezes, José C; Lochmann, Dirk

2011-08-01

A set of 192 fluid bed granulation batches at industrial scale were in-line monitored using microwave resonance technology (MRT) to determine moisture, temperature and density of the granules. Multivariate data analysis techniques such as multiway partial least squares (PLS), multiway principal component analysis (PCA) and multivariate batch control charts were applied onto collected batch data sets. The combination of all these techniques, along with off-line particle size measurements, led to significantly increased process understanding. A seasonality effect could be put into evidence that impacted further processing through its influence on the final granule size. Moreover, it was demonstrated by means of a PLS that a relation between the particle size and the MRT measurements can be quantitatively defined, highlighting a potential ability of the MRT sensor to predict information about the final granule size. This study has contributed to improve a fluid bed granulation process, and the process knowledge obtained shows that the product quality can be built in process design, following Quality by Design (QbD) and Process Analytical Technology (PAT) principles. Copyright © 2011. Published by Elsevier B.V.

Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging.

PubMed

Falahati, Farshad; Westman, Eric; Simmons, Andrew

2014-01-01

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

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

2013-01-01

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

Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.

PubMed

Sun, Jin; Rutkoski, Jessica E; Poland, Jesse A; Crossa, José; Jannink, Jean-Luc; Sorrells, Mark E

2017-07-01

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment. Copyright © 2017 Crop Science Society of America.

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo.

PubMed

Sharif, K M; Rahman, M M; Azmir, J; Khatib, A; Sabina, E; Shamsudin, S H; Zaidul, I S M

2015-12-01

Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed. Copyright © 2015 John Wiley & Sons, Ltd.

Non-fragile multivariable PID controller design via system augmentation

NASA Astrophysics Data System (ADS)

Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan

2017-07-01

In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.

Do state characteristics matter? State level factors related to tobacco cessation quitlines

PubMed Central

Keller, Paula A; Koss, Kalsea J; Baker, Timothy B; Bailey, Linda A; Fiore, Michael C

2007-01-01

Background Quitline services are an effective population‐wide tobacco cessation strategy adopted widely in the United States as part of state comprehensive tobacco control efforts. Despite widespread evidence supporting quitlines' effectiveness, many states lack sufficient financial resources to adequately fund and promote this service. Efforts to augment state tobacco control efforts might be fostered by greater knowledge of state level factors associated with the funding and implementation of those efforts. Methods We analysed data from the 2004 North American Quitline Consortium survey and from publicly available sources to identify state level factors related to quitline implementation and funding. Factors included in the analyses were state demographic characteristics, tobacco use variables, state tobacco control spending, and economic and political climate variables. Univariate and multivariate regression analyses were conducted. Results The best fitting multivariate model that significantly predicted the presence or absence of a state quitline included only cigarette excise tax rate (p = 0.020). In terms of funding levels, states with high rates of cigarette consumption (p = 0.047) and with higher per capita expenditures for tobacco control programmes (p = 0 .0.004) were most likely to spend more on per capita operations budget for quitlines. Conclusion State level factors appear to play a part in whether states had established quitlines by mid‐2004 and the amount of per capita quitline funding. PMID:18048637

Early warnings for suicide attempt among Chinese rural population.

PubMed

Lyu, Juncheng; Wang, Yingying; Shi, Hong; Zhang, Jie

2018-06-05

This study was to explore the main influencing factors of attempted suicide and establish an early warning model, so as to put forward prevention strategies for attempted suicide. Data came from a large-scale case-control epidemiological survey. A sample of 659 serious suicide attempters was randomly recruited from 13 rural counties in China. Each case was matched by a community control for gender, age, and residence location. Face to face interviews were conducted for all the cases and controls with the same structured questionnaire. Univariate logistic regression was applied to screen the factors and multivariate logistic regression was used to excavate the predictors. There were no statistical differences between suicide attempters and the community controls in gender, age, and residence location. The Cronbach`s coefficients for all the scales used were above 0.675. The multivariate logistic regressions have revealed 12 statistically significant variables predicting attempted suicide, including less education, family history of suicide, poor health, mental problem, aspiration strain, hopelessness, impulsivity, depression, negative life events. On the other hand, social support, coping skills, and healthy community protected the rural residents from suicide attempt. The excavated warning predictors are significant clinical meaning for the clinical psychiatrist. Crisis intervention strategies in rural China should be informed by the findings from this research. Education, social support, healthy community, and strain reduction are all measures to decrease the likelihood of crises. Copyright © 2018. Published by Elsevier B.V.

Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors.

PubMed

Vuong, Kylie; Armstrong, Bruce K; Weiderpass, Elisabete; Lund, Eiliv; Adami, Hans-Olov; Veierod, Marit B; Barrett, Jennifer H; Davies, John R; Bishop, D Timothy; Whiteman, David C; Olsen, Catherine M; Hopper, John L; Mann, Graham J; Cust, Anne E; McGeechan, Kevin

2016-08-01

Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies. To develop and externally validate a risk prediction model for incident first-primary cutaneous melanoma using self-assessed risk factors. We used unconditional logistic regression to develop a multivariable risk prediction model. Relative risk estimates from the model were combined with Australian melanoma incidence and competing mortality rates to obtain absolute risk estimates. A risk prediction model was developed using the Australian Melanoma Family Study (629 cases and 535 controls) and externally validated using 4 independent population-based studies: the Western Australia Melanoma Study (511 case-control pairs), Leeds Melanoma Case-Control Study (960 cases and 513 controls), Epigene-QSkin Study (44 544, of which 766 with melanoma), and Swedish Women's Lifestyle and Health Cohort Study (49 259 women, of which 273 had melanoma). We validated model performance internally and externally by assessing discrimination using the area under the receiver operating curve (AUC). Additionally, using the Swedish Women's Lifestyle and Health Cohort Study, we assessed model calibration and clinical usefulness. The risk prediction model included hair color, nevus density, first-degree family history of melanoma, previous nonmelanoma skin cancer, and lifetime sunbed use. On internal validation, the AUC was 0.70 (95% CI, 0.67-0.73). On external validation, the AUC was 0.66 (95% CI, 0.63-0.69) in the Western Australia Melanoma Study, 0.67 (95% CI, 0.65-0.70) in the Leeds Melanoma Case-Control Study, 0.64 (95% CI, 0.62-0.66) in the Epigene-QSkin Study, and 0.63 (95% CI, 0.60-0.67) in the Swedish Women's Lifestyle and Health Cohort Study. Model calibration showed close agreement between predicted and observed numbers of incident melanomas across all deciles of predicted risk. In the external validation setting, there was higher net benefit when using the risk prediction model to classify individuals as high risk compared with classifying all individuals as high risk. The melanoma risk prediction model performs well and may be useful in prevention interventions reliant on a risk assessment using self-assessed risk factors.

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

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

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela; ...

2017-07-01

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Modifiable risk factors of ecstasy use: risk perception, current dependence, perceived control, and depression

PubMed Central

Leung, Kit Sang; Ben Abdallah, Arbi; Cottler, Linda B.

2009-01-01

Risk perception, perceived behavioral control of obtaining ecstasy (PBC-obtaining), current ecstasy dependence, and recent depression have been associated with past ecstasy use, however, their utility in predicting ecstasy use has not been demonstrated. This study aimed to determine whether these four modifiable risk factors could predict ecstasy use after controlling for socio-demographic covariates and recent polydrug use. Data from 601 ecstasy users in the National Institute on Drug Abuse funded TriCity Study of Club Drug Use, Abuse and Dependence were analyzed using multivariate logistic regression. Participants were interviewed twice within a 2-week period using standardized instruments. Thirteen percent (n=80) of the participants reported using ecstasy between the two interviews. Low risk perception, high PBC-obtaining (an estimated ecstasy procurement time < 24 hours), and current ecstasy dependence were statistically associated with ecstasy use between the two interviews. Recent depression was not a significant predictor. Despite not being a target predictor, recent polydrug use was also statistically associated with ecstasy use. The present findings may inform the development of interventions targeting ecstasy users. PMID:19880258

Measurement of pH in whole blood by near-infrared spectroscopy

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

Alam, M. Kathleen; Maynard, John D.; Robinson, M. Ries

1999-03-01

Whole blood pH has been determined {ital in vitro} by using near-infrared spectroscopy over the wavelength range of 1500 to 1785 nm with multivariate calibration modeling of the spectral data obtained from two different sample sets. In the first sample set, the pH of whole blood was varied without controlling cell size and oxygen saturation (O{sub 2} Sat) variation. The result was that the red blood cell (RBC) size and O{sub 2} Sat correlated with pH. Although the partial least-squares (PLS) multivariate calibration of these data produced a good pH prediction cross-validation standard error of prediction (CVSEP)=0.046, R{sup 2}=0.982, themore » spectral data were dominated by scattering changes due to changing RBC size that correlated with the pH changes. A second experiment was carried out where the RBC size and O{sub 2} Sat were varied orthogonally to the pH variation. A PLS calibration of the spectral data obtained from these samples produced a pH prediction with an R{sup 2} of 0.954 and a cross-validated standard error of prediction of 0.064 pH units. The robustness of the PLS calibration models was tested by predicting the data obtained from the other sets. The predicted pH values obtained from both data sets yielded R{sup 2} values greater than 0.9 once the data were corrected for differences in hemoglobin concentration. For example, with the use of the calibration produced from the second sample set, the pH values from the first sample set were predicted with an R{sup 2} of 0.92 after the predictions were corrected for bias and slope. It is shown that spectral information specific to pH-induced chemical changes in the hemoglobin molecule is contained within the PLS loading vectors developed for both the first and second data sets. It is this pH specific information that allows the spectra dominated by pH-correlated scattering changes to provide robust pH predictive ability in the uncorrelated data, and visa versa. {copyright} {ital 1999} {ital Society for Applied Spectroscopy}« less

A Multivariate Model of Parent-Adolescent Relationship Variables in Early Adolescence

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2011-01-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle…

Real-time quality assurance testing using photonic techniques: Application to iodine water system

NASA Technical Reports Server (NTRS)

Arendale, W. F.; Hatcher, Richard; Garlington, Yadilett; Harwell, Jack; Everett, Tracey

1990-01-01

A feasibility study of the use of inspection systems incorporating photonic sensors and multivariate analyses to provide an instrumentation system that in real-time assures quality and that the system in control has been conducted. A system is in control when the near future of the product quality is predictable. Off-line chemical analyses can be used for a chemical process when slow kinetics allows time to take a sample to the laboratory and the system provides a recovery mechanism that returns the system to statistical control without intervention of the operator. The objective for this study has been the implementation of do-it-right-the-first-time and just-in-time philosophies. The Environment Control and Life Support Systems (ECLSS) water reclamation system that adds iodine for biocidal control is an ideal candidate for the study and implementation of do-it-right-the-first-time technologies.

Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study.

PubMed

Drewry, Anne M; Fuller, Brian M; Bailey, Thomas C; Hotchkiss, Richard S

2013-09-12

Early treatment of sepsis improves survival, but early diagnosis of hospital-acquired sepsis, especially in critically ill patients, is challenging. Evidence suggests that subtle changes in body temperature patterns may be an early indicator of sepsis, but data is limited. The aim of this study was to examine whether abnormal body temperature patterns, as identified by visual examination, could predict the subsequent diagnosis of sepsis in afebrile critically ill patients. Retrospective case-control study of 32 septic and 29 non-septic patients in an adult medical and surgical ICU. Temperature curves for the period starting 72 hours and ending 8 hours prior to the clinical suspicion of sepsis (for septic patients) and for the 72-hour period prior to discharge from the ICU (for non-septic patients) were rated as normal or abnormal by seven blinded physicians. Multivariable logistic regression was used to compare groups in regard to maximum temperature, minimum temperature, greatest change in temperature in any 24-hour period, and whether the majority of evaluators rated the curve to be abnormal. Baseline characteristics of the groups were similar except the septic group had more trauma patients (31.3% vs. 6.9%, p = .02) and more patients requiring mechanical ventilation (75.0% vs. 41.4%, p = .008). Multivariable logistic regression to control for baseline differences demonstrated that septic patients had significantly larger temperature deviations in any 24-hour period compared to control patients (1.5°C vs. 1.1°C, p = .02). An abnormal temperature pattern was noted by a majority of the evaluators in 22 (68.8%) septic patients and 7 (24.1%) control patients (adjusted OR 4.43, p = .017). This resulted in a sensitivity of 0.69 (95% CI [confidence interval] 0.50, 0.83) and specificity of 0.76 (95% CI 0.56, 0.89) of abnormal temperature curves to predict sepsis. The median time from the temperature plot to the first culture was 9.40 hours (IQR [inter-quartile range] 8.00, 18.20) and to the first dose of antibiotics was 16.90 hours (IQR 8.35, 34.20). Abnormal body temperature curves were predictive of the diagnosis of sepsis in afebrile critically ill patients. Analysis of temperature patterns, rather than absolute values, may facilitate decreased time to antimicrobial therapy.

Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study

PubMed Central

2013-01-01

Introduction Early treatment of sepsis improves survival, but early diagnosis of hospital-acquired sepsis, especially in critically ill patients, is challenging. Evidence suggests that subtle changes in body temperature patterns may be an early indicator of sepsis, but data is limited. The aim of this study was to examine whether abnormal body temperature patterns, as identified by visual examination, could predict the subsequent diagnosis of sepsis in afebrile critically ill patients. Methods Retrospective case-control study of 32 septic and 29 non-septic patients in an adult medical and surgical ICU. Temperature curves for the period starting 72 hours and ending 8 hours prior to the clinical suspicion of sepsis (for septic patients) and for the 72-hour period prior to discharge from the ICU (for non-septic patients) were rated as normal or abnormal by seven blinded physicians. Multivariable logistic regression was used to compare groups in regard to maximum temperature, minimum temperature, greatest change in temperature in any 24-hour period, and whether the majority of evaluators rated the curve to be abnormal. Results Baseline characteristics of the groups were similar except the septic group had more trauma patients (31.3% vs. 6.9%, p = .02) and more patients requiring mechanical ventilation (75.0% vs. 41.4%, p = .008). Multivariable logistic regression to control for baseline differences demonstrated that septic patients had significantly larger temperature deviations in any 24-hour period compared to control patients (1.5°C vs. 1.1°C, p = .02). An abnormal temperature pattern was noted by a majority of the evaluators in 22 (68.8%) septic patients and 7 (24.1%) control patients (adjusted OR 4.43, p = .017). This resulted in a sensitivity of 0.69 (95% CI [confidence interval] 0.50, 0.83) and specificity of 0.76 (95% CI 0.56, 0.89) of abnormal temperature curves to predict sepsis. The median time from the temperature plot to the first culture was 9.40 hours (IQR [inter-quartile range] 8.00, 18.20) and to the first dose of antibiotics was 16.90 hours (IQR 8.35, 34.20). Conclusions Abnormal body temperature curves were predictive of the diagnosis of sepsis in afebrile critically ill patients. Analysis of temperature patterns, rather than absolute values, may facilitate decreased time to antimicrobial therapy. PMID:24028682

A Database Approach for Predicting and Monitoring Baked Anode Properties

NASA Astrophysics Data System (ADS)

Lauzon-Gauthier, Julien; Duchesne, Carl; Tessier, Jayson

2012-11-01

The baked anode quality control strategy currently used by most carbon plants based on testing anode core samples in the laboratory is inadequate for facing increased raw material variability. The low core sampling rate limited by lab capacity and the common practice of reporting averaged properties based on some anode population mask a significant amount of individual anode variability. In addition, lab results are typically available a few weeks after production and the anodes are often already set in the reduction cells preventing early remedial actions when necessary. A database approach is proposed in this work to develop a soft-sensor for predicting individual baked anode properties at the end of baking cycle. A large historical database including raw material properties, process operating parameters and anode core data was collected from a modern Alcoa plant. A multivariate latent variable PLS regression method was used for analyzing the large database and building the soft-sensor model. It is shown that the general low frequency trends in most anode physical and mechanical properties driven by raw material changes are very well captured by the model. Improvements in the data infrastructure (instrumentation, sampling frequency and location) will be necessary for predicting higher frequency variations in individual baked anode properties. This paper also demonstrates how multivariate latent variable models can be interpreted against process knowledge and used for real-time process monitoring of carbon plants, and detection of faults and abnormal operation.

ASSOCIATIONS BETWEEN TRAUMATIC EVENTS AND SUICIDAL BEHAVIOUR IN SOUTH AFRICA

PubMed Central

Sorsdahl, Katherine; Stein, Dan J.; Williams, David R.; Nock, Matthew K.

2011-01-01

Research conducted predominantly in the developed world suggests that there is an association between trauma exposure and suicidal behaviour. However, there are limited data available investigating whether specific traumas are uniquely predictive of suicidal behaviour, or the extent to which traumatic events predict the progression from suicide ideation to plans and attempts. A national survey was conducted with 4351 adult South Africans between 2002 and 2004 as part of the WHO World Mental Health Surveys. Data on trauma exposure and subsequent suicidal behaviour were collected. Bivariate and multivariate survival models tested the relationship between the type and number of traumatic events and lifetime suicidal behaviour. A range of traumatic events are associated with lifetime suicide ideation and attempt; however, after controlling for all traumatic events in a multivariate model, only sexual violence (OR=4.7, CI 2.3-9.4) and having witnessed violence (OR=1.8, 1.1-2.9) remained significant predictors of life-time suicide attempts. Disaggregation of the associations between traumatic events and suicide attempts indicates that they are largely due to traumatic events predicting suicide ideation rather than to the progression from suicide ideation to attempt. This paper highlights the importance of traumatic life events in the occurrence of suicidal thoughts and behaviours and provides important information about the nature of this association. Future research is needed to better understand how and why such experiences increase the risk of suicidal outcomes. PMID:22134450

Cytokine activation is predictive of mortality in Zambian patients with AIDS-related diarrhoea.

PubMed

Zulu, Isaac; Hassan, Ghaniah; Njobvu R N, Lungowe; Dhaliwal, Winnie; Sianongo, Sandie; Kelly, Paul

2008-11-13

Mortality in Zambian AIDS patients is high, especially in patients with diarrhoea, and there is still unacceptably high mortality in Zambian patients just starting anti-retroviral therapy. We set out to determine if high concentrations of serum cytokines correlate with mortality. Serum samples from 30 healthy controls (HIV seropositive and seronegative) and 50 patients with diarrhoea (20 of whom died within 6 weeks) were analysed. Concentrations of tumour necrosis factor receptor p55 (TNFR p55), macrophage migration inhibitory factor (MIF), interleukin (IL)-6, IL-12, interferon (IFN)-gamma and C-reactive protein (CRP) were measured by ELISA, and correlated with mortality after 6 weeks follow-up. Apart from IL-12, concentrations of all cytokines, TNFR p55 and CRP increased with worsening severity of disease, showing highly statistically significant trends. In a multivariable analysis high TNFR p55, IFN-gamma, CRP and low CD4 count (CD4 count <100) were predictive of mortality. Although nutritional status (assessed by body mass index, BMI) was predictive in univariate analysis, it was not an independent predictor in multivariate analysis. High serum concentrations of TNFR p55, IFN-gamma, CRP and low CD4 count correlated with disease severity and short-term mortality in HIV-infected Zambian adults with diarrhoea. These factors were better predictors of survival than BMI. Understanding the cause of TNFR p55, IFN-gamma and CRP elevation may be useful in development of interventions to reduce mortality in AIDS patients with chronic diarrhoea in Africa.

Interaction between Helicobacter pylori and latent toxoplasmosis and demographic variables on cognitive function in young to middle-aged adults.

PubMed

Gale, Shawn D; Erickson, Lance D; Brown, Bruce L; Hedges, Dawson W

2015-01-01

Helicobacter pylori and latent toxoplasmosis are widespread diseases that have been associated with cognitive deficits and Alzheimer's disease. We sought to determine whether interactions between Helicobacter pylori and latent toxoplasmosis, age, race-ethnicity, educational attainment, economic status, and general health predict cognitive function in young and middle-aged adults. To do so, we used multivariable regression and multivariate models to analyze data obtained from the United States' National Health and Nutrition Examination Survey from the Centers for Disease Control and Prevention, which can be weighted to represent the US population. In this sample, we found that 31.6 percent of women and 36.2 percent of men of the overall sample had IgG Antibodies against Helicobacter pylori, although the seroprevalence of Helicobacter pylori varied with sociodemographic variables. There were no main effects for Helicobacter pylori or latent toxoplasmosis for any of the cognitive measures in models adjusting for age, sex, race-ethnicity, educational attainment, economic standing, and self-rated health predicting cognitive function. However, interactions between Helicobacter pylori and race-ethnicity, educational attainment, latent toxoplasmosis in the fully adjusted models predicted cognitive function. People seropositive for both Helicobacter pylori and latent toxoplasmosis - both of which appear to be common in the general population - appear to be more susceptible to cognitive deficits than are people seropositive for either Helicobacter pylori and or latent toxoplasmosis alone, suggesting a synergistic effect between these two infectious diseases on cognition in young to middle-aged adults.

Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers.

PubMed

Nmor, Jephtha C; Sunahara, Toshihiko; Goto, Kensuke; Futami, Kyoko; Sonye, George; Akweywa, Peter; Dida, Gabriel; Minakawa, Noboru

2013-01-16

Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae. Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC). All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The predictability of habitat types varied. Drains, foot-prints, puddles and swamp habitat types were most predictable. Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.

Lumbar Muscle Cross-Sectional Areas Do Not Predict Clinical Outcomes in Adults With Spinal Stenosis: A Longitudinal Study.

PubMed

Gellhorn, Alfred C; Suri, Pradeep; Rundell, Sean D; Olafsen, Nathan; Carlson, M Jake; Johnson, Steve; Fry, Adrielle; Annaswamy, Thiru M; Gilligan, Christopher; Comstock, Bryan; Heagerty, Patrick; Friedly, Janna; Jarvik, Jeffrey G

2017-06-01

Minimal longitudinal data exist regarding the role of lumbar musculature in predicting back pain and function. In cross-sectional study designs, there is often atrophy of the segmental multifidus muscle in subjects with low back pain compared with matched controls. However, the cross-sectional design of these studies prevents drawing conclusions regarding whether lumbar muscle characteristics predict or modify future back pain or function. The primary objective of this study is to determine whether the cross-sectional area (CSA) of lumbar muscles predict functional status or back pain at 6- or 12-month follow-up in older adults with spinal degeneration. The secondary objective is to evaluate whether these muscle characteristics improve outcome prediction above and beyond the prognostic information conferred by demographic and psychosocial variables. Secondary analysis of a randomized controlled trial. A total of 209 adults aged 50 years and older with clinical and radiographic spinal stenosis from the Lumbar Epidural steroid injection for Spinal Stenosis (LESS) trial. Using baseline magnetic resonance images, we calculated CSAs of the lumbar multifidus, psoas, and quadratus lumborum muscles using a standardized protocol by manually tracing the borders of each of the muscles. The relationship between lumbar muscle CSAs and baseline measures was assessed with Pearson or Spearman correlation coefficients. The relationship between lumbar muscle characteristics and 6- and 12-month Roland Morris Disability Questionnaire (RDQ) and back pain Numeric Rating Scale (NRS) responses was further evaluated with multivariate linear regression. A hierarchical approach to the regression was performed: a basic model with factors of conceptual importance including age, gender, BMI, and baseline RDQ score formed the first step. The second and third steps evaluated whether psychosocial variables or muscle measures conferred additional prognostic information to the basic model. Function as measured by the RDQ and back pain as measured by the NRS at 6- and 12-month follow-up. Lumbar muscle CSA was not a significant predictor of 6- or 12-month RDQ or pain score in multivariate analyses. Cross-sectional areas of lumbar muscles do not predict function or pain at medium- and long-term follow-up in adults with lumbar spinal stenosis. III. Copyright © 2017 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer's Disease Tests: A Multivariate and Machine Learning Study.

PubMed

Tunvirachaisakul, Chavit; Supasitthumrong, Thitiporn; Tangwongchai, Sookjareon; Hemrunroj, Solaphat; Chuchuen, Phenphichcha; Tawankanjanachot, Itthipol; Likitchareon, Yuthachai; Phanthumchinda, Kamman; Sriswasdi, Sira; Maes, Michael

2018-04-04

The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer's dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI. © 2018 S. Karger AG, Basel.

Response Surface Modeling Using Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2001-01-01

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data

PubMed Central

Batal, Iyad; Cooper, Gregory; Fradkin, Dmitriy; Harrison, James; Moerchen, Fabian; Hauskrecht, Milos

2015-01-01

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present Recent Temporal Pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the Minimal Predictive Recent Temporal Patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems. PMID:26752800

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

Russo, Andrea L.; Adams, Judith A.; Weyman, Elizabeth A.

Purpose: Squamous cell carcinoma (SCC) is the most common sinonasal cancer and is associated with one of the poor outcomes. Proton therapy allows excellent target coverage with maximal sparing of adjacent normal tissues. We evaluated the long-term outcomes in patients with sinonasal SCC treated with proton therapy. Methods and Materials: Between 1991 and 2008, 54 patients with Stage III and IV SCC of the nasal cavity and paranasal sinus received proton beam therapy at our institution to a median dose of 72.8 Gy(RBE). Sixty-nine percent underwent prior surgical resection, and 74% received elective nodal radiation. Locoregional control and survival probabilities weremore » estimated with the Kaplan-Meier method. Multivariate analyses were performed using the Cox proportional-hazards model. Treatment toxicity was scored using the Common Terminology Criteria for Adverse Events version 4.0. Results: With a median follow-up time of 82 months in surviving patients, there were 10 local, 7 regional, and 11 distant failures. The 2-year and 5-year actuarial local control rate was 80%. The 2-year and 5-year rates of overall survival were 67% and 47%, respectively. Only smoking status was predictive for worse locoregional control, with current smokers having a 5-year rate of 23% compared with 83% for noncurrent smokers (P=.004). Karnofsky performance status ≤80 was the most significant factor predictive for worse overall survival in multivariate analysis (adjusted hazard ratio 4.5, 95% confidence interval 1.6-12.5, P=.004). There were nine grade 3 and six grade 4 toxicities, and no grade 5 toxicity. Wound adverse events constituted the most common grade 3-4 toxicity. Conclusions: Our long-term results show that proton radiation therapy is well tolerated and yields good locoregional control for SCC of the nasal cavity and paranasal sinus. Current smokers and patients with poor performance status had inferior outcomes. Prospective study is necessary to compare IMRT with proton therapy in the treatment of sinonasal malignancy.« less

Risk factors and predictors for candidemia in pediatric intensive care unit patients: implications for prevention.

PubMed

Zaoutis, Theoklis E; Prasad, Priya A; Localio, A Russell; Coffin, Susan E; Bell, Louis M; Walsh, Thomas J; Gross, Robert

2010-09-01

Candida species are the leading cause of invasive fungal infections in hospitalized children and are the third most common isolates recovered from patients with healthcare-associated bloodstream infection in the United States. Few data exist on risk factors for candidemia in pediatric intensive care unit (PICU) patients. We conducted a population-based case-control study of PICU patients at Children's Hospital of Philadelphia during the period from 1997 through 2004. Case patients were identified using laboratory records, and control patients were selected from PICU rosters. Control patients were matched to case patients by incidence density sampling, adjusting for time at risk. Following conditional multivariate analysis, we performed weighted multivariate analysis to determine predicted probabilities for candidemia given certain risk factor combinations. We identified 101 case patients with candidemia (incidence, 3.5 cases per 1000 PICU admissions). Factors independently associated with candidemia included presence of a central venous catheter (odds ratio [OR], 30.4; 95% confidence interval [CI], 7.7-119.5), malignancy (OR, 4.0; 95% CI, 1.23-13.1), use of vancomycin for >3 days in the prior 2 weeks (OR, 6.2; 95% CI, 2.4-16), and receipt of agents with activity against anaerobic organisms for >3 days in the prior 2 weeks (OR, 3.5; 95% CI, 1.5-8.4). Predicted probability of having various combinations of the aforementioned factors ranged from 10.7% to 46%. The 30-day mortality rate was 44% among case patients and 14% among control patients (OR, 4.22; 95% CI, 2.35-7.60). To our knowledge, this is the first study to evaluate independent risk factors and to determine a population of children in PICUs at high risk for developing candidemia. Future efforts should focus on validation of these risk factors identified in a different PICU population and development of interventions for prevention of candidemia in critically ill children.

Does investor-ownership of nursing homes compromise the quality of care?

PubMed

Harrington, Charlene; Woolhandler, Steffie; Mullan, Joseph; Carrillo, Helen; Himmelstein, David U

2002-01-01

Quality problems have long plagued the nursing home industry. While two-thirds of U.S. nursing homes are investor-owned, few studies have examined the impact of investor-ownership on the quality of care. The authors analyzed 1998 data from inspections of 13,693 nursing facilities representing virtually all U.S. nursing homes. They grouped deficiency citations issued by inspectors into three categories ("quality of care," "quality of life," and "other") and compared deficiency rates in investor-owned, nonprofit, and public nursing homes. A multivariate model was used to control for case mix, percentage of residents covered by Medicaid, whether the facility was hospital-based, whether it was a skilled nursing facility for Medicare only, chain ownership, and location by state. The study also assessed nurse staffing. The authors found that investor-owned nursing homes provide worse care and less nursing care than nonprofit or public homes. Investor-owned facilities averaged 5.89 deficiencies per home, 46.5 percent higher than nonprofit and 43.0 percent higher than public facilities, and also had more of each category of deficiency. In the multivariate analysis, investor-ownership predicted 0.679 additional deficiencies per home; chain-ownership predicted an additional 0.633 deficiencies per home. Nurse staffing ratios were markedly lower at investor-owned homes.

EEG-based emergency braking intention prediction for brain-controlled driving considering one electrode falling-off.

PubMed

Huikang Wang; Luzheng Bi; Teng Teng

2017-07-01

This paper proposes a novel method of electroencephalography (EEG)-based driver emergency braking intention detection system for brain-controlled driving considering one electrode falling-off. First, whether one electrode falls off is discriminated based on EEG potentials. Then, the missing signals are estimated by using the signals collected from other channels based on multivariate linear regression. Finally, a linear decoder is applied to classify driver intentions. Experimental results show that the falling-off discrimination accuracy is 99.63% on average and the correlation coefficient and root mean squared error (RMSE) between the estimated and experimental data are 0.90 and 11.43 μV, respectively, on average. Given one electrode falls off, the system accuracy of the proposed intention prediction method is significantly higher than that of the original method (95.12% VS 79.11%) and is close to that (95.95%) of the original system under normal situations (i. e., no electrode falling-off).

Unbuckling the Bible Belt: A State-Level Analysis of Religious Factors and Google Searches for Porn.

PubMed

Whitehead, Andrew L; Perry, Samuel L

2018-01-01

While the link between individual religious characteristics and pornography consumption is well established, relatively little research has considered how the wider religious context may influence pornography use. Exceptions in the literature to date have relied on relatively broad, subjective measures of religious commitment, largely ignoring issues of religious belonging, belief, or practice. This study moves the conversation forward by examining how a variety of state-level religious factors predict Google searches for the term porn, net of relevant sociodemog raphic and ideological controls. Our multivariate findings indicate that higher percentages of Evangelical Protestants, theists, and biblical literalists in a state predict higher frequencies of searching for porn, as do higher church attendance rates. Conversely, higher percentages of religiously unaffiliated persons in a state predict lower frequencies of searching for porn. Higher percentages of total religious adherents, Catholics, or mainline Protestants in a state are unrelated to searching for porn with controls in place. Contrary to recent research, our analyses also show that higher percentages of political conservatives in a state predicted lower frequencies of porn searches. Our findings support theories that more salient, traditional religious influences in a state may influence residents-whether religious or not-toward more covert sexual experiences.

Novel adipokines WISP1 and betatrophin in PCOS: relationship to AMH levels, atherogenic and metabolic profile.

PubMed

Sahin Ersoy, Gulcin; Altun Ensari, Tugba; Vatansever, Dogan; Emirdar, Volkan; Cevik, Ozge

2017-02-01

To determine the levels of WISP1 and betatrophin in normal weight and obese women with polycystic ovary syndrome (PCOS) and to assess their relationship with anti-Müllerian hormone (AMH) levels, atherogenic profile and metabolic parameters Methods: In this prospective cross-sectional study, the study group was composed of 49 normal weighed and 34 obese women with PCOS diagnosed based on the Rotterdam criteria; 36 normal weight and 26 obese age matched non-hyperandrogenemic women with regular menstrual cycle. Serum WISP1, betatrophin, homeostasis model assessment of insulin resistance (HOMA-IR) and AMH levels were evaluated. Univariate and multivariate analyses were performed between betatrophin, WISP1 levels and AMH levels, metabolic and atherogenic parameters. Serum WISP1 and betatrophin values were elevated in the PCOS group than in the control group. Moreover, serum WISP1 and betatrophin levels were higher in the obese PCOS subgroup than in normal weight and obese control subgroups. Multivariate analyses revealed that Body mass index, HOMA-IR, AMH independently and positively predicted WISP1 levels. Serum betatrophin level variability was explained by homocysteine, HOMA-IR and androstenedione levels. WISP1 and betatrophin may play a key role on the pathogenesis of PCOS.

The Impact of Tic Severity, Comorbidity and Peer Attachment on Quality of Life Outcomes and Functioning in Tourette's Syndrome: Parental Perspectives.

PubMed

O'Hare, Deirdre; Helmes, Edward; Eapen, Valsamma; Grove, Rachel; McBain, Kerry; Reece, John

2016-08-01

The aim of this controlled, community-based study based on data from parents of youth (aged 7-16 years) with Tourette's syndrome (TS; n = 86) and parents of age and gender matched peers (n = 108) was to test several hypotheses involving a range of variables salient to the TS population, including peer attachment, quality of life, severity of tics, comorbidity, and psychological, behavioural and social dysfunction. Multivariate between-group analyses confirmed that TS group youth experienced lower quality of life, increased emotional, behavioural and social difficulties, and elevated rates of insecure peer attachment relative to controls, as reported by their primary caregiver. Results also confirmed the main hypothesis that security of peer attachment would be associated with individual variability in outcomes for youth with TS. As predicted, multivariate within-TS group analyses determined strong relationships among adverse quality of life outcomes and insecure attachment to peers, increased tic severity, and the presence of comorbid disorder. Findings suggest that youth with TS are at increased risk for insecure peer attachment and that this might be an important variable impacting the quality of life outcomes for those diagnosed.

Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

PubMed

Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

2017-07-01

To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

Multivariate statistical assessment of predictors of firefighters' muscular and aerobic work capacity.

PubMed

Lindberg, Ann-Sofie; Oksa, Juha; Antti, Henrik; Malm, Christer

2015-01-01

Physical capacity has previously been deemed important for firefighters physical work capacity, and aerobic fitness, muscular strength, and muscular endurance are the most frequently investigated parameters of importance. Traditionally, bivariate and multivariate linear regression statistics have been used to study relationships between physical capacities and work capacities among firefighters. An alternative way to handle datasets consisting of numerous correlated variables is to use multivariate projection analyses, such as Orthogonal Projection to Latent Structures. The first aim of the present study was to evaluate the prediction and predictive power of field and laboratory tests, respectively, on firefighters' physical work capacity on selected work tasks. Also, to study if valid predictions could be achieved without anthropometric data. The second aim was to externally validate selected models. The third aim was to validate selected models on firefighters' and on civilians'. A total of 38 (26 men and 12 women) + 90 (38 men and 52 women) subjects were included in the models and the external validation, respectively. The best prediction (R2) and predictive power (Q2) of Stairs, Pulling, Demolition, Terrain, and Rescue work capacities included field tests (R2 = 0.73 to 0.84, Q2 = 0.68 to 0.82). The best external validation was for Stairs work capacity (R2 = 0.80) and worst for Demolition work capacity (R2 = 0.40). In conclusion, field and laboratory tests could equally well predict physical work capacities for firefighting work tasks, and models excluding anthropometric data were valid. The predictive power was satisfactory for all included work tasks except Demolition.

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

PubMed

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

2015-01-01

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

The Impact of Adolescent Deviance on Marital Trajectories.

PubMed

Doherty, Elaine Eggleston; Green, Kerry M; Ensminger, Margaret E

2012-01-01

Marriage is a key life event that has numerous benefits. Recent research extends these benefits to include desistance from crime and drug use yet there has been little investigation regarding whether deviant behavior in adolescence impacts long-term marital patterns. Since rates of marriage are low among African Americans and rates of adolescent deviance and crime are high, we investigate the long-term relationship between the two drawing on longitudinal data from the Woodlawn cohort of urban African Americans. This article investigates whether serious adolescent delinquency and marijuana use predict marital trajectories, controlling for known correlates. Multivariate findings indicate that within this African-American population, deviance predicts the probability of marriage, stability of marriage, and timing of marriage for men yet deviance relates solely to the probability of marriage for women.

The Impact of Adolescent Deviance on Marital Trajectories

PubMed Central

Doherty, Elaine Eggleston; Green, Kerry M.; Ensminger, Margaret E.

2014-01-01

Marriage is a key life event that has numerous benefits. Recent research extends these benefits to include desistance from crime and drug use yet there has been little investigation regarding whether deviant behavior in adolescence impacts long-term marital patterns. Since rates of marriage are low among African Americans and rates of adolescent deviance and crime are high, we investigate the long-term relationship between the two drawing on longitudinal data from the Woodlawn cohort of urban African Americans. This article investigates whether serious adolescent delinquency and marijuana use predict marital trajectories, controlling for known correlates. Multivariate findings indicate that within this African-American population, deviance predicts the probability of marriage, stability of marriage, and timing of marriage for men yet deviance relates solely to the probability of marriage for women. PMID:25284919

Effects of Body Mass Index on Lung Function Index of Chinese Population

NASA Astrophysics Data System (ADS)

Guo, Qiao; Ye, Jun; Yang, Jian; Zhu, Changan; Sheng, Lei; Zhang, Yongliang

2018-01-01

To study the effect of body mass index (BMI) on lung function indexes in Chinese population. A cross-sectional study was performed on 10, 592 participants. The linear relationship between lung function and BMI was evaluated by multivariate linear regression analysis, and the correlation between BMI and lung function was assessed by Pearson correlation analysis. Correlation analysis showed that BMI was positively related with the decreasing of forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and FEV1/FVC (P <0.05), the increasing of FVC% predicted value (FVC%pre) and FEV1% predicted value (FEV1%pre). These suggested that Chinese people can restrain the decline of lung function to prevent the occurrence and development of COPD by the control of BMI.

Risk factors for low receptive vocabulary abilities in the preschool and early school years in the longitudinal study of Australian children.

PubMed

Christensen, Daniel; Zubrick, Stephen R; Lawrence, David; Mitrou, Francis; Taylor, Catherine L

2014-01-01

Receptive vocabulary development is a component of the human language system that emerges in the first year of life and is characterised by onward expansion throughout life. Beginning in infancy, children's receptive vocabulary knowledge builds the foundation for oral language and reading skills. The foundations for success at school are built early, hence the public health policy focus on reducing developmental inequalities before children start formal school. The underlying assumption is that children's development is stable, and therefore predictable, over time. This study investigated this assumption in relation to children's receptive vocabulary ability. We investigated the extent to which low receptive vocabulary ability at 4 years was associated with low receptive vocabulary ability at 8 years, and the predictive utility of a multivariate model that included child, maternal and family risk factors measured at 4 years. The study sample comprised 3,847 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Multivariate logistic regression was used to investigate risks for low receptive vocabulary ability from 4-8 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. In the multivariate model, substantial risk factors for receptive vocabulary delay from 4-8 years, in order of descending magnitude, were low receptive vocabulary ability at 4 years, low maternal education, and low school readiness. Moderate risk factors, in order of descending magnitude, were low maternal parenting consistency, socio-economic area disadvantage, low temperamental persistence, and NESB status. The following risk factors were not significant: One or more siblings, low family income, not reading to the child, high maternal work hours, and Aboriginal or Torres Strait Islander ethnicity. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude does not do particularly well in predicting low receptive vocabulary ability from 4-8 years.

Locating the Seventh Cervical Spinous Process: Development and Validation of a Multivariate Model Using Palpation and Personal Information.

PubMed

Ferreira, Ana Paula A; Póvoa, Luciana C; Zanier, José F C; Ferreira, Arthur S

2017-02-01

The aim of this study was to develop and validate a multivariate prediction model, guided by palpation and personal information, for locating the seventh cervical spinous process (C7SP). A single-blinded, cross-sectional study at a primary to tertiary health care center was conducted for model development and temporal validation. One-hundred sixty participants were prospectively included for model development (n = 80) and time-split validation stages (n = 80). The C7SP was located using the thorax-rib static method (TRSM). Participants underwent chest radiography for assessment of the inner body structure located with TRSM and using radio-opaque markers placed over the skin. Age, sex, height, body mass, body mass index, and vertex-marker distance (D V-M ) were used to predict the distance from the C7SP to the vertex (D V-C7 ). Multivariate linear regression modeling, limits of agreement plot, histogram of residues, receiver operating characteristic curves, and confusion tables were analyzed. The multivariate linear prediction model for D V-C7 (in centimeters) was D V-C7 = 0.986D V-M + 0.018(mass) + 0.014(age) - 1.008. Receiver operating characteristic curves had better discrimination of D V-C7 (area under the curve = 0.661; 95% confidence interval = 0.541-0.782; P = .015) than D V-M (area under the curve = 0.480; 95% confidence interval = 0.345-0.614; P = .761), with respective cutoff points at 23.40 cm (sensitivity = 41%, specificity = 63%) and 24.75 cm (sensitivity = 69%, specificity = 52%). The C7SP was correctly located more often when using predicted D V-C7 in the validation sample than when using the TRSM in the development sample: n = 53 (66%) vs n = 32 (40%), P < .001. Better accuracy was obtained when locating the C7SP by use of a multivariate model that incorporates palpation and personal information. Copyright © 2016. Published by Elsevier Inc.

Predictive value of preoperative inflammatory response biomarkers for metabolic syndrome and post-PCNL systemic inflammatory response syndrome in patients with nephrolithiasis.

PubMed

Tang, Kun; Liu, Haoran; Jiang, Kehua; Ye, Tao; Yan, Libin; Liu, Peijun; Xia, Ding; Chen, Zhiqiang; Xu, Hua; Ye, Zhangqun

2017-10-17

Neutrophil to lymphocyte ratio (NLR), derived neutrophil to lymphocyte ratio (dNLR), platelet to lymphocyte ratio (PLR) and lymphocyte to monocyte ratio (LMR) were promising biomarkers used to predict diagnosis and prognosis in various inflammatory responses diseases and cancers. However, there have been no reports regarding these biomarkers in kidney stone patients. This study aimed to evaluate the predictive value of inflammatory biomarkers for metabolic syndrome (MetS) and post-PCNL SIRS in nephrolithiasis patients. We retrospectively enrolled 513 patients with nephrolithiasis and 204 healthy controls. NLR, dNLR, LMR and PLR in nephrolithiasis patients were significantly higher than control group. Patients with renal stone have higher NLR, dNLR, LMR and PLR than those without. ROC curve analysis indicated NLR, dNLR, LMR and PLR for predicting patients with nephrolithiasis and MetS, displayed AUC of 0.730, 0.717, 0.627 and 0.606. Additionally, ROC curves, using post-PCNL SIRS as the end-point for NLR, dNLR, LMR and PLR with AUC of 0.831, 0.813, 0.723 and 0.685. Multivariate analysis revealed that NLR, dNLR represented independent factors for predicting post-PCNL SIRS. While LMR independently associated with MetS. These resluts demonstrate preoperative NLR, dNLR and LMR appears to be effective predictors of post-PCNL SIRS and LMR of MetS in nephrolithiasis patients.

Evaluation of plasma proteomic data for Alzheimer disease state classification and for the prediction of progression from mild cognitive impairment to Alzheimer disease.

PubMed

Llano, Daniel A; Devanarayan, Viswanath; Simon, Adam J

2013-01-01

Previous studies that have examined the potential for plasma markers to serve as biomarkers for Alzheimer disease (AD) have studied single analytes and focused on the amyloid-β and τ isoforms and have failed to yield conclusive results. In this study, we performed a multivariate analysis of 146 plasma analytes (the Human DiscoveryMAP v 1.0 from Rules-Based Medicine) in 527 subjects with AD, mild cognitive impairment (MCI), or cognitively normal elderly subjects from the Alzheimer's Disease Neuroimaging Initiative database. We identified 4 different proteomic signatures, each using 5 to 14 analytes, that differentiate AD from control patients with sensitivity and specificity ranging from 74% to 85%. Five analytes were common to all 4 signatures: apolipoprotein A-II, apolipoprotein E, serum glutamic oxaloacetic transaminase, α-1-microglobulin, and brain natriuretic peptide. None of the signatures adequately predicted progression from MCI to AD over a 12- and 24-month period. A new panel of analytes, optimized to predict MCI to AD conversion, was able to provide 55% to 60% predictive accuracy. These data suggest that a simple panel of plasma analytes may provide an adjunctive tool to differentiate AD from controls, may provide mechanistic insights to the etiology of AD, but cannot adequately predict MCI to AD conversion.

The value of FATS expression in predicting sensitivity to radiotherapy in breast cancer

PubMed Central

Zhang, Tiemei; Sun, Tao; Su, Yi; Zhao, Jing; Mu, Kun; Jin, Zhao; Gao, Ming; Liu, Juntian; Gu, Lin

2017-01-01

Purpose The fragile-site associated tumor suppressor (FATS) is a newly identified tumor suppressor involved in radiation-induced tumorigenesis. The purpose of this study was to characterize FATS expression in breast cancers about radiotherapy benefit, patient characteristics, and prognosis. Results The expression of FATS mRNA was silent or downregulated in 95.2% of breast cancer samples compared with paired normal controls (P < .0001). Negative status of FATS was correlated with higher nuclear grade (P = .01) and shorter disease-free survival (DFS) of breast cancer (P = .036). In a multivariate analysis, FATS expression showed favorable prognostic value for DFS (odds ratio, 0.532; 95% confidence interval, 0.299 to 0.947; (P = .032). Furthermore, improved survival time was seen in FATS-positive patients receiving radiotherapy (P = .006). The results of multivariate analysis revealed independent prognostic value of FATS expression in predicting longer DFS (odds ratio, 0.377; 95% confidence interval, 0.176 to 0.809; P = 0.012) for patients receiving adjuvant radiotherapy. In support of this, reduction of FATS expression in breast cancer cell lines, FATS positive group significantly sensitized than Knock-down of FATS group. Materials and Methods Tissue samples from 156 breast cancer patients and 42 controls in tumor bank were studied. FATS gene expression was evaluated using quantitative reverse transcription polymerase chain reaction (qRT-PCR). FATS function was examined in breast cancer cell lines using siRNA knock-downs and colony forming assays after irradiation. Conclusions FATS status is a biomarker in breast cancer to identify individuals likely to benefit from radiotherapy. PMID:28402275

The value of FATS expression in predicting sensitivity to radiotherapy in breast cancer.

PubMed

Zhang, Jun; Wu, Nan; Zhang, Tiemei; Sun, Tao; Su, Yi; Zhao, Jing; Mu, Kun; Jin, Zhao; Gao, Ming; Liu, Juntian; Gu, Lin

2017-06-13

The fragile-site associated tumor suppressor (FATS) is a newly identified tumor suppressor involved in radiation-induced tumorigenesis. The purpose of this study was to characterize FATS expression in breast cancers about radiotherapy benefit, patient characteristics, and prognosis. The expression of FATS mRNA was silent or downregulated in 95.2% of breast cancer samples compared with paired normal controls (P < .0001). Negative status of FATS was correlated with higher nuclear grade (P = .01) and shorter disease-free survival (DFS) of breast cancer (P = .036). In a multivariate analysis, FATS expression showed favorable prognostic value for DFS (odds ratio, 0.532; 95% confidence interval, 0.299 to 0.947; (P = .032). Furthermore, improved survival time was seen in FATS-positive patients receiving radiotherapy (P = .006). The results of multivariate analysis revealed independent prognostic value of FATS expression in predicting longer DFS (odds ratio, 0.377; 95% confidence interval, 0.176 to 0.809; P = 0.012) for patients receiving adjuvant radiotherapy. In support of this, reduction of FATS expression in breast cancer cell lines, FATS positive group significantly sensitized than Knock-down of FATS group. Tissue samples from 156 breast cancer patients and 42 controls in tumor bank were studied. FATS gene expression was evaluated using quantitative reverse transcription polymerase chain reaction (qRT-PCR). FATS function was examined in breast cancer cell lines using siRNA knock-downs and colony forming assays after irradiation. FATS status is a biomarker in breast cancer to identify individuals likely to benefit from radiotherapy.

Multivariate normative comparisons using an aggregated database

PubMed Central

Murre, Jaap M. J.; Huizenga, Hilde M.

2017-01-01

In multivariate normative comparisons, a patient’s profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R. PMID:28267796

Clinical validation of an epigenetic assay to predict negative histopathological results in repeat prostate biopsies.

PubMed

Partin, Alan W; Van Neste, Leander; Klein, Eric A; Marks, Leonard S; Gee, Jason R; Troyer, Dean A; Rieger-Christ, Kimberly; Jones, J Stephen; Magi-Galluzzi, Cristina; Mangold, Leslie A; Trock, Bruce J; Lance, Raymond S; Bigley, Joseph W; Van Criekinge, Wim; Epstein, Jonathan I

2014-10-01

The DOCUMENT multicenter trial in the United States validated the performance of an epigenetic test as an independent predictor of prostate cancer risk to guide decision making for repeat biopsy. Confirming an increased negative predictive value could help avoid unnecessary repeat biopsies. We evaluated the archived, cancer negative prostate biopsy core tissue samples of 350 subjects from a total of 5 urological centers in the United States. All subjects underwent repeat biopsy within 24 months with a negative (controls) or positive (cases) histopathological result. Centralized blinded pathology evaluation of the 2 biopsy series was performed in all available subjects from each site. Biopsies were epigenetically profiled for GSTP1, APC and RASSF1 relative to the ACTB reference gene using quantitative methylation specific polymerase chain reaction. Predetermined analytical marker cutoffs were used to determine assay performance. Multivariate logistic regression was used to evaluate all risk factors. The epigenetic assay resulted in a negative predictive value of 88% (95% CI 85-91). In multivariate models correcting for age, prostate specific antigen, digital rectal examination, first biopsy histopathological characteristics and race the test proved to be the most significant independent predictor of patient outcome (OR 2.69, 95% CI 1.60-4.51). The DOCUMENT study validated that the epigenetic assay was a significant, independent predictor of prostate cancer detection in a repeat biopsy collected an average of 13 months after an initial negative result. Due to its 88% negative predictive value adding this epigenetic assay to other known risk factors may help decrease unnecessary repeat prostate biopsies. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Tumor response ratio predicts overall survival in breast cancer patients treated with neoadjuvant chemotherapy.

PubMed

Miller, Marian; Ottesen, Rebecca A; Niland, Joyce C; Kruper, Laura; Chen, Steven L; Vito, Courtney

2014-10-01

Neoadjuvant chemotherapy (NAC) is commonly used to treat locally advanced breast cancer. Pathologic complete response (pCR) predicts improved overall survival (OS); however, prognosis of patients with partial response remains unclear. We evaluated whether tumor response ratio (TRR) is a better predictor of OS than current staging methods. Using the National Comprehensive Cancer Network Breast Cancer Outcomes Database, we identified patients with stage I-III breast cancer who had NAC and pretreatment imaging at City of Hope (1997-2010). Patient demographics, tumor characteristics, and OS were analyzed. TRR was calculated as residual in-breast disease divided by size on pre-NAC imaging. Four TRR groups were stratified; TRR 0 (pCR), TRR > 0-0.4 (strong partial response, SPR), TRR > 0.4-1.0 (weak partial response, WPR), or TRR > 1.0 (tumor growth, TG). OS was estimated by the Kaplan-Meier method and tested by the log-rank test. Cox regression was performed to evaluate associations between OS and TRR in a multivariable analysis while controlling for potential confounders. There were 218 eligible patients identified; 59 (27 %) had pCR, 61 (28 %) SPR, 72 (33 %) WPR, and 26 (12 %) TG. Five-year OS decreased continuously with increasing TRR:pCR (90 %), SPR (79 %), WPR (66 %), and TG (60 %). TRR was the only measure that significantly predicted OS (p = 0.0035); pathologic stage (p = 0.23) and pre-NAC clinical tumor stage (cT) (p = 0.87) were not significant. TRR continued to be statistically significant by multivariable analysis (p = 0.016). TRR takes into account both pretreatment and residual disease and more accurately predicts OS than pathologic stage and pre-NAC cT. TRR may be useful to more accurately assess prognosis and OS in breast cancer patients undergoing NAC.

MicroRNA-Related DNA Repair/Cell-Cycle Genes Independently Associated With Relapse After Radiation Therapy for Early Breast Cancer

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

Gee, Harriet E., E-mail: harriet.gee@sydney.edu.au; The Chris O'Brien Lifehouse, Missenden Road, Camperdown, NSW; Central Clinical School, Sydney Medical School, University of Sydney, NSW

Purpose: Local recurrence and distant failure after adjuvant radiation therapy for breast cancer remain significant clinical problems, incompletely predicted by conventional clinicopathologic markers. We had previously identified microRNA-139-5p and microRNA-1274a as key regulators of breast cancer radiation response in vitro. The purpose of this study was to investigate standard clinicopathologic markers of local recurrence in a contemporary series and to establish whether putative target genes of microRNAs involved in DNA repair and cell cycle control could better predict radiation therapy response in vivo. Methods and Materials: With institutional ethics board approval, local recurrence was measured in a contemporary, prospectively collected series ofmore » 458 patients treated with radiation therapy after breast-conserving surgery. Additionally, independent publicly available mRNA/microRNA microarray expression datasets totaling >1000 early-stage breast cancer patients, treated with adjuvant radiation therapy, with >10 years of follow-up, were analyzed. The expression of putative microRNA target biomarkers—TOP2A, POLQ, RAD54L, SKP2, PLK2, and RAG1—were correlated with standard clinicopathologic variables using 2-sided nonparametric tests, and to local/distant relapse and survival using Kaplan-Meier and Cox regression analysis. Results: We found a low rate of isolated local recurrence (1.95%) in our modern series, and that few clinicopathologic variables (such as lymphovascular invasion) were significantly predictive. In multiple independent datasets (n>1000), however, high expression of RAD54L, TOP2A, POLQ, and SKP2 significantly correlated with local recurrence, survival, or both in univariate and multivariate analyses (P<.001). Low RAG1 expression significantly correlated with local recurrence (multivariate, P=.008). Additionally, RAD54L, SKP2, and PLK2 may be predictive, being prognostic in radiation therapy–treated patients but not in untreated matched control individuals (n=107; P<.05). Conclusions: Biomarkers of DNA repair and cell cycle control can identify patients at high risk of treatment failure in those receiving radiation therapy for early breast cancer in independent cohorts. These should be further investigated prospectively, especially TOP2A and SKP2, for which targeted therapies are available.« less

Planning Target Volume D95 and Mean Dose Should Be Considered for Optimal Local Control for Stereotactic Ablative Radiation Therapy

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

Zhao, Lina; Zhou, Shouhao; Balter, Peter

Purpose: To identify the optimal dose parameters predictive for local/lobar control after stereotactic ablative radiation therapy (SABR) in early-stage non-small cell lung cancer (NSCLC). Methods and Materials: This study encompassed a total of 1092 patients (1200 lesions) with NSCLC of clinical stage T1-T2 N0M0 who were treated with SABR of 50 Gy in 4 fractions or 70 Gy in 10 fractions, depending on tumor location/size, using computed tomography-based heterogeneity corrections and a convolution superposition calculation algorithm. Patients were monitored by chest CT or positron emission tomography/CT and/or biopsy after SABR. Factors predicting local/lobar recurrence (LR) were determined by competing risk multivariate analysis.more » Continuous variables were divided into 2 subgroups at cutoff values identified by receiver operating characteristic curves. Results: At a median follow-up time of 31.7 months (interquartile range, 14.8-51.3 months), the 5-year time to local recurrence within the same lobe and overall survival rates were 93.8% and 44.8%, respectively. Total cumulative number of patients experiencing LR was 40 (3.7%), occurring at a median time of 14.4 months (range, 4.8-46 months). Using multivariate competing risk analysis, independent predictive factors for LR after SABR were minimum biologically effective dose (BED{sub 10}) to 95% of planning target volume (PTVD95 BED{sub 10}) ≤86 Gy (corresponding to PTV D95 physics dose of 42 Gy in 4 fractions or 55 Gy in 10 fractions) and gross tumor volume ≥8.3 cm{sup 3}. The PTVmean BED{sub 10} was highly correlated with PTVD95 BED{sub 10.} In univariate analysis, a cutoff of 130 Gy for PTVmean BED{sub 10} (corresponding to PTVmean physics dose of 55 Gy in 4 fractions or 75 Gy in 10 fractions) was also significantly associated with LR. Conclusions: In addition to gross tumor volume, higher radiation dose delivered to the PTV predicts for better local/lobar control. We recommend that both PTVD95 BED{sub 10} >86 Gy and PTVmean BED{sub 10} >130 Gy should be considered for SABR plan optimization.« less

Lateral control system design for VTOL landing on a DD963 in high sea states. M.S. Thesis

NASA Technical Reports Server (NTRS)

Bodson, M.

1982-01-01

The problem of designing lateral control systems for the safe landing of VTOL aircraft on small ships is addressed. A ship model is derived. The issues of estimation and prediction of ship motions are discussed, using optimal linear linear estimation techniques. The roll motion is the most important of the lateral motions, and it is found that it can be predicted for up to 10 seconds in perfect conditions. The automatic landing of the VTOL aircraft is considered, and a lateral controller, defined as a ship motion tracker, is designed, using optimal control techniqes. The tradeoffs between the tracking errors and the control authority are obtained. The important couplings between the lateral motions and controls are demonstrated, and it is shown that the adverse couplings between the sway and the roll motion at the landing pad are significant constraints in the tracking of the lateral ship motions. The robustness of the control system, including the optimal estimator, is studied, using the singular values analysis. Through a robustification procedure, a robust control system is obtained, and the usefulness of the singular values to define stability margins that take into account general types of unstructured modelling errors is demonstrated. The minimal destabilizing perturbations indicated by the singular values analysis are interpreted and related to the multivariable Nyquist diagrams.

IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

ERIC Educational Resources Information Center

Wang, Lijuan

2010-01-01

This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…

Physical activity in Black breast cancer survivors: implications for quality of life and mood at baseline and 6-month follow-up.

PubMed

Diggins, Allyson D; Hearn, Lauren E; Lechner, Suzanne C; Annane, Debra; Antoni, Michael H; Whitehead, Nicole Ennis

2017-06-01

The present study sought to examine the influence of physical activity on quality of life and negative mood in a sample of Black breast cancer survivors to determine if physical activity (dichotomized) predicted mean differences in negative mood and quality of life in this population. Study participants include 114 women diagnosed with breast cancer (any stage of disease, any type of breast cancer) recruited to participate in an adaptive cognitive-behavioral stress management intervention. The mean body mass index of the sample at baseline was 31.39 (standard deviation = 7.17). A multivariate analysis of covariance (MANCOVA) was conducted to determine if baseline physical activity predicted mean differences in negative mood and quality of life at baseline and at follow ups while controlling for relevant covariates. A one-way MANCOVA revealed a significant multivariate effect by physical activity group for the combined dependent variables at Time 2 (post 10-week intervention), p = .039. The second one-way MANCOVA revealed a significant multivariate effect at Time 3 (6 months after Time 2), p = .034. Specifically, Black breast cancer survivors who engaged in physical activity experienced significantly lower negative mood and higher social/family well-being at Time 2 and higher spiritual and functional well-being at Times 2 and 3. Results show that baseline physical activity served protective functions for breast cancer survivors over time. Developing culturally relevant physical activity interventions specifically for Black breast cancer survivors may prove vital to improving quality of life and mood in this population. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Diagnostic tools for nearest neighbors techniques when used with satellite imagery

Treesearch

Ronald E. McRoberts

2009-01-01

Nearest neighbors techniques are non-parametric approaches to multivariate prediction that are useful for predicting both continuous and categorical forest attribute variables. Although some assumptions underlying nearest neighbor techniques are common to other prediction techniques such as regression, other assumptions are unique to nearest neighbor techniques....

The Neural-fuzzy Thermal Error Compensation Controller on CNC Machining Center

NASA Astrophysics Data System (ADS)

Tseng, Pai-Chung; Chen, Shen-Len

The geometric errors and structural thermal deformation are factors that influence the machining accuracy of Computer Numerical Control (CNC) machining center. Therefore, researchers pay attention to thermal error compensation technologies on CNC machine tools. Some real-time error compensation techniques have been successfully demonstrated in both laboratories and industrial sites. The compensation results still need to be enhanced. In this research, the neural-fuzzy theory has been conducted to derive a thermal prediction model. An IC-type thermometer has been used to detect the heat sources temperature variation. The thermal drifts are online measured by a touch-triggered probe with a standard bar. A thermal prediction model is then derived by neural-fuzzy theory based on the temperature variation and the thermal drifts. A Graphic User Interface (GUI) system is also built to conduct the user friendly operation interface with Insprise C++ Builder. The experimental results show that the thermal prediction model developed by neural-fuzzy theory methodology can improve machining accuracy from 80µm to 3µm. Comparison with the multi-variable linear regression analysis the compensation accuracy is increased from ±10µm to ±3µm.

Medicalising normality? Using a simulated dataset to assess the performance of different diagnostic criteria of HIV-associated cognitive impairment

PubMed Central

De Francesco, Davide; Leech, Robert; Sabin, Caroline A.; Winston, Alan

2018-01-01

Objective The reported prevalence of cognitive impairment remains similar to that reported in the pre-antiretroviral therapy era. This may be partially artefactual due to the methods used to diagnose impairment. In this study, we evaluated the diagnostic performance of the HIV-associated neurocognitive disorder (Frascati criteria) and global deficit score (GDS) methods in comparison to a new, multivariate method of diagnosis. Methods Using a simulated ‘normative’ dataset informed by real-world cognitive data from the observational Pharmacokinetic and Clinical Observations in PeoPle Over fiftY (POPPY) cohort study, we evaluated the apparent prevalence of cognitive impairment using the Frascati and GDS definitions, as well as a novel multivariate method based on the Mahalanobis distance. We then quantified the diagnostic properties (including positive and negative predictive values and accuracy) of each method, using bootstrapping with 10,000 replicates, with a separate ‘test’ dataset to which a pre-defined proportion of ‘impaired’ individuals had been added. Results The simulated normative dataset demonstrated that up to ~26% of a normative control population would be diagnosed with cognitive impairment with the Frascati criteria and ~20% with the GDS. In contrast, the multivariate Mahalanobis distance method identified impairment in ~5%. Using the test dataset, diagnostic accuracy [95% confidence intervals] and positive predictive value (PPV) was best for the multivariate method vs. Frascati and GDS (accuracy: 92.8% [90.3–95.2%] vs. 76.1% [72.1–80.0%] and 80.6% [76.6–84.5%] respectively; PPV: 61.2% [48.3–72.2%] vs. 29.4% [22.2–36.8%] and 33.9% [25.6–42.3%] respectively). Increasing the a priori false positive rate for the multivariate Mahalanobis distance method from 5% to 15% resulted in an increase in sensitivity from 77.4% (64.5–89.4%) to 92.2% (83.3–100%) at a cost of specificity from 94.5% (92.8–95.2%) to 85.0% (81.2–88.5%). Conclusion Our simulations suggest that the commonly used diagnostic criteria of HIV-associated cognitive impairment label a significant proportion of a normative reference population as cognitively impaired, which will likely lead to a substantial over-estimate of the true proportion in a study population, due to their lower than expected specificity. These findings have important implications for clinical research regarding cognitive health in people living with HIV. More accurate methods of diagnosis should be implemented, with multivariate techniques offering a promising solution. PMID:29641619

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

PubMed

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in challenging Raman endoscopic applications.

Augmented classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2004-02-03

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-07-26

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-01-11

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

NASA Astrophysics Data System (ADS)

Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

2016-10-01

The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

Developmental Associations Between Adolescent Alcohol Use and Dating Aggression

PubMed Central

Reyes, H. Luz McNaughton; Foshee, Vangie A.; Bauer, Daniel J.; Ennett, Susan T.

2012-01-01

While numerous studies have established a link between alcohol use and partner violence in adulthood, little research has examined this relation during adolescence. The current study used multivariate growth models to examine relations between alcohol use and dating aggression across grades 8 through 12 controlling for shared risk factors (common causes) that predict both behaviors. Associations between trajectories of alcohol use and dating aggression were reduced substantially when common causes were controlled. Concurrent associations between the two behaviors were significant across nearly all grades but no evidence was found for prospective connections from prior alcohol use to subsequent dating aggression or vice versa. Findings suggest that prevention efforts should target common causes of alcohol use and dating aggression. PMID:23589667

Gut microbiota in early pediatric multiple sclerosis: a case-control study.

PubMed

Tremlett, Helen; Fadrosh, Douglas W; Faruqi, Ali A; Zhu, Feng; Hart, Janace; Roalstad, Shelly; Graves, Jennifer; Lynch, Susan; Waubant, Emmanuelle

2016-08-01

Alterations in the gut microbial community composition may be influential in neurological disease. Microbial community profiles were compared between early onset pediatric multiple sclerosis (MS) and control children similar for age and sex. Children ≤18 years old within 2 years of MS onset or controls without autoimmune disorders attending a University of California, San Francisco, USA, pediatric clinic were examined for fecal bacterial community composition and predicted function by 16S ribosomal RNA sequencing and phylogenetic reconstruction of unobserved states (PICRUSt) analysis. Associations between subject characteristics and the microbiota, including beta diversity and taxa abundance, were identified using non-parametric tests, permutational multivariate analysis of variance and negative binomial regression. Eighteen relapsing-remitting MS cases and 17 controls (mean age 13 years; range 4-18) were studied. Cases had a short disease duration (mean 11 months; range 2-24) and half were immunomodulatory drug (IMD) naïve. Whilst overall gut bacterial beta diversity was not significantly related to MS status, IMD exposure was (Canberra, P < 0.02). However, relative to controls, MS cases had a significant enrichment in relative abundance for members of the Desulfovibrionaceae (Bilophila, Desulfovibrio and Christensenellaceae) and depletion in Lachnospiraceae and Ruminococcaceae (all P and q < 0.000005). Microbial genes predicted as enriched in MS versus controls included those involved in glutathione metabolism (Mann-Whitney, P = 0.017), findings that were consistent regardless of IMD exposure. In recent onset pediatric MS, perturbations in the gut microbiome composition were observed, in parallel with predicted enrichment of metabolic pathways associated with neurodegeneration. Findings were suggestive of a pro-inflammatory milieu. © 2016 EAN.

Physiology-Based Modeling May Predict Surgical Treatment Outcome for Obstructive Sleep Apnea

PubMed Central

Li, Yanru; Ye, Jingying; Han, Demin; Cao, Xin; Ding, Xiu; Zhang, Yuhuan; Xu, Wen; Orr, Jeremy; Jen, Rachel; Sands, Scott; Malhotra, Atul; Owens, Robert

2017-01-01

Study Objectives: To test whether the integration of both anatomical and nonanatomical parameters (ventilatory control, arousal threshold, muscle responsiveness) in a physiology-based model will improve the ability to predict outcomes after upper airway surgery for obstructive sleep apnea (OSA). Methods: In 31 patients who underwent upper airway surgery for OSA, loop gain and arousal threshold were calculated from preoperative polysomnography (PSG). Three models were compared: (1) a multiple regression based on an extensive list of PSG parameters alone; (2) a multivariate regression using PSG parameters plus PSG-derived estimates of loop gain, arousal threshold, and other trait surrogates; (3) a physiological model incorporating selected variables as surrogates of anatomical and nonanatomical traits important for OSA pathogenesis. Results: Although preoperative loop gain was positively correlated with postoperative apnea-hypopnea index (AHI) (P = .008) and arousal threshold was negatively correlated (P = .011), in both model 1 and 2, the only significant variable was preoperative AHI, which explained 42% of the variance in postoperative AHI. In contrast, the physiological model (model 3), which included AHIREM (anatomy term), fraction of events that were hypopnea (arousal term), the ratio of AHIREM and AHINREM (muscle responsiveness term), loop gain, and central/mixed apnea index (control of breathing terms), was able to explain 61% of the variance in postoperative AHI. Conclusions: Although loop gain and arousal threshold are associated with residual AHI after surgery, only preoperative AHI was predictive using multivariate regression modeling. Instead, incorporating selected surrogates of physiological traits on the basis of OSA pathophysiology created a model that has more association with actual residual AHI. Commentary: A commentary on this article appears in this issue on page 1023. Clinical Trial Registration: ClinicalTrials.Gov; Title: The Impact of Sleep Apnea Treatment on Physiology Traits in Chinese Patients With Obstructive Sleep Apnea; Identifier: NCT02696629; URL: https://clinicaltrials.gov/show/NCT02696629 Citation: Li Y, Ye J, Han D, Cao X, Ding X, Zhang Y, Xu W, Orr J, Jen R, Sands S, Malhotra A, Owens R. Physiology-based modeling may predict surgical treatment outcome for obstructive sleep apnea. J Clin Sleep Med. 2017;13(9):1029–1037. PMID:28818154

Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

DOEpatents

Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

2006-11-14

A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

Capital market based warning indicators of bank runs

NASA Astrophysics Data System (ADS)

Vakhtina, Elena; Wosnitza, Jan Henrik

2015-01-01

In this investigation, we examine the univariate as well as the multivariate capabilities of the log-periodic [super-exponential] power law (LPPL) for the prediction of bank runs. The research is built upon daily CDS spreads of 40 international banks for the period from June 2007 to March 2010, i.e. at the heart of the global financial crisis. For this time period, 20 of the financial institutions received federal bailouts and are labeled as defaults while the remaining institutions are categorized as non-defaults. The employed multivariate pattern recognition approach represents a modification of the CORA3 algorithm. The approach is found to be robust regardless of reasonable changes of its inputs. Despite the fact that distinct alarm indices for banks do not clearly demonstrate predictive capabilities of the LPPL, the synchronized alarm indices confirm the multivariate discriminative power of LPPL patterns in CDS spread developments acknowledged by bootstrap intervals with 70% confidence level.

A time domain frequency-selective multivariate Granger causality approach.

PubMed

Leistritz, Lutz; Witte, Herbert

2016-08-01

The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions. It is based on a comparison of prediction errors of multivariate autoregressive models fitted to systematically modified time series. These modifications are based on signal decompositions, which enable a targeted cancellation of specific signal components with specific spectral properties. Depending on the embedded signal decomposition method, a frequency-selective or data-driven signal-adaptive Granger Causality Index may be derived.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Acoustic neuroma: potential risk factors and audiometric surveillance in the aluminium industry

PubMed Central

Taiwo, Oyebode; Galusha, Deron; Tessier-Sherman, Baylah; Kirsche, Sharon; Cantley, Linda; Slade, Martin D; Cullen, Mark R; Donoghue, A Michael

2014-01-01

Objectives To look for an association between acoustic neuroma (AN) and participation in a hearing conservation programme (HCP) and also for an association between AN and possible occupational risk factors in the aluminium industry. Methods We conducted a case–control analysis of a population of US aluminium production workers in 8 smelters and 43 other plants. Using insurance claims data, 97 cases of AN were identified between 1996 and 2009. Each was matched with four controls. Covariates included participation in a HCP, working in an aluminium smelter, working in an electrical job and hearing loss. Results In the bivariate analyses, covariates associated with AN were participation in the HCP (OR=1.72; 95% CI 1.09 to 2.69) and smelter work (OR=1.88; 95% CI 1.06 to 3.36). Electrical work was not significant (OR=1.60; 95% CI 0.65 to 3.94). Owing to high participation in the HCP in smelters, multivariate subanalyses were required. In the multivariate analyses, participation in the HCP was the only statistically significant risk factor for AN. In the multivariate analysis restricted to employees not working in a smelter, the OR was 1.81 (95% CI 1.04 to 3.17). Hearing loss, an indirect measure of in-ear noise dose, was not predictive of AN. Conclusions Our results suggest the incidental detection of previously undiagnosed tumours in workers who participated in the company-sponsored HCP. The increased medical surveillance among this population of workers most likely introduced detection bias, leading to the identification of AN cases that would have otherwise remained undetected. PMID:25015928

Stress and Personal Resource as Predictors of the Adjustment of Parents to Autistic Children: A Multivariate Model

ERIC Educational Resources Information Center

Siman-Tov, Ayelet; Kaniel, Shlomo

2011-01-01

The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

All-Possible-Subsets for MANOVA and Factorial MANOVAs: Less than a Weekend Project

ERIC Educational Resources Information Center

Nimon, Kim; Zientek, Linda Reichwein; Kraha, Amanda

2016-01-01

Multivariate techniques are increasingly popular as researchers attempt to accurately model a complex world. MANOVA is a multivariate technique used to investigate the dimensions along which groups differ, and how these dimensions may be used to predict group membership. A concern in a MANOVA analysis is to determine if a smaller subset of…

Child and adult outcomes of chronic child maltreatment.

PubMed

Jonson-Reid, Melissa; Kohl, Patricia L; Drake, Brett

2012-05-01

To describe how child maltreatment chronicity is related to negative outcomes in later childhood and early adulthood. The study included 5994 low-income children from St Louis, including 3521 with child maltreatment reports, who were followed from 1993-1994 through 2009. Children were 1.5 to 11 years of age at sampling. Data include administrative and treatment records indicating substance abuse, mental health treatment, brain injury, sexually transmitted disease, suicide attempts, and violent delinquency before age 18 and child maltreatment perpetration, mental health treatment, or substance abuse in adulthood. Multivariate analysis controlled for potential confounders. Child maltreatment chronicity predicted negative childhood outcomes in a linear fashion (eg, percentage with at least 1 negative outcome: no maltreatment = 29.7%, 1 report = 39.5%, 4 reports = 67.1%). Suicide attempts before age 18 showed the largest proportionate increase with repeated maltreatment (no report versus 4+ reports = +625%, P < .0001). The dose-response relationship was reduced once controls for other adverse child outcomes were added in multivariate models of child maltreatment perpetration and mental health issues. The relationship between adult substance abuse and maltreatment report history disappeared after controlling for adverse child outcomes. Child maltreatment chronicity as measured by official reports is a robust indicator of future negative outcomes across a range of systems, but this relationship may desist for certain adult outcomes once childhood adverse events are controlled. Although primary and secondary prevention remain important approaches, this study suggests that enhanced tertiary prevention may pay high dividends across a range of medical and behavioral domains.

Self-tuning multivariable pole placement control of a multizone crystal growth furnace

NASA Technical Reports Server (NTRS)

Batur, C.; Sharpless, R. B.; Duval, W. M. B.; Rosenthal, B. N.

1992-01-01

This paper presents the design and implementation of a multivariable self-tuning temperature controller for the control of lead bromide crystal growth. The crystal grows inside a multizone transparent furnace. There are eight interacting heating zones shaping the axial temperature distribution inside the furnace. A multi-input, multi-output furnace model is identified on-line by a recursive least squares estimation algorithm. A multivariable pole placement controller based on this model is derived and implemented. Comparison between single-input, single-output and multi-input, multi-output self-tuning controllers demonstrates that the zone-to-zone interactions can be minimized better by a multi-input, multi-output controller design. This directly affects the quality of crystal grown.

The EXCITE Trial: Predicting a Clinically Meaningful Motor Activity Log Outcome

PubMed Central

Park, Si-Woon; Wolf, Steven L.; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S.

2013-01-01

Background and Objective This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. Methods A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Results Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Conclusions Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT. PMID:18780883

The EXCITE Trial: Predicting a clinically meaningful motor activity log outcome.

PubMed

Park, Si-Woon; Wolf, Steven L; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S

2008-01-01

This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT.

Feedback control design for non-inductively sustained scenarios in NSTX-U using TRANSP

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

Boyer, M. D.; Andre, R. G.; Gates, D. A.

This paper examines a method for real-time control of non-inductively sustained scenarios in NSTX-U by using TRANSP, a time-dependent integrated modeling code for prediction and interpretive analysis of tokamak experimental data, as a simulator. The actuators considered for control in this work are the six neutral beam sources and the plasma boundary shape. To understand the response of the plasma current, stored energy, and central safety factor to these actuators and to enable systematic design of control algorithms, simulations were run in which the actuators were modulated and a linearized dynamic response model was generated. A multi-variable model-based control schememore » that accounts for the coupling and slow dynamics of the system while mitigating the effect of actuator limitations was designed and simulated. Simulations show that modest changes in the outer gap and heating power can improve the response time of the system, reject perturbations, and track target values of the controlled values.« less

Feedback control design for non-inductively sustained scenarios in NSTX-U using TRANSP

DOE PAGES

Boyer, M. D.; Andre, R. G.; Gates, D. A.; ...

2017-04-24

This paper examines a method for real-time control of non-inductively sustained scenarios in NSTX-U by using TRANSP, a time-dependent integrated modeling code for prediction and interpretive analysis of tokamak experimental data, as a simulator. The actuators considered for control in this work are the six neutral beam sources and the plasma boundary shape. To understand the response of the plasma current, stored energy, and central safety factor to these actuators and to enable systematic design of control algorithms, simulations were run in which the actuators were modulated and a linearized dynamic response model was generated. A multi-variable model-based control schememore » that accounts for the coupling and slow dynamics of the system while mitigating the effect of actuator limitations was designed and simulated. Simulations show that modest changes in the outer gap and heating power can improve the response time of the system, reject perturbations, and track target values of the controlled values.« less

Feedback control design for non-inductively sustained scenarios in NSTX-U using TRANSP

NASA Astrophysics Data System (ADS)

Boyer, M. D.; Andre, R. G.; Gates, D. A.; Gerhardt, S. P.; Menard, J. E.; Poli, F. M.

2017-06-01

This paper examines a method for real-time control of non-inductively sustained scenarios in NSTX-U by using TRANSP, a time-dependent integrated modeling code for prediction and interpretive analysis of tokamak experimental data, as a simulator. The actuators considered for control in this work are the six neutral beam sources and the plasma boundary shape. To understand the response of the plasma current, stored energy, and central safety factor to these actuators and to enable systematic design of control algorithms, simulations were run in which the actuators were modulated and a linearized dynamic response model was generated. A multi-variable model-based control scheme that accounts for the coupling and slow dynamics of the system while mitigating the effect of actuator limitations was designed and simulated. Simulations show that modest changes in the outer gap and heating power can improve the response time of the system, reject perturbations, and track target values of the controlled values.

Physics-based Control-oriented Modeling of the Current Profile Evolution in NSTX-Upgrade

NASA Astrophysics Data System (ADS)

Ilhan, Zeki; Barton, Justin; Shi, Wenyu; Schuster, Eugenio; Gates, David; Gerhardt, Stefan; Kolemen, Egemen; Menard, Jonathan

2013-10-01

The operational goals for the NSTX-Upgrade device include non-inductive sustainment of high- β plasmas, realization of the high performance equilibrium scenarios with neutral beam heating, and achievement of longer pulse durations. Active feedback control of the current profile is proposed to enable these goals. Motivated by the coupled, nonlinear, multivariable, distributed-parameter plasma dynamics, the first step towards feedback control design is the development of a physics-based, control-oriented model for the current profile evolution in response to non-inductive current drives and heating systems. For this purpose, the nonlinear magnetic-diffusion equation is coupled with empirical models for the electron density, electron temperature, and non-inductive current drives (neutral beams). The resulting first-principles-driven, control-oriented model is tailored for NSTX-U based on the PTRANSP predictions. Main objectives and possible challenges associated with the use of the developed model for control design are discussed. This work was supported by PPPL.

Multivariate Drought Characterization in India for Monitoring and Prediction

NASA Astrophysics Data System (ADS)

Sreekumaran Unnithan, P.; Mondal, A.

2016-12-01

Droughts are one of the most important natural hazards that affect the society significantly in terms of mortality and productivity. The metric that is most widely used by the India Meteorological Department (IMD) to monitor and predict the occurrence, spread, intensification and termination of drought is based on the univariate Standardized Precipitation Index (SPI). However, droughts may be caused by the influence and interaction of many variables (such as precipitation, soil moisture, runoff, etc.), emphasizing the need for a multivariate approach for drought characterization. This study advocates and illustrates use of the recently proposed multivariate standardized drought index (MSDI) in monitoring and prediction of drought and assessing its concerned risk in the Indian region. MSDI combines information from multiple sources: precipitation and soil moisture, and has been deemed to be a more reliable drought index. All-India monthly rainfall and soil moisture data sets are analysed for the period 1980 to 2014 to characterize historical droughts using both the univariate indices, the precipitation-based SPI and the standardized soil moisture index (SSI), as well as the multivariate MSDI using parametric and non-parametric approaches. We confirm that MSDI can capture droughts of 1986 and 1990 that aren't detected by using SPI alone. Moreover, in 1987, MSDI indicated a higher severity of drought when a deficiency in both soil moisture and precipitation was encountered. Further, this study also explores the use of MSDI for drought forecasts and assesses its performance vis-à-vis existing predictions from the IMD. Future research efforts will be directed towards formulating a more robust standardized drought indicator that can take into account socio-economic aspects that also play a key role for water-stressed regions such as India.

Predicting trauma patient mortality: ICD [or ICD-10-AM] versus AIS based approaches.

PubMed

Willis, Cameron D; Gabbe, Belinda J; Jolley, Damien; Harrison, James E; Cameron, Peter A

2010-11-01

The International Classification of Diseases Injury Severity Score (ICISS) has been proposed as an International Classification of Diseases (ICD)-10-based alternative to mortality prediction tools that use Abbreviated Injury Scale (AIS) data, including the Trauma and Injury Severity Score (TRISS). To date, studies have not examined the performance of ICISS using Australian trauma registry data. This study aimed to compare the performance of ICISS with other mortality prediction tools in an Australian trauma registry. This was a retrospective review of prospectively collected data from the Victorian State Trauma Registry. A training dataset was created for model development and a validation dataset for evaluation. The multiplicative ICISS model was compared with a worst injury ICISS approach, Victorian TRISS (V-TRISS, using local coefficients), maximum AIS severity and a multivariable model including ICD-10-AM codes as predictors. Models were investigated for discrimination (C-statistic) and calibration (Hosmer-Lemeshow statistic). The multivariable approach had the highest level of discrimination (C-statistic 0.90) and calibration (H-L 7.65, P= 0.468). Worst injury ICISS, V-TRISS and maximum AIS had similar performance. The multiplicative ICISS produced the lowest level of discrimination (C-statistic 0.80) and poorest calibration (H-L 50.23, P < 0.001). The performance of ICISS may be affected by the data used to develop estimates, the ICD version employed, the methods for deriving estimates and the inclusion of covariates. In this analysis, a multivariable approach using ICD-10-AM codes was the best-performing method. A multivariable ICISS approach may therefore be a useful alternative to AIS-based methods and may have comparable predictive performance to locally derived TRISS models. © 2010 The Authors. ANZ Journal of Surgery © 2010 Royal Australasian College of Surgeons.

Multivariable adaptive closed-loop control of an artificial pancreas without meal and activity announcement.

PubMed

Turksoy, Kamuran; Bayrak, Elif Seyma; Quinn, Lauretta; Littlejohn, Elizabeth; Cinar, Ali

2013-05-01

Accurate closed-loop control is essential for developing artificial pancreas (AP) systems that adjust insulin infusion rates from insulin pumps. Glucose concentration information from continuous glucose monitoring (CGM) systems is the most important information for the control system. Additional physiological measurements can provide valuable information that can enhance the accuracy of the control system. Proportional-integral-derivative control and model predictive control have been popular in AP development. Their implementations to date rely on meal announcements (e.g., bolus insulin dose based on insulin:carbohydrate ratios) by the user. Adaptive control techniques provide a powerful alternative that do not necessitate any meal or activity announcements. Adaptive control systems based on the generalized predictive control framework are developed by extending the recursive modeling techniques. Physiological signals such as energy expenditure and galvanic skin response are used along with glucose measurements to generate a multiple-input-single-output model for predicting future glucose concentrations used by the controller. Insulin-on-board (IOB) is also estimated and used in control decisions. The controllers were tested with clinical studies that include seven cases with three different patients with type 1 diabetes for 32 or 60 h without any meal or activity announcements. The adaptive control system kept glucose concentration in the normal preprandial and postprandial range (70-180 mg/dL) without any meal or activity announcements during the test period. After IOB estimation was added to the control system, mild hypoglycemic episodes were observed only in one of the four experiments. This was reflected in a plasma glucose value of 56 mg/dL (YSI 2300 STAT; Yellow Springs Instrument, Yellow Springs, OH) and a CGM value of 63 mg/dL). Regulation of blood glucose concentration with an AP using adaptive control techniques was successful in clinical studies, even without any meal and physical activity announcement.

Prediction of mortality rates using a model with stochastic parameters

NASA Astrophysics Data System (ADS)

Tan, Chon Sern; Pooi, Ah Hin

2016-10-01

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

Statistical analysis of multivariate atmospheric variables. [cloud cover

NASA Technical Reports Server (NTRS)

Tubbs, J. D.

1979-01-01

Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

[Predictive factors of mortality in extremely preterm infants].

PubMed

Lin, L; Fang, M C; Jiang, H; Zhu, M L; Chen, S Q; Lin, Z L

2018-04-02

Objective: To investigate the predictive factors of mortality in extremely preterm infants. Methods: The retrospective case-control study was accomplished in the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. A total of 268 extremely preterm infants seen from January 1, 1999 to December 31, 2015 were divided into survival group (192 cases) and death group (76 cases). The potential predictive factors of mortality were identified by univariate analysis, and then analyzed by multivariate unconditional Logistic regression analysis. The mortality and predictive factors were also compared between two time periods, which were January 1, 1999 to December 31, 2007 (65 cases) and January 1, 2008 to December 31, 2015 (203 cases). Results: The median gestational age (GA) of extremely preterm infants was 27 weeks (23 +3 -27 +6 weeks). The mortality was higher in infants with GA of 25-<26 weeks ( OR= 2.659, 95% CI: 1.211-5.840) and<25 weeks ( OR= 10.029, 95% CI: 3.266-30.792) compared to that in infants with GA> 26 weeks. From January 1, 2008 to December 31, 2015, the number of extremely preterm infants was increased significantly compared to the previous 9 years, while the mortality decreased significantly ( OR= 0.490, 95% CI: 0.272-0.884). Multivariate unconditional Logistic regression analysis showed that GA below 25 weeks ( OR= 6.033, 95% CI: 1.393-26.133), lower birth weight ( OR= 0.997, 95% CI: 0.995-1.000), stage Ⅲ necrotizing enterocolitis (NEC) ( OR= 15.907, 95% CI: 3.613-70.033), grade Ⅰ and Ⅱ intraventricular hemorrhage (IVH) ( OR= 0.260, 95% CI: 0.117-0.575) and dependence on invasive mechanical ventilation ( OR= 3.630, 95% CI: 1.111-11.867) were predictive factors of mortality in extremely preterm infants. Conclusions: GA below 25 weeks, lower birth weight, stage Ⅲ NEC and dependence on invasive mechanical ventilation are risk factors of mortality in extremely preterm infants. But grade ⅠandⅡ IVH is protective factor.

Multivariable PID controller design tuning using bat algorithm for activated sludge process

NASA Astrophysics Data System (ADS)

Atikah Nor’Azlan, Nur; Asmiza Selamat, Nur; Mat Yahya, Nafrizuan

2018-04-01

The designing of a multivariable PID control for multi input multi output is being concerned with this project by applying four multivariable PID control tuning which is Davison, Penttinen-Koivo, Maciejowski and Proposed Combined method. The determination of this study is to investigate the performance of selected optimization technique to tune the parameter of MPID controller. The selected optimization technique is Bat Algorithm (BA). All the MPID-BA tuning result will be compared and analyzed. Later, the best MPID-BA will be chosen in order to determine which techniques are better based on the system performances in terms of transient response.

Predictive value of sperm morphology and progressively motile sperm count for pregnancy outcomes in intrauterine insemination.

PubMed

Lemmens, Louise; Kos, Snjezana; Beijer, Cornelis; Brinkman, Jacoline W; van der Horst, Frans A L; van den Hoven, Leonie; Kieslinger, Dorit C; van Trooyen-van Vrouwerff, Netty J; Wolthuis, Albert; Hendriks, Jan C M; Wetzels, Alex M M

2016-06-01

To investigate the value of sperm parameters to predict an ongoing pregnancy outcome in couples treated with intrauterine insemination (IUI), during a methodologically stable period of time. Retrospective, observational study with logistic regression analyses. University hospital. A total of 1,166 couples visiting the fertility laboratory for their first IUI episode, including 4,251 IUI cycles. None. Sperm morphology, total progressively motile sperm count (TPMSC), and number of inseminated progressively motile spermatozoa (NIPMS); odds ratios (ORs) of the sperm parameters after the first IUI cycle and the first finished IUI episode; discriminatory accuracy of the multivariable model. None of the sperm parameters was of predictive value for pregnancy after the first IUI cycle. In the first finished IUI episode, a positive relationship was found for ≤4% of morphologically normal spermatozoa (OR 1.39) and a moderate NIPMS (5-10 million; OR 1.73). Low NIPMS showed a negative relation (≤1 million; OR 0.42). The TPMSC had no predictive value. The multivariable model (i.e., sperm morphology, NIPMS, female age, male age, and the number of cycles in the episode) had a moderate discriminatory accuracy (area under the curve 0.73). Intrauterine insemination is especially relevant for couples with moderate male factor infertility (sperm morphology ≤4%, NIPMS 5-10 million). In the multivariable model, however, the predictive power of these sperm parameters is rather low. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Prediction of outcome following early salvage radiotherapy among patients with biochemical recurrence after radical prostatectomy.

PubMed

Briganti, Alberto; Karnes, R Jeffrey; Joniau, Steven; Boorjian, Stephen A; Cozzarini, Cesare; Gandaglia, Giorgio; Hinkelbein, Wolfgang; Haustermans, Karin; Tombal, Bertrand; Shariat, Shahrokh; Sun, Maxine; Karakiewicz, Pierre I; Montorsi, Francesco; Van Poppel, Hein; Wiegel, Thomas

2014-09-01

Early salvage radiotherapy (eSRT) represents the only curative option for prostate cancer patients experiencing biochemical recurrence (BCR) for local recurrence after radical prostatectomy (RP). To develop and internally validate a novel nomogram predicting BCR after eSRT in patients treated with RP. Using a multi-institutional cohort, 472 node-negative patients who experienced BCR after RP were identified. All patients received eSRT, defined as local radiation to the prostate and seminal vesicle bed, delivered at prostate-specific antigen (PSA) ≤ 0.5 ng/ml. BCR after eSRT was defined as two consecutive PSA values ≥ 0.2 ng/ml. Uni- and multivariable Cox regression models predicting BCR after eSRT were fitted. Regression-based coefficients were used to develop a nomogram predicting the risk of 5-yr BCR after eSRT. The discrimination of the nomogram was quantified with the Harrell concordance index and the calibration plot method. Two hundred bootstrap resamples were used for internal validation. Mean follow-up was 58 mo (median: 48 mo). Overall, 5-yr BCR-free survival rate after eSRT was 73.4%. In univariable analyses, pathologic stage, Gleason score, and positive surgical margins were associated with the risk of BCR after eSRT (all p ≤ 0.04). These results were confirmed in multivariable analysis, where all the previously mentioned covariates as well as pre-RT PSA were significantly associated with BCR after eSRT (all p ≤ 0.04). A coefficient-based nomogram demonstrated a bootstrap-corrected discrimination of 0.74. Our study is limited by its retrospective nature and use of BCR as an end point. eSRT leads to excellent cancer control in patients with BCR for presumed local failure after RP. We developed the first nomogram to predict outcome after eSRT. Our model facilitates risk stratification and patient counselling regarding the use of secondary therapy for individuals experiencing BCR after RP. Salvage radiotherapy leads to optimal cancer control in patients who experience recurrence after radical prostatectomy. We developed a novel tool to identify the best candidates for salvage treatment and to allow selection of patients to be considered for additional forms of therapy. Copyright © 2013 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Development and validation of a risk calculator predicting exercise-induced ventricular arrhythmia in patients with cardiovascular disease.

PubMed

Hermes, Ilarraza-Lomelí; Marianna, García-Saldivia; Jessica, Rojano-Castillo; Carlos, Barrera-Ramírez; Rafael, Chávez-Domínguez; María Dolores, Rius-Suárez; Pedro, Iturralde

2016-10-01

Mortality due to cardiovascular disease is often associated with ventricular arrhythmias. Nowadays, patients with cardiovascular disease are more encouraged to take part in physical training programs. Nevertheless, high-intensity exercise is associated to a higher risk for sudden death, even in apparently healthy people. During an exercise testing (ET), health care professionals provide patients, in a controlled scenario, an intense physiological stimulus that could precipitate cardiac arrhythmia in high risk individuals. There is still no clinical or statistical tool to predict this incidence. The aim of this study was to develop a statistical model to predict the incidence of exercise-induced potentially life-threatening ventricular arrhythmia (PLVA) during high intensity exercise. 6415 patients underwent a symptom-limited ET with a Balke ramp protocol. A multivariate logistic regression model where the primary outcome was PLVA was performed. Incidence of PLVA was 548 cases (8.5%). After a bivariate model, thirty one clinical or ergometric variables were statistically associated with PLVA and were included in the regression model. In the multivariate model, 13 of these variables were found to be statistically significant. A regression model (G) with a X(2) of 283.987 and a p<0.001, was constructed. Significant variables included: heart failure, antiarrhythmic drugs, myocardial lower-VD, age and use of digoxin, nitrates, among others. This study allows clinicians to identify patients at risk of ventricular tachycardia or couplets during exercise, and to take preventive measures or appropriate supervision. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Cytokine activation is predictive of mortality in Zambian patients with AIDS-related diarrhoea

PubMed Central

Zulu, Isaac; Hassan, Ghaniah; Njobvu RN, Lungowe; Dhaliwal, Winnie; Sianongo, Sandie; Kelly, Paul

2008-01-01

Background Mortality in Zambian AIDS patients is high, especially in patients with diarrhoea, and there is still unacceptably high mortality in Zambian patients just starting anti-retroviral therapy. We set out to determine if high concentrations of serum cytokines correlate with mortality. Methods Serum samples from 30 healthy controls (HIV seropositive and seronegative) and 50 patients with diarrhoea (20 of whom died within 6 weeks) were analysed. Concentrations of tumour necrosis factor receptor p55 (TNFR p55), macrophage migration inhibitory factor (MIF), interleukin (IL)-6, IL-12, interferon (IFN)-γ and C-reactive protein (CRP) were measured by ELISA, and correlated with mortality after 6 weeks follow-up. Results Apart from IL-12, concentrations of all cytokines, TNFR p55 and CRP increased with worsening severity of disease, showing highly statistically significant trends. In a multivariable analysis high TNFR p55, IFN-γ, CRP and low CD4 count (CD4 count <100) were predictive of mortality. Although nutritional status (assessed by body mass index, BMI) was predictive in univariate analysis, it was not an independent predictor in multivariate analysis. Conclusion High serum concentrations of TNFR p55, IFN-γ, CRP and low CD4 count correlated with disease severity and short-term mortality in HIV-infected Zambian adults with diarrhoea. These factors were better predictors of survival than BMI. Understanding the cause of TNFR p55, IFN-γ and CRP elevation may be useful in development of interventions to reduce mortality in AIDS patients with chronic diarrhoea in Africa. PMID:19014537

Interaction between Helicobacter pylori and Latent Toxoplasmosis and Demographic Variables on Cognitive Function in Young to Middle-Aged Adults

PubMed Central

Gale, Shawn D.; Erickson, Lance D.; Brown, Bruce L.; Hedges, Dawson W.

2015-01-01

Helicobacter pylori and latent toxoplasmosis are widespread diseases that have been associated with cognitive deficits and Alzheimer’s disease. We sought to determine whether interactions between Helicobacter pylori and latent toxoplasmosis, age, race-ethnicity, educational attainment, economic status, and general health predict cognitive function in young and middle-aged adults. To do so, we used multivariable regression and multivariate models to analyze data obtained from the United States’ National Health and Nutrition Examination Survey from the Centers for Disease Control and Prevention, which can be weighted to represent the US population. In this sample, we found that 31.6 percent of women and 36.2 percent of men of the overall sample had IgG Antibodies against Helicobacter pylori, although the seroprevalence of Helicobacter pylori varied with sociodemographic variables. There were no main effects for Helicobacter pylori or latent toxoplasmosis for any of the cognitive measures in models adjusting for age, sex, race-ethnicity, educational attainment, economic standing, and self-rated health predicting cognitive function. However, interactions between Helicobacter pylori and race-ethnicity, educational attainment, latent toxoplasmosis in the fully adjusted models predicted cognitive function. People seropositive for both Helicobacter pylori and latent toxoplasmosis – both of which appear to be common in the general population – appear to be more susceptible to cognitive deficits than are people seropositive for either Helicobacter pylori and or latent toxoplasmosis alone, suggesting a synergistic effect between these two infectious diseases on cognition in young to middle-aged adults. PMID:25590622

Risk Factors and Predictors for Candidemia in Pediatric Intensive Care Unit Patients: Implications for Prevention

PubMed Central

Zaoutis, Theoklis E.; Prasad, Priya A.; Localio, A. Russell; Coffin, Susan E.; Bell, Louis M.; Walsh, Thomas J.; Gross, Robert

2013-01-01

Summary Few data exist on risk factors for candidemia in pediatric intensive care unit (PICU) patients who are at high risk of mortality from infection. We conducted a population-based case-control study to determine risk factors and predictors for candidemia in the PICU. Background Candida species are the leading cause of invasive fungal infections in hospitalized children and are the third most common isolates recovered from pediatric healthcare-associated bloodstream infection in the US [1]. Few data exist on risk factors for candidemia in pediatric intensive care unit (PICU) patients. Methods We conducted a population-based case-control study of PICU patients at Children's Hospital of Philadelphia (CHOP) from 1997-2004. Cases were identified using laboratory records, controls were selected from PICU rosters. Controls were matched to cases by incidence density sampling, adjusting for time at risk. Following conditional multivariate analysis, we performed weighted multivariate analysis to determine predicted probabilities for candidemia given certain risk factor combinations. Results We identified 101 cases of candidemia(incidence,3.5/1,000 PICU admissions). Factors independently associated with candidemia included presence of a central venous catheter(OR 30.4;CI,7.7,119.5), malignancy(OR 4.0;CI,1.23,13.1), use of vancomycin for >3 days in the prior two weeks(OR 6.2;CI,2.4,16), and receipt of agents with activity against anaerobic organisms for >3 days in the prior two weeks(OR 3.5;CI, 1.5,8.4). Predicted probability of various combinations of the factors above ranged from 10.7%-46%. The 30-day mortality rate was 44% in cases compared to 14% in controls (OR 4.22;CI,2.35,7.60). Conclusions To our knowledge, this is the first study to evaluate independent risk factors and to determine a population of children in PICUs at high risk for developing candidemia. Future efforts should focus on validation of these risk factors identified in a different PICU population and development of interventions for prevention of candidemia in critically ill children. PMID:20636126

Comparing theories' performance in predicting violence.

PubMed

Haas, Henriette; Cusson, Maurice

2015-01-01

The stakes of choosing the best theory as a basis for violence prevention and offender rehabilitation are high. However, no single theory of violence has ever been universally accepted by a majority of established researchers. Psychiatry, psychology and sociology are each subdivided into different schools relying upon different premises. All theories can produce empirical evidence for their validity, some of them stating the opposite of each other. Calculating different models with multivariate logistic regression on a dataset of N = 21,312 observations and ninety-two influences allowed a direct comparison of the performance of operationalizations of some of the most important schools. The psychopathology model ranked as the best model in terms of predicting violence right after the comprehensive interdisciplinary model. Next came the rational choice and lifestyle model and third the differential association and learning theory model. Other models namely the control theory model, the childhood-trauma model and the social conflict and reaction model turned out to have low sensitivities for predicting violence. Nevertheless, all models produced acceptable results in predictions of a non-violent outcome. Copyright © 2015. Published by Elsevier Ltd.

Resilience linked to personality dimensions, alexithymia and affective symptoms in motor functional neurological disorders.

PubMed

Jalilianhasanpour, Rozita; Williams, Benjamin; Gilman, Isabelle; Burke, Matthew J; Glass, Sean; Fricchione, Gregory L; Keshavan, Matcheri S; LaFrance, W Curt; Perez, David L

2018-04-01

Reduced resilience, a construct associated with maladaptive stress coping and a predisposing vulnerability for Functional Neurological Disorders (FND), has been under-studied compared to other neuropsychiatric factors in FND. This prospective case-control study investigated self-reported resilience in patients with FND compared to controls and examined relationships between resilience and affective symptoms, personality traits, alexithymia, health status and adverse life event burden. 50 individuals with motor FND and 47 healthy controls participated. A univariate test followed by a logistic regression analysis investigated group-level differences in Connor-Davidson Resilience Scale (CD-RISC) scores. For within-group analyses performed separately in patients with FND and controls, univariate screening tests followed by multivariate linear regression analyses examined factors associated with self-reported resilience. Adjusting for age, gender, education status, ethnicity and lifetime adverse event burden, patients with FND reported reduced resilience compared to controls. Within-group analyses in patients with FND showed that individual-differences in mental health, extraversion, conscientiousness, and openness positively correlated with CD-RISC scores; post-traumatic stress disorder symptom severity, depression, anxiety, alexithymia and neuroticism scores negatively correlated with CD-RISC scores. Extraversion independently predicted resilience scores in patients with FND. In control subjects, univariate associations were appreciated between CD-RISC scores and gender, personality traits, anxiety, alexithymia and physical health; conscientiousness independently predicted resilience in controls. Patients with FND reported reduced resilience, and CD-RISC scores covaried with other important predisposing vulnerabilities for the development of FND. Future research should investigate if the CD-RISC is predictive of clinical outcomes in patients with FND. Copyright © 2018 Elsevier Inc. All rights reserved.

Cognitive correlates of serious suicidal ideation in a community sample of adolescents.

PubMed

Labelle, Réal; Breton, Jean-Jacques; Pouliot, Louise; Dufresne, Marie-Josée; Berthiaume, Claude

2013-03-05

Studies indicate that a dysfunctional attributional style, problem-solving deficits and hopelessness place youths at risk of developing suicidal thoughts and engaging in suicidal behaviour. However, in the realm of suicidality in adolescent, no study has examined the linkages between these three cognitive variables and suicidal ideation in non-clinical samples while taking into account the moderating role of gender on the relationships and controlling for depression. In this community study of 712 adolescents 14-18 years of age, through a multivariate approach, the interaction between the cognitive variables, depression and gender was examined with depression controlled in the analyses. Problem-solving deficits and hopelessness proved predictive of such ideation whether or not depressive symptoms were controlled in the analyses. Negative problem orientation/avoidant style was more predictive of ideation in boys than in girls. On the other hand, hopelessness was more predictive for girls than boys. Results were based on a convenience community sample of adolescents and a cross-sectional survey. Results suggest that a unique explanatory model of the suicide process in adolescence that fails to take account of gender would be ill informed. Suicide prevention strategies should be differentiated according to gender with a stronger emphasis in hopelessness in female adolescents, and problem-solving deficits in male adolescents. Copyright © 2012 Elsevier B.V. All rights reserved.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported.

PubMed

Whittle, Rebecca; Peat, George; Belcher, John; Collins, Gary S; Riley, Richard D

2018-05-18

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use. A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risk. Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorised as high risk of error, however this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured. Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions. Copyright © 2018. Published by Elsevier Inc.

Opportunities of probabilistic flood loss models

NASA Astrophysics Data System (ADS)

Schröter, Kai; Kreibich, Heidi; Lüdtke, Stefan; Vogel, Kristin; Merz, Bruno

2016-04-01

Oftentimes, traditional uni-variate damage models as for instance depth-damage curves fail to reproduce the variability of observed flood damage. However, reliable flood damage models are a prerequisite for the practical usefulness of the model results. Innovative multi-variate probabilistic modelling approaches are promising to capture and quantify the uncertainty involved and thus to improve the basis for decision making. In this study we compare the predictive capability of two probabilistic modelling approaches, namely Bagging Decision Trees and Bayesian Networks and traditional stage damage functions. For model evaluation we use empirical damage data which are available from computer aided telephone interviews that were respectively compiled after the floods in 2002, 2005, 2006 and 2013 in the Elbe and Danube catchments in Germany. We carry out a split sample test by sub-setting the damage records. One sub-set is used to derive the models and the remaining records are used to evaluate the predictive performance of the model. Further we stratify the sample according to catchments which allows studying model performance in a spatial transfer context. Flood damage estimation is carried out on the scale of the individual buildings in terms of relative damage. The predictive performance of the models is assessed in terms of systematic deviations (mean bias), precision (mean absolute error) as well as in terms of sharpness of the predictions the reliability which is represented by the proportion of the number of observations that fall within the 95-quantile and 5-quantile predictive interval. The comparison of the uni-variable Stage damage function and the multivariable model approach emphasises the importance to quantify predictive uncertainty. With each explanatory variable, the multi-variable model reveals an additional source of uncertainty. However, the predictive performance in terms of precision (mbe), accuracy (mae) and reliability (HR) is clearly improved in comparison to uni-variable Stage damage function. Overall, Probabilistic models provide quantitative information about prediction uncertainty which is crucial to assess the reliability of model predictions and improves the usefulness of model results.

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

PubMed

Sepehrband, Farshid; Lynch, Kirsten M; Cabeen, Ryan P; Gonzalez-Zacarias, Clio; Zhao, Lu; D'Arcy, Mike; Kesselman, Carl; Herting, Megan M; Dinov, Ivo D; Toga, Arthur W; Clark, Kristi A

2018-05-15

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases). Copyright © 2018 Elsevier Inc. All rights reserved.

Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

NASA Astrophysics Data System (ADS)

Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

2014-12-01

Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability

NASA Astrophysics Data System (ADS)

Liu, Sijun; Chen, Jiaping; Wang, Jianming; Wu, Zhuchao; Wu, Weihua; Xu, Zhiwei; Hu, Wenbiao; Xu, Fei; Tong, Shilu; Shen, Hongbing

2017-10-01

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.

Severity of CIND and MCI predict incidence of dementia in an ischemic stroke cohort

PubMed Central

Narasimhalu, K; Ang, S; De Silva, D A.; Wong, M -C.; Chang, H -M.; Chia, K -S.; Auchus, A P.; Chen, C

2009-01-01

Background: The utility of poststroke cognitive status, namely dementia, cognitive impairment no dementia (CIND), mild cognitive impairment (MCI), and no cognitive impairment (NCI), in predicting dementia has been previously examined. However, no studies to date have compared the ability of subtypes of MCI and CIND to predict dementia in a poststroke population. Methods: A cohort of ischemic stroke patients underwent neuropsychological assessment annually for up to 5 years. Dementia was defined using the DSM-IV criteria. Univariate and multivariable Cox proportional regression was performed to determine the ability of MCI subtypes, CIND severity, and individual domains of impairment to predict dementia. Results: A total of 362 patients without dementia were followed up for a mean of 3.4 years (17% drop out), with 24 developing incident dementia. Older age, previous and recurrent stroke, and CIND and MCI subtypes were significant predictors of dementia. In multivariable analysis controlling for treatment allocation, patients who were older, had previous or recurrent stroke, and had either CIND moderate or multiple domain MCI with amnestic component were at elevated risk for dementia. In multivariable domain analysis, recurrent strokes, age, and previous strokes, verbal memory, and visual memory were significant predictors of dementia. Receiver operating characteristic curve analysis showed that CIND moderate (area under the curve: 0.893) and multiple domain MCI with amnestic component (area under the curve: 0.832) were significant predictors of conversion to dementia. All other classifications of cognitive impairment had areas under the curve less than 0.7. Conclusion: Stroke patients with cognitive impairment no dementia (CIND) moderate are at higher risk of developing dementia, while CIND mild patients are not at increased risk of developing dementia. GLOSSARY AD = Alzheimer disease; AUC = area under the curve; CI = confidence interval; CIND = cognitive impairment no dementia; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders, 4th edition; ESPRIT = European Australasian Stroke Prevention in Reversible Ischemia Trial; ESPRIT-Cog = European Australasian Stroke Prevention in Reversible Ischemia Trial, cognitive substudy; HR = hazard ratio; LACI = lacunar infarct; MCI = mild cognitive impairment; mRS = modified Rankin scale; NCI = no cognitive impairment; OCSP = Oxfordshire Community Stroke Project; PACI = partial anterior circulation infarct; POCI = posterior circulation infarct; ROC = receiver operating curve; TACI = total anterior circulation infarct; VaD = vascular dementia; WAIS-R = Wechsler Adult Intelligence Scale–Revised; WMS-R = Wechsler Memory Scale–Revised. PMID:19949033

Choosing to regulate: does choice enhance craving regulation?

PubMed Central

Mobasser, Arian; Zeithamova, Dagmar; Pfeifer, Jennifer H

2018-01-01

Abstract Goal-directed behavior and lifelong well-being often depend on the ability to control appetitive motivations, such as cravings. Cognitive reappraisal is an effective way to modulate emotional states, including cravings, but is often studied under explicit instruction to regulate. Despite the strong prediction from Self-Determination Theory that choice should enhance task engagement and regulation success, little is known empirically about whether and how regulation is different when participants choose (vs are told) to exert control. To investigate how choice affects neural activity and regulation success, participants reappraised their responses to images of personally-craved foods while undergoing functional neuroimaging. Participants were either instructed to view or reappraise (‘no-choice’) or chose freely to view or reappraise (‘yes-choice’). Choice increased activity in the frontoparietal control network. We expected this activity would be associated with increased task engagement, resulting in better regulation success. However, contrary to this prediction, choice slightly reduced regulation success. Follow-up multivariate functional neuroimaging analyses indicated that choice likely disrupted allocation of limited cognitive resources during reappraisal. While unexpected, these results highlight the importance of studying upstream processes such as regulation choice, as they may affect the ability to regulate cravings and other emotional states. PMID:29462475

Age, gender and tumour size predict work capacity after surgical treatment of vestibular schwannomas.

PubMed

Al-Shudifat, Abdul Rahman; Kahlon, Babar; Höglund, Peter; Soliman, Ahmed Y; Lindskog, Kristoffer; Siesjo, Peter

2014-01-01

The aim of the present study was to identify predictive factors for outcome after surgery of vestibular schwannomas. This is a retrospective study with partially collected prospective data of patients who were surgically treated for vestibular schwannomas at a single institution from 1979 to 2000. Patients with recurrent tumours, NF2 and those incapable of answering questionnaires were excluded from the study. The short form 36 (SF36) questionnaire and a specific questionnaire regarding neurological status, work status and independent life (IL) status were sent to all eligible patients. The questionnaires were sent to 430 eligible patients (out of 537) and 395 (93%) responded. Scores for work capacity (WC) and IL were compared with SF36 scores as outcome estimates. Patients were divided into two groups (<64, ≥64-years-old) in order to assess them for either WC or IL. Putative preoperative and postoperative predictive factors were tested in univariate and multivariable regression analysis for the outcome scores of WC, IL and SF36. In the group <64 years, age, gender and tumour diameter were independent predictive factors for postoperative WC in multivariate analysis. A high-risk group was identified in women with age >50 years and tumour diameter >25 mm. In patients ≥64, gender and tumour diameter were significant predictive factors for IL in univariate analysis. Perioperative and postoperative objective factors as length of surgery, blood loss and complications did not predict outcome in the multivariable analysis for any age group. Patients' assessment of change in balance function was the only neurological factor that showed significance both in univariate and multivariable analysis in both age cohorts. While SF36 scores were lower in surgically treated patients in relation to normograms for the general population, they did not correlate significantly to WC and IL. The SF36 questionnaire did not correlate to outcome measures as WC and IL in patients undergoing surgery for vestibular schwannomas. Women and patients above 50 years with larger tumours have a high risk for reduced WC after surgical treatment. These results question the validity of quality of life scores in assessment of outcome after surgery of benign skullbase lesions.

Worry and cognitive control predict course trajectories of anxiety in older adults with late-life depression.

PubMed

Spinhoven, P; van der Veen, D C; Voshaar, R C Oude; Comijs, H C

2017-07-01

Many older adults with depressive disorder manifest anxious distress. This longitudinal study examines the predictive value of worry as a maladaptive cognitive emotion regulation strategy, and resources necessary for successful emotion regulation (i.e., cognitive control and resting heart rate variability [HRV]) for the course of anxiety symptoms in depressed older adults. Moreover, it examines whether these emotion regulation variables moderate the impact of negative life events on severity of anxiety symptoms. Data of 378 depressed older adults (CIDI) between 60 and 93 years (of whom 144 [41%] had a comorbid anxiety disorder) from the Netherlands Study of Depression in Older Adults (NESDO) were used. Latent Growth Mixture Modeling was used to identify different course trajectories of six-months BAI scores. Univariable and multivariable longitudinal associations of worry, cognitive control and HRV with symptom course trajectories were assessed. We identified a course trajectory with low and improving symptoms (57.9%), a course trajectory with moderate and persistent symptoms (33.5%), and a course trajectory with severe and persistent anxiety symptoms (8.6%). Higher levels of worry and lower levels of cognitive control predicted persistent and severe levels of anxiety symptoms independent of presence of anxiety disorder. However, worry, cognitive control and HRV did not moderate the impact of negative life events on anxiety severity. Worry may be an important and malleable risk factor for persistence of anxiety symptoms in depressed older adults. Given the high prevalence of anxious depression in older adults, modifying worry may constitute a viable venue for alleviating anxiety levels. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-06-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant cha...

Copula-based prediction of economic movements

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Hirsh, I. D.

2016-06-01

In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

Design, evaluation and test of an electronic, multivariable control for the F100 turbofan engine

NASA Technical Reports Server (NTRS)

Skira, C. A.; Dehoff, R. L.; Hall, W. E., Jr.

1980-01-01

A digital, multivariable control design procedure for the F100 turbofan engine is described. The controller is based on locally linear synthesis techniques using linear, quadratic regulator design methods. The control structure uses an explicit model reference form with proportional and integral feedback near a nominal trajectory. Modeling issues, design procedures for the control law and the estimation of poorly measured variables are presented.

Serum Adiponectin, Vitamin D, and Alpha-Fetoprotein in Children with Chronic Hepatitis C: Can They Predict Treatment Response?

PubMed Central

Khedr, Mohamed Ahmed; Sira, Ahmad Mohamed; Saber, Magdy Anwar; Raia, Gamal Yousef

2015-01-01

Background & Aims. The currently available treatment for chronic hepatitis C (CHC) in children is costly and with much toxicity. So, predicting the likelihood of response before starting therapy is important. Methods. Serum adiponectin, vitamin D, and alpha-fetoprotein (AFP) were measured before starting pegylated-interferon/ribavirin therapy for 50 children with CHC. Another 21 healthy children were recruited as controls. Results. Serum adiponectin, vitamin D, and AFP were higher in the CHC group than healthy controls (p < 0.0001, p = 0.071, and p = 0.87, resp.). In univariate analysis, serum adiponectin was significantly higher in responders than nonresponders (p < 0.0001) and at a cutoff value ≥8.04 ng/mL it can predict treatment response by 77.8% sensitivity and 92.9% specificity, while both AFP and viremia were significantly lower in responders than nonresponders, p < 0.0001 and p = 0.0003, respectively, and at cutoff values ≤3.265 ng/mL and ≤235,384 IU/mL, respectively, they can predict treatment response with a sensitivity of 83.3% for both and specificity of 85.7% and 78.6%, respectively. In multivariate analysis, adiponectin was found to be the only independent predictor of treatment response (p = 0.044). Conclusions. The pretreatment serum level of adiponectin can predict the likelihood of treatment response, thus avoiding toxicities for those unlikely to respond to therapy. PMID:26640716

Serum Adiponectin, Vitamin D, and Alpha-Fetoprotein in Children with Chronic Hepatitis C: Can They Predict Treatment Response?

PubMed

Khedr, Mohamed Ahmed; Sira, Ahmad Mohamed; Saber, Magdy Anwar; Raia, Gamal Yousef

2015-01-01

Background & Aims. The currently available treatment for chronic hepatitis C (CHC) in children is costly and with much toxicity. So, predicting the likelihood of response before starting therapy is important. Methods. Serum adiponectin, vitamin D, and alpha-fetoprotein (AFP) were measured before starting pegylated-interferon/ribavirin therapy for 50 children with CHC. Another 21 healthy children were recruited as controls. Results. Serum adiponectin, vitamin D, and AFP were higher in the CHC group than healthy controls (p < 0.0001, p = 0.071, and p = 0.87, resp.). In univariate analysis, serum adiponectin was significantly higher in responders than nonresponders (p < 0.0001) and at a cutoff value ≥8.04 ng/mL it can predict treatment response by 77.8% sensitivity and 92.9% specificity, while both AFP and viremia were significantly lower in responders than nonresponders, p < 0.0001 and p = 0.0003, respectively, and at cutoff values ≤3.265 ng/mL and ≤235,384 IU/mL, respectively, they can predict treatment response with a sensitivity of 83.3% for both and specificity of 85.7% and 78.6%, respectively. In multivariate analysis, adiponectin was found to be the only independent predictor of treatment response (p = 0.044). Conclusions. The pretreatment serum level of adiponectin can predict the likelihood of treatment response, thus avoiding toxicities for those unlikely to respond to therapy.

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

PubMed

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

2011-01-01

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

Preoperative Short Form Health Survey Score Is Predictive of Return to Play and Minimal Clinically Important Difference at a Minimum 2-Year Follow-up After Anterior Cruciate Ligament Reconstruction.

PubMed

Nwachukwu, Benedict U; Chang, Brenda; Voleti, Pramod B; Berkanish, Patricia; Cohn, Matthew R; Altchek, David W; Allen, Answorth A; Williams, Riley J

2017-10-01

There is increased interest in understanding the preoperative determinants of postoperative outcomes. Return to play (RTP) and the patient-reported minimal clinically important difference (MCID) are useful measures of postoperative outcomes after anterior cruciate ligament reconstruction (ACLR). To define the MCID after ACLR and to investigate the role of preoperative outcome scores for predicting the MCID and RTP after ACLR. Case-control study; Level of evidence, 3. There were 294 active athletes enrolled as part of an institutional ACL registry with a minimum 2-year follow-up who were eligible for inclusion. A questionnaire was administered to elicit factors associated with RTP. Patient demographic and clinical data as well as patient-reported outcome measures were captured as part of the registry. Outcome measures included the International Knee Documentation Committee (IKDC) subjective knee evaluation form, Lysholm scale, and 12-Item Short Form Health Survey (SF-12) physical component summary (PCS) and mental component summary (MCS). Preoperative outcome score thresholds predictive of RTP were determined using a receiver operating characteristic (ROC) with area under the curve (AUC) analysis. The MCID was calculated using a distribution-based method. Multivariable logistic models were fitted to identify predictors for achieving the MCID and RTP. At a mean (±SD) follow-up of 3.7 ± 0.7 years, 231 patients were included from a total 294 eligible patients. The mean age and body mass index were 26.7 ± 12.5 years and 23.7 ± 3.2 kg/m 2 , respectively. Of the 231 patients, 201 (87.0%) returned to play at a mean time of 10.1 months. Two-year postoperative scores on all measures were significantly increased from preoperative scores (IKDC: 50.1 ± 15.6 to 87.4 ± 10.7; Lysholm: 61.2 ± 18.1 to 89.5 ± 10.4; SF-12 PCS: 41.5 ± 9.0 to 54.7 ± 4.6; SF-12 MCS: 53.6 ± 8.1 to 55.7 ± 5.7; P < .001 for all). The corresponding MCID values were 9.0 (IKDC), 10.0 (Lysholm), 5.1 (SF-12 PCS), and 4.3 (SF-12 MCS). Preoperative score thresholds predictive of RTP were the following: IKDC, 60.9; Lysholm, 57.0; SF-12 PCS, 42.3; and SF-12 MCS, 48.3. These thresholds were not independently predictive but achieved significance as part of the multivariable analysis. In the multivariable analysis for RTP, preoperative SF-12 PCS scores above 42.3 (odds ratio [OR], 2.73; 95% CI, 1.09-7.62) and SF-12 MCS scores above 48.3 (OR, 4.41; 95% CI, 1.80-10.98) were predictive for achieving RTP; an ACL allograft (OR, 0.26; 95% CI, 0.06-1.00) was negatively predictive of RTP. In the multivariable analysis for the MCID, patients with higher preoperative scores were less likely to achieve the MCID ( P < .0001); however, a higher preoperative SF-12 MCS score was predictive of achieving the MCID on the IKDC form (OR, 1.27; 95% CI, 1.11-1.52) and Lysholm scale (OR, 1.08; 95% CI, 1.00-1.16). Medial meniscal injuries, older age, and white race were also associated with a decreased likelihood for achieving the MCID. Preoperative SF-12 MCS and PCS scores were predictive of RTP after ACLR; patients scoring above 42.3 on the SF-12 PCS and 48.3 on the SF-12 MCS were more likely to achieve RTP. Additionally, we defined the MCID after ACLR and found that higher SF-12 MCS scores were predictive of achieving the MCID on knee-specific questionnaires.

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

PubMed

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

2018-06-01

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

Unchanged Levels of Soluble CD14 and IL-6 Over Time Predict Serious Non-AIDS Events in HIV-1-Infected People

PubMed Central

Sunil, Meena; Nigalye, Maitreyee; Somasunderam, Anoma; Martinez, Maria Laura; Yu, Xiaoying; Arduino, Roberto C.; Bell, Tanvir K.

2016-01-01

Abstract HIV-1-infected persons have increased risk of serious non-AIDS events (SNAEs) despite suppressive antiretroviral therapy. Increased circulating levels of soluble CD14 (sCD14), soluble CD163 (sCD163), and interleukin-6 (IL-6) at a single time point have been associated with SNAEs. However, whether changes in these biomarker levels predict SNAEs in HIV-1-infected persons is unknown. We hypothesized that greater decreases in inflammatory biomarkers would be associated with fewer SNAEs. We identified 39 patients with SNAEs, including major cardiovascular events, end stage renal disease, decompensated cirrhosis, non-AIDS-defining malignancies, and death of unknown cause, and age- and sex-matched HIV-1-infected controls. sCD14, sCD163, and IL-6 were measured at study enrollment (T1) and proximal to the event (T2) or equivalent duration in matched controls. Over ∼34 months, unchanged rather than decreasing levels of sCD14 and IL-6 predicted SNAEs. Older age and current illicit substance abuse, but not HCV coinfection, were associated with SNAEs. In a multivariate analysis, older age, illicit substance use, and unchanged IL-6 levels remained significantly associated with SNAEs. Thus, the trajectories of sCD14 and IL-6 levels predict SNAEs. Interventions to decrease illicit substance use may decrease the risk of SNAEs in HIV-1-infected persons. PMID:27344921

Extra-hippocampal subcortical limbic involvement predicts episodic recall performance in multiple sclerosis.

PubMed

Dineen, Robert A; Bradshaw, Christopher M; Constantinescu, Cris S; Auer, Dorothee P

2012-01-01

Episodic memory impairment is a common but poorly-understood phenomenon in multiple sclerosis (MS). We aim to establish the relative contributions of reduced integrity of components of the extended hippocampal-diencephalic system to memory performance in MS patients using quantitative neuroimaging. 34 patients with relapsing-remitting MS and 24 healthy age-matched controls underwent 3 T MRI including diffusion tensor imaging and 3-D T1-weighted volume acquisition. Manual fornix regions-of-interest were used to derive fornix fractional anisotropy (FA). Normalized hippocampal, mammillary body and thalamic volumes were derived by manual segmentation. MS subjects underwent visual recall, verbal recall, verbal recognition and verbal fluency assessment. Significant differences between MS patients and controls were found for fornix FA (0.38 vs. 0.46, means adjusted for age and fornix volume, P<.0005) and mammillary body volumes (age-adjusted means 0.114 ml vs. 0.126 ml, P<.023). Multivariate regression analysis identified fornix FA and mammillary bodies as predictor of visual recall (R(2) = .31, P = .003, P = .006), and thalamic volume as predictive of verbal recall (R(2) = .37, P<.0005). No limbic measures predicted verbal recognition or verbal fluency. These findings indicate that structural and ultrastructural alterations in subcortical limbic components beyond the hippocampus predict performance of episodic recall in MS patients with mild memory dysfunction.

Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples.

PubMed

Doan, Nhat Trung; Engvig, Andreas; Zaske, Krystal; Persson, Karin; Lund, Martina Jonette; Kaufmann, Tobias; Cordova-Palomera, Aldo; Alnæs, Dag; Moberget, Torgeir; Brækhus, Anne; Barca, Maria Lage; Nordvik, Jan Egil; Engedal, Knut; Agartz, Ingrid; Selbæk, Geir; Andreassen, Ole A; Westlye, Lars T

2017-09-01

Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis. Using the lasso classifier, we performed group classification and prediction of cognition and age at different age ranges to assess the sensitivity and diagnostic accuracy of the LICA patterns in relation to AD, as well as early and late healthy aging. Three components showed high sensitivity to the diagnosis and cognitive status of AD, with different relationships with age: one reflected an anterior-posterior gradient in thickness and gray matter density and was uniquely related to diagnosis, whereas the other two, reflecting widespread cortical thickness and medial temporal lobe volume, respectively, also correlated significantly with age. Repeating the LICA decomposition and between-subject analysis on ADNI data, including 186 AD, 395 MCI and 220 age-matched healthy controls, revealed largely consistent brain patterns and clinical associations across samples. Classification results showed that multivariate LICA-derived brain characteristics could be used to predict AD and age with high accuracy (area under ROC curve up to 0.93 for classification of AD from controls). Comparison between classifiers based on feature ranking and feature selection suggests both common and unique feature sets implicated in AD and aging, and provides evidence of distinct age-related differences in early compared to late aging. Copyright © 2017 Elsevier Inc. All rights reserved.

Why do some studies find that CPR fraction is not a predictor of survival?

PubMed

Wik, Lars; Olsen, Jan-Aage; Persse, David; Sterz, Fritz; Lozano, Michael; Brouwer, Marc A; Westfall, Mark; Souders, Chris M; Travis, David T; Herken, Ulrich R; Lerner, E Brooke

2016-07-01

An 80% chest compression fraction (CCF) during resuscitation is recommended. However, heterogeneous results in CCF studies were found during the 2015 Consensus on Science (CoS), which may be because chest compressions are stopped for a wide variety of reasons including providing lifesaving care, provider distraction, fatigue, confusion, and inability to perform lifesaving skills efficiently. The effect of confounding variables on CCF to predict cardiac arrest survival. A secondary analysis of emergency medical services (EMS) treated out-of-hospital cardiac arrest (OHCA) patients who received manual compressions. CCF (percent of time patients received compressions) was determined from electronic defibrillator files. Two Sample Wilcoxon Rank Sum or regression determined a statistical association between CCF and age, gender, bystander CPR, public location, witnessed arrest, shockable rhythm, resuscitation duration, study site, and number of shocks. Univariate and multivariate logistic regressions were used to determine CCF effect on survival. Of 2132 patients with manual compressions 1997 had complete data. Shockable rhythm (p<0.001), public location (p<0.004), treatment duration (p<0.001), and number of shocks (p<0.001) were associated with lower CCF. Univariate logistic regression found that CCF was inversely associated with survival (OR 0.07; 95% CI 0.01-0.36). Multivariate regression controlling for factors associated with survival and/or CCF found that increasing CCF was associated with survival (OR 6.34; 95% CI 1.02-39.5). CCF cannot be looked at in isolation as a predictor of survival, but in the context of other resuscitation activities. When controlling for the effects of other resuscitation activities, a higher CCF is predictive of survival. This may explain the heterogeneity of findings during the CoS review. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Investigating consummatory and anticipatory pleasure across motivation deficits in schizophrenia and healthy controls.

PubMed

Da Silva, Susana; Saperia, Sarah; Siddiqui, Ishraq; Fervaha, Gagan; Agid, Ofer; Daskalakis, Z Jeff; Ravindran, Arun; Voineskos, Aristotle N; Zakzanis, Konstantine K; Remington, Gary; Foussias, George

2017-08-01

Anhedonia has traditionally been considered a characteristic feature of schizophrenia, but the true nature of this deficit remains elusive. This study sought to investigate consummatory and anticipatory pleasure as it relates to motivation deficits. Eighty-four outpatients with schizophrenia and 81 healthy controls were administered the Temporal Experience of Pleasure Scale (TEPS), as well as a battery of clinical and cognitive assessments. Multivariate analyses of variance were used to examine the experience of pleasure as a function of diagnosis, and across levels of motivation deficits (i.e. low vs. moderate. vs. high) in schizophrenia. Hierarchical regression analyses were also conducted to evaluate the predictive value of amotivation in relation to the TEPS. There were no significant differences between schizophrenia and healthy control groups for either consummatory or anticipatory pleasure. Within the schizophrenia patients, only those with high levels of amotivation were significantly impaired in consummatory and anticipatory pleasure compared to low and moderate groups, and compared to healthy controls. Further, our results revealed that amotivation significantly predicts both consummatory and anticipatory pleasure, with no independent contribution of group. Utilizing study samples with a wide range of motivation deficits and incorporating objective paradigms may provide a more comprehensive understanding of hedonic deficits. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Application of the MNA design method to a nonlinear turbofan engine. [multivariable Nyquist array method

NASA Technical Reports Server (NTRS)

Leininger, G. G.

1981-01-01

Using nonlinear digital simulation as a representative model of the dynamic operation of the QCSEE turbofan engine, a feedback control system is designed by variable frequency design techniques. Transfer functions are generated for each of five power level settings covering the range of operation from approach power to full throttle (62.5% to 100% full power). These transfer functions are then used by an interactive control system design synthesis program to provide a closed loop feedback control using the multivariable Nyquist array and extensions to multivariable Bode diagrams and Nichols charts.

Cerebellar contribution to motor and cognitive performance in multiple sclerosis: An MRI sub-regional volumetric analysis.

PubMed

D'Ambrosio, Alessandro; Pagani, Elisabetta; Riccitelli, Gianna C; Colombo, Bruno; Rodegher, Mariaemma; Falini, Andrea; Comi, Giancarlo; Filippi, Massimo; Rocca, Maria A

2017-08-01

To investigate the role of cerebellar sub-regions on motor and cognitive performance in multiple sclerosis (MS) patients. Whole and sub-regional cerebellar volumes, brain volumes, T2 hyperintense lesion volumes (LV), and motor performance scores were obtained from 95 relapse-onset MS patients and 32 healthy controls (HC). MS patients also underwent an evaluation of working memory and processing speed functions. Cerebellar anterior and posterior lobes were segmented using the Spatially Unbiased Infratentorial Toolbox (SUIT) from Statistical Parametric Mapping (SPM12). Multivariate linear regression models assessed the relationship between magnetic resonance imaging (MRI) measures and motor/cognitive scores. Compared to HC, only secondary progressive multiple sclerosis (SPMS) patients had lower cerebellar volumes (total and posterior cerebellum). In MS patients, lower anterior cerebellar volume and brain T2 LV predicted worse motor performance, whereas lower posterior cerebellar volume and brain T2 LV predicted poor cognitive performance. Global measures of brain volume and infratentorial T2 LV were not selected by the final multivariate models. Cerebellar volumetric abnormalities are likely to play an important contribution to explain motor and cognitive performance in MS patients. Consistently with functional mapping studies, cerebellar posterior-inferior volume accounted for variance in cognitive measures, whereas anterior cerebellar volume accounted for variance in motor performance, supporting the assessment of cerebellar damage at sub-regional level.

Expression of miR-146a-5p in patients with intracranial aneurysms and its association with prognosis.

PubMed

Zhang, H-L; Li, L; Cheng, C-J; Sun, X-C

2018-02-01

The study aims to detect the association of miR-146a-5p with intracranial aneurysms (IAs). The expression of miR-146a-5p was compared from plasma samples between 72 patients with intracranial aneurysms (IAs) and 40 healthy volunteers by quantitative Real-time polymerase chain reaction (qRT-PCR). Statistical analysis was performed to analyze the relationship between miR-146a-5p expression and clinical data and overall survival (OS) time of IAs patients. Univariate and multivariate Cox proportional hazards have also been performed. Notably, higher miR-146a-5p expression was found in plasma samples from 72 patients with intracranial aneurysms (IAs) compared with 40 healthy controls. Higher miR-146a-5p expression was significantly associated with rupture and Hunt-Hess level in IAs patients. Kaplan-Meier survival analysis verified that higher miR-146a-5p expression predicted a shorter overall survival (OS) compared with lower miR-146a-5p expression in IAs patients. Univariate and multivariate Cox proportional hazards demonstrated that higher miR-146a-5p expression, rupture, and Hunt-Hess were independent risk factors of OS in patients with intracranial aneurysms (IAs). MiR-146a-5p expression may serve as a biomarker for predicting prognosis in patients with IAs.

Closed Loop System Identification with Genetic Algorithms

NASA Technical Reports Server (NTRS)

Whorton, Mark S.

2004-01-01

High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.

Using Performance Data Gathered at Several Stages of Achievement in Predicting Subsequent Performance.

ERIC Educational Resources Information Center

Owen, Steven V.; Feldhusen, John F.

This study compares the effectiveness of three models of multivariate prediction for academic success in identifying the criterion variance of achievement in nursing education. The first model involves the use of an optimum set of predictors and one equation derived from a regression analysis on first semester grade average in predicting the…

Use of preoperative CHA2 DS2 -VASc score to predict the risk of atrial fibrillation after cardiothoracic surgery: a nested case-control study from the Atrial Fibrillation Suppression Trials (AFIST) I, II, and III.

PubMed

Baker, William L; Coleman, Craig I; White, C Michael; Kluger, Jeffrey

2013-05-01

To evaluate whether the preoperative CHA2 DS2 -VASc score predicts the risk of atrial fibrillation (AF) after cardiothoracic surgery (CTS). Retrospective, nested case-control study. A total of 560 patients undergoing coronary artery bypass grafting and/or valvular surgery from the Atrial Fibrillation Suppression Trials I, II, and III. All variables showing a univariate association (p≤0.20) with AF occurrence were entered into a backward stepwise multivariate logistic regression analysis to control for potential confounders and to calculate adjusted odds ratios (AORs) with 95% confidence intervals (CIs). The population was age 67.8 ± 8.6 (mean ± SD) years and 77.1% male, with CHA2 DS2 -VASc scores of 0-1 (low) in 34 patients (6.1%), 2-3 (medium) in 261 patients (46.6%), and more than 3 (high) in 265 patients (47.3%). Post-CTS AF occurred in 177 patients (31.6%), with 27%, 23%, and 41% in the low-, medium-, and high-CHA2 DS2 -VASc score groups, respectively. The high-score group had a 2.3-fold increased odds of developing AF versus the medium-score group (p<0.0001). The differences between the high- and medium-score groups when each group was compared with the low-score group were not statistically significant. On the multivariate logistic regression analysis, CHA2 DS2 -VASc score was associated with development of AF (AOR 1.20, 95% CI 1.06-1.36). Increasing CHA2 DS2 -VASc score was an independent predictor for the development of post-CTS AF, with patients in the high-score group having the highest overall incidence. © 2013 Pharmacotherapy Publications, Inc.

Venous thromboembolism after major venous injuries: Competing priorities.

PubMed

Frank, Brian; Maher, Zoё; Hazelton, Joshua P; Resnick, Shelby; Dauer, Elizabeth; Goldenberg, Anna; Lubitz, Andrea L; Smith, Brian P; Saillant, Noelle N; Reilly, Patrick M; Seamon, Mark J

2017-12-01

Venous thromboembolism (VTE) after major vascular injury (MVI) is particularly challenging because the competing risk of thrombosis and embolization after direct vessel injury must be balanced with risk of bleeding after surgical repair. We hypothesized that venous injuries, repair type, and intraoperative anticoagulation would influence VTE formation after MVI. A multi-institution, retrospective cohort study of consecutive MVI patients was conducted at three urban, Level I centers (2005-2013). Patients with MVI of the neck, torso, or proximal extremities (to elbows/knees) were included. Our primary study endpoint was the development of VTE (DVT or pulmonary embolism [PE]). The 435 major vascular injury patients were primarily young (27 years) men (89%) with penetrating (84%) injuries. When patients with (n = 108) and without (n = 327) VTE were compared, we observed no difference in age, mechanism, extremity injury, tourniquet use, orthopedic and spine injuries, damage control, local heparinized saline, or vascular surgery consultation (all p > 0.05). VTE patients had greater Injury Severity Score (ISS) (17 vs. 12), shock indices (1 vs. 0.9), and more torso (58% vs. 35%) and venous (73% vs. 48%) injuries, but less often received systemic intraoperative anticoagulation (39% vs. 53%) or postoperative enoxaparin (47% vs. 61%) prophylaxis (all p < 0.05). After controlling for ISS, hemodynamics, injured vessel, intraoperative anticoagulation, and postoperative prophylaxis, multivariable analysis revealed venous injury was independently predictive of VTE (odds ratio, 2.7; p = 0.002). Multivariable analysis of the venous injuries subset (n = 237) then determined that only delay in starting VTE chemoprophylaxis (odds ratio, 1.3/day; p = 0.013) independently predicted VTE after controlling for ISS, hemodynamics, injured vessel, surgical subspecialty, intraoperative anticoagulation, and postoperative prophylaxis. Overall, 3.4% of venous injury patients developed PE, but PE rates were not related to their operative management (p = 0.72). Patients with major venous injuries are at high risk for VTE, regardless of intraoperative management. Our results support the immediate initiation of postoperative chemoprophylaxis in patients with major venous injuries. Therapeutic/care management, level IV.

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children.

PubMed

Zubrick, Stephen R; Taylor, Catherine L; Christensen, Daniel

2015-01-01

Oral language is the foundation of literacy. Naturally, policies and practices to promote children's literacy begin in early childhood and have a strong focus on developing children's oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children's progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children's oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children's progress along the oral to literate continuum is stable and predictable. Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years.

Prediction of energy expenditure and physical activity in preschoolers

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and feasible methods are needed to predict energy expenditure (EE) and physical activity (PA) levels in preschoolers. Herein, we validated cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on accelerometry and heart rate (HR) ...

Risk of false decision on conformity of a multicomponent material when test results of the components' content are correlated.

PubMed

Kuselman, Ilya; Pennecchi, Francesca R; da Silva, Ricardo J N B; Hibbert, D Brynn

2017-11-01

The probability of a false decision on conformity of a multicomponent material due to measurement uncertainty is discussed when test results are correlated. Specification limits of the components' content of such a material generate a multivariate specification interval/domain. When true values of components' content and corresponding test results are modelled by multivariate distributions (e.g. by multivariate normal distributions), a total global risk of a false decision on the material conformity can be evaluated based on calculation of integrals of their joint probability density function. No transformation of the raw data is required for that. A total specific risk can be evaluated as the joint posterior cumulative function of true values of a specific batch or lot lying outside the multivariate specification domain, when the vector of test results, obtained for the lot, is inside this domain. It was shown, using a case study of four components under control in a drug, that the correlation influence on the risk value is not easily predictable. To assess this influence, the evaluated total risk values were compared with those calculated for independent test results and also with those assuming much stronger correlation than that observed. While the observed statistically significant correlation did not lead to a visible difference in the total risk values in comparison to the independent test results, the stronger correlation among the variables caused either the total risk decreasing or its increasing, depending on the actual values of the test results. Copyright © 2017 Elsevier B.V. All rights reserved.

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D

2017-07-01

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

The Impact of ART on the Economic Outcomes of People Living with HIV/AIDS.

PubMed

Nannungi, Annet; Wagner, Glenn; Ghosh-Dastidar, Bonnie

2013-01-01

Background. Clinical benefits of ART are well documented, but less is known about its effects on economic outcomes such as work status and income in sub-Saharan Africa. Methods. Data were examined from 482 adult clients entering HIV care (257 starting ART; 225 not yet eligible for ART) in Kampala, Uganda. Self-reported data on work status and income were assessed at baseline, months 6 and 12. Multivariate analysis examined the effects of ART over time, controlling for change in physical health functioning and baseline covariates. Results. Fewer ART patients worked at baseline compared to non-ART patients (25.5% versus 34.2%); 48.8% of those not working at baseline were now working at month 6, and 50% at month 12, with similar improvement in both the ART and non-ART groups. However, multivariate analysis revealed that the ART group experienced greater improvement over time. Average weekly income did not differ between the groups at baseline nor change significantly over time, among those who were working; being male gender and having any secondary education were predictive of higher income. Conclusions. ART was associated with greater improvement in work status, even after controlling for change in physical health functioning, suggesting other factors associated with ART may influence work.

Mortality Prediction Model of Septic Shock Patients Based on Routinely Recorded Data

PubMed Central

Carrara, Marta; Baselli, Giuseppe; Ferrario, Manuela

2015-01-01

We studied the problem of mortality prediction in two datasets, the first composed of 23 septic shock patients and the second composed of 73 septic subjects selected from the public database MIMIC-II. For each patient we derived hemodynamic variables, laboratory results, and clinical information of the first 48 hours after shock onset and we performed univariate and multivariate analyses to predict mortality in the following 7 days. The results show interesting features that individually identify significant differences between survivors and nonsurvivors and features which gain importance only when considered together with the others in a multivariate regression model. This preliminary study on two small septic shock populations represents a novel contribution towards new personalized models for an integration of multiparameter patient information to improve critical care management of shock patients. PMID:26557154

Active Rack Isolation System Program and Technical Status

NASA Technical Reports Server (NTRS)

Bushnell, Glenn; Fialho, Ian; Allen, James; Quraishi, Naveed

2000-01-01

The Boeing Active Rack Isolation System (ARIS) is one of the means used to isolate acceleration-sensitive scientific experiments from structurally transmitted disturbances aboard the International Space Station. The presentation provides an overview of ARIS and technical issues associated with the development of the active control system. An overview of ARIS analytical models is presented along with recent isolation performance predictions made using these models. Issues associated with commanding and capturing ARIS data are discussed and possible future options based on the ARIS ISS Characterization Experiment (ICE) Payload On-orbit Processor (POP) are outlined. An overview of the ARIS-ICE experiment scheduled to fly on ISS Flight 6A is presented. The presentation concludes with a discussion of recent- developmental work that includes passive rack damping, umbilical redesigns and advanced multivariable control design methods.

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

The Role of Maladaptive Cognitions in Hypersexuality among Highly Sexually Active Gay and Bisexual Men

PubMed Central

Pachankis, John E.; Rendina, H. Jonathon; Ventuneac, Ana; Grov, Christian; Parsons, Jeffrey T.

2014-01-01

Cognitive appraisals about sex may represent an important component of the maintenance and treatment of hypersexuality, but they are not currently represented in conceptual models of hypersexuality. Therefore, we validated a measure of maladaptive cognitions about sex and examined its unique ability to predict hypersexuality. Qualitative interviews with a pilot sample of 60 highly sexually active gay and bisexual men and expert review of items yielded a pool of 17 items regarding maladaptive cognitions about sex. A separate sample of 202 highly sexually active gay and bisexual men completed measures of sexual inhibition and excitation, impulsivity, emotional dysregulation, depression and anxiety, sexual compulsivity, the Hypersexual Disorder Screening Inventory proposed by the American Psychiatric Association’s DSM-5 Workgroup on Sexual and Gender Identity Disorders (2010). Factor analysis confirmed the presence of three subscales: perceived sexual needs, sexual costs, and sexual control efficacy. Structural equation modeling results were consistent with a cognitive model of hypersexuality whereby magnifying the necessity of sex and disqualifying the benefits of sex partially predicted minimized self-efficacy for controlling one’s sexual behavior, all of which predicted problematic hypersexuality. In multivariate logistic regression, disqualifying the benefits of sex predicted unique variance in hypersexuality, even after adjusting for the role of core constructs of existing research on hypersexuality, AOR = 1.78, 95% CI 1.02, 3.10. Results suggest the utility of a cognitive approach for better understanding hypersexuality and the importance of developing treatment approaches that encourage adaptive appraisals regarding the outcomes of sex and one’s ability to control his sexual behavior. PMID:24558123

Prediction Errors but Not Sharpened Signals Simulate Multivoxel fMRI Patterns during Speech Perception

PubMed Central

Davis, Matthew H.

2016-01-01

Successful perception depends on combining sensory input with prior knowledge. However, the underlying mechanism by which these two sources of information are combined is unknown. In speech perception, as in other domains, two functionally distinct coding schemes have been proposed for how expectations influence representation of sensory evidence. Traditional models suggest that expected features of the speech input are enhanced or sharpened via interactive activation (Sharpened Signals). Conversely, Predictive Coding suggests that expected features are suppressed so that unexpected features of the speech input (Prediction Errors) are processed further. The present work is aimed at distinguishing between these two accounts of how prior knowledge influences speech perception. By combining behavioural, univariate, and multivariate fMRI measures of how sensory detail and prior expectations influence speech perception with computational modelling, we provide evidence in favour of Prediction Error computations. Increased sensory detail and informative expectations have additive behavioural and univariate neural effects because they both improve the accuracy of word report and reduce the BOLD signal in lateral temporal lobe regions. However, sensory detail and informative expectations have interacting effects on speech representations shown by multivariate fMRI in the posterior superior temporal sulcus. When prior knowledge was absent, increased sensory detail enhanced the amount of speech information measured in superior temporal multivoxel patterns, but with informative expectations, increased sensory detail reduced the amount of measured information. Computational simulations of Sharpened Signals and Prediction Errors during speech perception could both explain these behavioural and univariate fMRI observations. However, the multivariate fMRI observations were uniquely simulated by a Prediction Error and not a Sharpened Signal model. The interaction between prior expectation and sensory detail provides evidence for a Predictive Coding account of speech perception. Our work establishes methods that can be used to distinguish representations of Prediction Error and Sharpened Signals in other perceptual domains. PMID:27846209

Early PET/CT scan is more effective than RECIST in predicting outcome of patients with liver metastases from colorectal cancer treated with preoperative chemotherapy plus bevacizumab.

PubMed

Lastoria, Secondo; Piccirillo, Maria Carmela; Caracò, Corradina; Nasti, Guglielmo; Aloj, Luigi; Arrichiello, Cecilia; de Lutio di Castelguidone, Elisabetta; Tatangelo, Fabiana; Ottaiano, Alessandro; Iaffaioli, Rosario Vincenzo; Izzo, Francesco; Romano, Giovanni; Giordano, Pasqualina; Signoriello, Simona; Gallo, Ciro; Perrone, Francesco

2013-12-01

Markers predictive of treatment effect might be useful to improve the treatment of patients with metastatic solid tumors. Particularly, early changes in tumor metabolism measured by PET/CT with (18)F-FDG could predict the efficacy of treatment better than standard dimensional Response Evaluation Criteria In Solid Tumors (RECIST) response. We performed PET/CT evaluation before and after 1 cycle of treatment in patients with resectable liver metastases from colorectal cancer, within a phase 2 trial of preoperative FOLFIRI plus bevacizumab. For each lesion, the maximum standardized uptake value (SUV) and the total lesion glycolysis (TLG) were determined. On the basis of previous studies, a ≤ -50% change from baseline was used as a threshold for significant metabolic response for maximum SUV and, exploratively, for TLG. Standard RECIST response was assessed with CT after 3 mo of treatment. Pathologic response was assessed in patients undergoing resection. The association between metabolic and CT/RECIST and pathologic response was tested with the McNemar test; the ability to predict progression-free survival (PFS) and overall survival (OS) was tested with the Log-rank test and a multivariable Cox model. Thirty-three patients were analyzed. After treatment, there was a notable decrease of all the parameters measured by PET/CT. Early metabolic PET/CT response (either SUV- or TLG-based) had a stronger, independent and statistically significant predictive value for PFS and OS than both CT/RECIST and pathologic response at multivariate analysis, although with different degrees of statistical significance. The predictive value of CT/RECIST response was not significant at multivariate analysis. PET/CT response was significantly predictive of long-term outcomes during preoperative treatment of patients with liver metastases from colorectal cancer, and its predictive ability was higher than that of CT/RECIST response after 3 mo of treatment. Such findings need to be confirmed by larger prospective trials.

Multivariable nonlinear analysis of foreign exchange rates

NASA Astrophysics Data System (ADS)

Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

2003-05-01

We analyze the multivariable time series of foreign exchange rates. These are price movements that have often been analyzed, and dealing time intervals and spreads between bid and ask prices. Considering dealing time intervals as event timing such as neurons’ firings, we use raster plots (RPs) and peri-stimulus time histograms (PSTHs) which are popular methods in the field of neurophysiology. Introducing special processings to obtaining RPs and PSTHs time histograms for analyzing exchange rates time series, we discover that there exists dynamical interaction among three variables. We also find that adopting multivariables leads to improvements of prediction accuracy.

Significance of the DNA-Histone Complex Level as a Predictor of Major Adverse Cardiovascular Events in Hemodialysis Patients: The Effect of Uremic Toxin on DNA-Histone Complex Formation.

PubMed

Jeong, Jong Cheol; Kim, Ji-Eun; Gu, Ja-Yoon; Yoo, Hyun Ju; Ryu, Ji Won; Kim, Dong Ki; Joo, Kwon Wook; Kim, Hyun Kyung

2016-01-01

Neutrophils can release the DNA-histone complex into circulation following exposure to inflammatory stimuli. This prospective study investigated whether the DNA-histone complex and other biomarkers could predict major cardiovascular adverse events (MACEs) in hemodialysis (HD) patients. The levels of circulating DNA-histone complexes, cell-free DNA, interleukin (IL)-6, and neutrophil elastase were measured in 60 HD patients and 28 healthy controls. MACE was assessed at 24 months. Uremic toxin-induced neutrophil released contents were measured in vitro. Compared with controls, HD patients showed higher levels of DNA-histone complexes and IL-6. The DNA-histone complex level was inversely associated with the Kt/V. In a multivariable Cox analysis, the high level of DNA-histone complexes was a significant independent predictor of MACE. The uremic toxins induced DNA-histone complex formation in normal neutrophils in vitro. The DNA-histone complex is a potentially useful marker to predict MACE in HD patients. Uremic toxins induced DNA-histone complex formation in vitro. © 2015 S. Karger AG, Basel.

Using ecological momentary assessment to examine antecedents and correlates of physical activity bouts in adults age 50+ years: a pilot study.

PubMed

Dunton, Genevieve Fridlund; Atienza, Audie A; Castro, Cynthia M; King, Abby C

2009-12-01

National recommendations supporting the promotion of multiple short (10+ minute) physical activity bouts each day to increase overall physical activity levels in middle-aged and older adults underscore the need to identify antecedents and correlates of such daily physical activity episodes. This pilot study used Ecological Momentary Assessment to examine the time-lagged and concurrent effects of empirically supported social, cognitive, affective, and physiological factors on physical activity among adults age 50+ years. Participants (N = 23) responded to diary prompts on a handheld computer four times per day across a 2-week period. Moderate-to-vigorous physical activity (MVPA), self-efficacy, positive and negative affect, control, demand, fatigue, energy, social interactions, and stressful events were assessed during each sequence. Multivariate results showed that greater self-efficacy and control predicted greater MVPA at each subsequent assessment throughout the day (p < 0.05). Also, having a positive social interaction was concurrently related to higher levels of MVPA (p = 0.052). Time-varying multidimensional individual processes predict within daily physical activity levels.

Application of the new Cross Recurrence Plots to multivariate data

NASA Astrophysics Data System (ADS)

Thiel, M.; Romano, C.; Kurths, J.

2003-04-01

We extend and then apply the method of the new Cross Recurrence Plots (XRPs) to multivariate data. After introducing the new method we carry out an analysis of spatiotemporal ecological data. We compute not only the Rényi entropies and cross entropies by XRP, that allow to draw conclusions about the coupling of the systems, but also find a prediction horizon for intermediate time scales.

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Treesearch

Anantha M. Prasad; Louis R. Iverson; Andy Liaw; Andy Liaw

2006-01-01

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

PubMed

Lenhard, Fabian; Sauer, Sebastian; Andersson, Erik; Månsson, Kristoffer Nt; Mataix-Cols, David; Rück, Christian; Serlachius, Eva

2018-03-01

There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted. Copyright © 2017 John Wiley & Sons, Ltd.

A New Approach of Juvenile Age Estimation using Measurements of the Ilium and Multivariate Adaptive Regression Splines (MARS) Models for Better Age Prediction.

PubMed

Corron, Louise; Marchal, François; Condemi, Silvana; Chaumoître, Kathia; Adalian, Pascal

2017-01-01

Juvenile age estimation methods used in forensic anthropology generally lack methodological consistency and/or statistical validity. Considering this, a standard approach using nonparametric Multivariate Adaptive Regression Splines (MARS) models were tested to predict age from iliac biometric variables of male and female juveniles from Marseilles, France, aged 0-12 years. Models using unidimensional (length and width) and bidimensional iliac data (module and surface) were constructed on a training sample of 176 individuals and validated on an independent test sample of 68 individuals. Results show that MARS prediction models using iliac width, module and area give overall better and statistically valid age estimates. These models integrate punctual nonlinearities of the relationship between age and osteometric variables. By constructing valid prediction intervals whose size increases with age, MARS models take into account the normal increase of individual variability. MARS models can qualify as a practical and standardized approach for juvenile age estimation. © 2016 American Academy of Forensic Sciences.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

PubMed

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

PubMed

Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

2016-01-01

A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multivariable control altitude demonstration on the F100 turbofan engine

NASA Technical Reports Server (NTRS)

Lehtinen, B.; Dehoff, R. L.; Hackney, R. D.

1979-01-01

The F100 Multivariable control synthesis (MVCS) program, was aimed at demonstrating the benefits of LGR synthesis theory in the design of a multivariable engine control system for operation throughout the flight envelope. The advantages of such procedures include: (1) enhanced performance from cross-coupled controls, (2) maximum use of engine variable geometry, and (3) a systematic design procedure that can be applied efficiently to new engine systems. The control system designed, under the MVCS program, for the Pratt & Whitney F100 turbofan engine is described. Basic components of the control include: (1) a reference value generator for deriving a desired equilibrium state and an approximate control vector, (2) a transition model to produce compatible reference point trajectories during gross transients, (3) gain schedules for producing feedback terms appropriate to the flight condition, and (4) integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance.

Prognostic importance of DNA ploidy in non-endometrioid, high-risk endometrial carcinomas.

PubMed

Sorbe, Bengt

2016-03-01

The present study investigated the predictive and prognostic impact of DNA ploidy together with other well-known prognostic factors in a series of non-endometrioid, high-risk endometrial carcinomas. From a complete consecutive series of 4,543 endometrial carcinomas of International Federation of Gynecology and Obstetrics (FIGO) stages I-IV, 94 serous carcinomas, 48 clear cell carcinomas and 231 carcinosarcomas were selected as a non-endometrioid, high-risk group for further studies regarding prognosis. The impact of DNA ploidy, as assessed by flow cytometry, was of particular focus. The age of the patients, FIGO stage, depth of myometrial infiltration and tumor expression of p53 were also included in the analyses (univariate and multivariate). In the complete series of cases, the recurrence rate was 37%, and the 5-year overall survival rate was 39% with no difference between the three histological subtypes. The primary cure rate (78%) was also similar for all tumor types studied. DNA ploidy was a significant predictive factor (on univariate analysis) for primary tumor cure rate, and a prognostic factor for survival rate (on univariate and multivariate analyses). The predictive and prognostic impact of DNA ploidy was higher in carcinosarcomas than in serous and clear cell carcinomas. In the majority of multivariate analyses, FIGO stage and depth of myometrial infiltration were the most important predictive (tumor recurrence) and prognostic (survival rate) factors. DNA ploidy status is a less important predictive and prognostic factor in non-endometrioid, high-risk endometrial carcinomas than in the common endometrioid carcinomas, in which FIGO and nuclear grade also are highly significant and important factors.

Subsequent Vertebral Fractures Post Cement Augmentation of the Thoracolumbar Spine: Does it Correlate With Level-specific Bone Mineral Density Scores?

PubMed

Hey, Hwee Weng Dennis; Hwee Weng, Dennis Hey; Tan, Jun Hao; Jun, Hao Tan; Tan, Chuen Seng; Chuen, Seng Tan; Tan, Hsi Ming Bryan; Ming, Bryan Tan Hsi; Lau, Puang Huh Bernard; Huh, Bernard Lau Puang; Hee, Hwan Tak; Hwan, Tak Hee

2015-12-01

A case-control study. In this study, we investigated the correlation between level-specific preoperative bone mineral density and subsequent vertebral fractures. We also identified factors associated with subsequent vertebral fractures. Complications of cement augmentation of the spine include subsequent vertebral fractures, leading to unnecessary morbidity and more treatment. Ability to predict at-risk vertebra will help guide management. We studied all patients with osteoporotic compression fractures who underwent cement augmentation in a single institution from November 2001 to December 2010 by a single surgeon. Association between level-specific bone mineral density T-scores and subsequent fractures was assessed. Multivariable analysis was performed to identify significant factors associated with subsequent vertebral fractures. 93 patients followed up for a mean duration of 25.1 months (12-96) had a mean age of 76.8 years (47-99). Vertebroplasty was performed in 58 patients (62.4%) on 68 levels and kyphoplasty in 35 patients (37.6%) on 44 levels. Refracture was seen in 16 patients (17.2%). The time to subsequent fracture post cement augmentation was 20.5 months (2-90). For refracture cases, 43.8% (7/16) fractured in the adjacent vertebrae. Subsequently fractured vertebra had a mean T-score of -2.860 (95% confidence interval -3.268 to -2.452) and nonfractured vertebra had a mean T-score of -2.180 (95% confidence interval -2.373 to -1.986). A T-score of -2.2 or lower is predictive of refracture at that vertebra (P = 0.047). Odds ratio increases with decreasing T-scores from -2.2 or lower to -2.6 or lower. A T-score of -2.6 or lower gives no additional predictive advantage. After multivariable analysis, age (P = 0.049) and loss of preoperative anterior vertebral height (P = 0.017) are associated with refracture. Level-specific T-scores are predictive of subsequent fractures and the odds ratio increases with lower T-scores from -2.2 or less to -2.6 or less. They have a low positive predictive value, but a high negative predictive value for subsequent fractures. Other significant associations with subsequent refractures include age and anterior vertebral height. 4.

Glyburide in gestational diabetes--prediction of treatment failure.

PubMed

Yogev, Yariv; Melamed, Nir; Chen, Rony; Nassie, Daniel; Pardo, Joseph; Hod, Moshe

2011-06-01

To identify factors predicting failure of glyburide treatment in women with gestational diabetes mellitus (GDM). A retrospective study of all women with GDM that were treated with glyburide in a single tertiary referral center. Patients were switched from glyburide to insulin if they failed to achieve glycemic goals, and were then classified as glyburide failure. Overall, 124 women with GDM treated with glyburide were included in the study, of which 31 (25%) failed to achieve glycemic control. Women in the failure group were characterized by a higher weight gain during pregnancy, higher rates of GDM on previous pregnancies, and a glucose challenge test (GCT) result. On multivariate logistic regression analysis, a GCT value of >200 mg/dl (OR = 7.1, 95% CI 2.8-27.6) and weight gain ≥ 12 kg (OR = 3.9, 95% CI 1.2-13.0) were the only significant and independent predictors of glyburide failure. Most women who were successfully treated with glyburide required a daily dose of 5 mg or less and the time required to achieve glycemic control in these cases was 12.4 ± 4.9 days (range 5-24 days). Of the women who failed to achieve glycemic control with gluburide, 26/31 were switched to insulin, of them only 12 (46%) achieved desired level of glycemic control. Most women with GDM achieved desired level of glycemic control under glyburide treatment.

Acoustic neuroma: potential risk factors and audiometric surveillance in the aluminium industry.

PubMed

Taiwo, Oyebode; Galusha, Deron; Tessier-Sherman, Baylah; Kirsche, Sharon; Cantley, Linda; Slade, Martin D; Cullen, Mark R; Donoghue, A Michael

2014-09-01

To look for an association between acoustic neuroma (AN) and participation in a hearing conservation programme (HCP) and also for an association between AN and possible occupational risk factors in the aluminium industry. We conducted a case-control analysis of a population of US aluminium production workers in 8 smelters and 43 other plants. Using insurance claims data, 97 cases of AN were identified between 1996 and 2009. Each was matched with four controls. Covariates included participation in a HCP, working in an aluminium smelter, working in an electrical job and hearing loss. In the bivariate analyses, covariates associated with AN were participation in the HCP (OR=1.72; 95% CI 1.09 to 2.69) and smelter work (OR=1.88; 95% CI 1.06 to 3.36). Electrical work was not significant (OR=1.60; 95% CI 0.65 to 3.94). Owing to high participation in the HCP in smelters, multivariate subanalyses were required. In the multivariate analyses, participation in the HCP was the only statistically significant risk factor for AN. In the multivariate analysis restricted to employees not working in a smelter, the OR was 1.81 (95% CI 1.04 to 3.17). Hearing loss, an indirect measure of in-ear noise dose, was not predictive of AN. Our results suggest the incidental detection of previously undiagnosed tumours in workers who participated in the company-sponsored HCP. The increased medical surveillance among this population of workers most likely introduced detection bias, leading to the identification of AN cases that would have otherwise remained undetected. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Predictors of Recurrence, Progression, and Retreatment in Basilar Tip Aneurysms: A Location-Controlled Analysis.

PubMed

Abecassis, Isaac Josh; Sen, Rajeev D; Barber, Jason; Shetty, Rakshith; Kelly, Cory M; Ghodke, Basavaraj V; Hallam, Danial K; Levitt, Michael R; Kim, Louis J; Sekhar, Laligam N

2018-06-14

Endovascular treatment of intracranial aneurysms is associated with higher rates of recurrence and retreatment, though contemporary rates and risk factors for basilar tip aneurysms (BTAs) are less well-described. To characterize progression, retreatement, and retreated progression of BTAs treated with microsurgical or endovascular interventions. We retrospectively reviewed records for 141 consecutive BTA patients. We included 158 anterior communicating artery (ACoA) and 118 middle cerebral artery (MCA) aneurysms as controls. Univariate and multivariate analyses were used to calculate rates of progression (recurrence of previously obliterated aneurysms and progression of known residual aneurysm dome or neck), retreatment, and retreated progression. Kaplan-Meier analysis was used to characterize 24-mo event rates for primary outcome prediction. Of 141 BTA patients, 62.4% were ruptured and 37.6% were unruptured. Average radiographical follow-up was 33 mo. Among ruptured aneurysms treated with clipping, there were 2 rehemorrhages due to recurrence (6.1%), and none in any other cohorts. Overall rates of progression (28.9%), retreatment (28.9%), and retreated progression (24.7%) were not significantly different between surgical and endovascular subgroups, though ruptured aneurysms had higher event rates. Multivariate modeling confirmed rupture status (P = .003, hazard ratio = 0.14) and aneurysm dome width (P = .005, hazard ratio = 1.23) as independent predictors of progression requiring retreatment. In a separate multivariate analysis with ACoA and MCA aneurysms, basilar tip location was an independent predictor of progression, retreatment, and retreated progression. BTAs have higher rates of progression and retreated progression than other aneurysm locations, independent of treatment modality. Rupture status and dome width are risk factors for progression requiring retreatment.

Productivity losses in chronic obstructive pulmonary disease: a population-based survey.

PubMed

Erdal, Marta; Johannessen, Ane; Askildsen, Jan Erik; Eagan, Tomas; Gulsvik, Amund; Grønseth, Rune

2014-01-01

We aimed to estimate incremental productivity losses (sick leave and disability) of spirometry-defined chronic obstructive pulmonary disease (COPD) in a population-based sample and in hospital-recruited patients with COPD. Furthermore, we examined predictors of productivity losses by multivariate analyses. We performed four quarterly telephone interviews of 53 and 107 population-based patients with COPD and controls, as well as 102 hospital-recruited patients with COPD below retirement age. Information was gathered regarding annual productivity loss, exacerbations of respiratory symptoms and comorbidities. Incremental productivity losses were estimated by multivariate quantile median regression according to the human capital approach, adjusting for sex, age, smoking habits, education and lung function. Main effect variables were COPD/control status, number of comorbidities and exacerbations of respiratory symptoms. Altogether 55%, 87% and 31% of population-based COPD cases, controls and hospital patients, respectively, had a paid job at baseline. The annual incremental productivity losses were 5.8 (95% CI 1.4 to 10.1) and 330.6 (95% CI 327.8 to 333.3) days, comparing population-recruited and hospital-recruited patients with COPD to controls, respectively. There were significantly higher productivity losses associated with female sex and less education. Additional adjustments for comorbidities, exacerbations and FEV1% predicted explained all productivity losses in the population-based sample, as well as nearly 40% of the productivity losses in hospital-recruited patients. Annual incremental productivity losses were more than 50 times higher in hospital-recruited patients with COPD than that of population-recruited patients with COPD. To ensure a precise estimation of societal burden, studies on patients with COPD should be population-based.

Effects of Humidity Variation on the Hantavirus Infection and Hemorrhagic Fever with Renal Syndrome Occurrence in Subtropical China

PubMed Central

Xiao, Hong; Huang, Ru; Gao, Li-Dong; Huang, Cun-Rui; Lin, Xiao-Ling; Li, Na; Liu, Hai-Ning; Tong, Shi-Lu; Tian, Huai-Yu

2016-01-01

Infection rates of rodents have a significant influence on the transmission of hemorrhagic fever with renal syndrome (HFRS). In this study, four cities and two counties with high HFRS incidence in eastern Hunan Province in China were studied, and surveillance data of rodents, as well as HFRS cases and related environmental variables from 2007 to 2010, were collected. Results indicate that the distribution and infection rates of rodents are closely associated with environmental conditions. Hantavirus infections in rodents were positively correlated with temperature vegetation dryness index and negatively correlated with elevation. The predictive risk maps based on multivariate regression model revealed that the annual variation of infection risks is small, whereas monthly variation is large and corresponded well to the seasonal variation of human HFRS incidence. The identification of risk factors and risk prediction provides decision support for rodent surveillance and the prevention and control of HFRS. PMID:26711521

A k-omega-multivariate beta PDF for supersonic combustion

NASA Technical Reports Server (NTRS)

Alexopoulos, G. A.; Baurle, R. A.; Hassan, H. A.

1992-01-01

In an attempt to study the interaction between combustion and turbulence in supersonic flows, an assumed PDF has been employed. This makes it possible to calculate the time average of the chemical source terms that appear in the species conservation equations. In order to determine the averages indicated in an equation, two transport equations, one for the temperature (enthalpy) variance and one for Q, are required. Model equations are formulated for such quantities. The turbulent time scale controls the evolution. An algebraic model similar to that used by Eklund et al was used in an attempt to predict the recent measurements of Cheng et al. Predictions were satisfactory before ignition but were less satisfactory after ignition. One of the reasons for this behavior is the inadequacy of the algebraic turbulence model employed. Because of this, the objective of this work is to develop a k-omega model to remedy the situation.

Robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials

PubMed Central

Jiang, Xuejun; Guo, Xu; Zhang, Ning; Wang, Bo

2018-01-01

This article presents and investigates performance of a series of robust multivariate nonparametric tests for detection of location shift between two multivariate samples in randomized controlled trials. The tests are built upon robust estimators of distribution locations (medians, Hodges-Lehmann estimators, and an extended U statistic) with both unscaled and scaled versions. The nonparametric tests are robust to outliers and do not assume that the two samples are drawn from multivariate normal distributions. Bootstrap and permutation approaches are introduced for determining the p-values of the proposed test statistics. Simulation studies are conducted and numerical results are reported to examine performance of the proposed statistical tests. The numerical results demonstrate that the robust multivariate nonparametric tests constructed from the Hodges-Lehmann estimators are more efficient than those based on medians and the extended U statistic. The permutation approach can provide a more stringent control of Type I error and is generally more powerful than the bootstrap procedure. The proposed robust nonparametric tests are applied to detect multivariate distributional difference between the intervention and control groups in the Thai Healthy Choices study and examine the intervention effect of a four-session motivational interviewing-based intervention developed in the study to reduce risk behaviors among youth living with HIV. PMID:29672555

Insulin resistance, diabetes mellitus and thyroid dysfunction in patients with palmoplantar pustulosis: a case-controlled study.

PubMed

Ataş, Hatice; Gönül, Müzeyyen

2017-06-01

Palmoplantar pustulosis (PPP) is a chronic pustular inflammatory skin disease; however, its pathogenesis is not well understood. Several factors, such as genetics, tobacco use and autoimmune issues, may contribute to this disease. This research was conducted to investigate the relationships between insulin resistance, thyroid disease and PPP. Thirty-three patients with PPP and 27 age- and gender-matched controls were analysed for their smoking histories, thyroid function tests, anti-thyroid peroxidase antibody (anti-TPO) levels, fasting glucose, fasting insulin levels and the homeostatic model assessment (HOMA) index for insulin resistance. We found significant differences between the PPP and control groups according to their tobacco use and anti-TPO levels ( p = 0.009 and p = 0.009, respectively). The proportion of tobacco use was 90% in the PPP patients and 63% in the controls. Gender and tobacco use were predictive risk factors for PPP in the multivariate analysis ( OR = 141.7, p < 0.0001 and OR = 147.6, p = 0.006, respectively). An anti-TPO level > 35 U/ml and the presence of a thyroid abnormality were independent risk factors in the univariate, but not the multivariate analysis ( OR = 4.2, p = 0.025 and OR = 5.4, p = 0.004, respectively). A moderate correlation between the gender and anti-TPO level was found ( r = 0.361, p = 0.039); however, the fasting glucose, insulin and HOMA index were not significant between the PPP and control groups. Female gender and smoking were the most important risk factors for PPP; however, the increase in the anti-TPO level may be related to the predominance of females afflicted with this disease. Additional studies are necessary to clarify the relationships between PPP, thyroid disease and diabetes mellitus.

Trait-related decision-making impairment in the three phases of bipolar disorder.

PubMed

Adida, Marc; Jollant, Fabrice; Clark, Luke; Besnier, Nathalie; Guillaume, Sébastien; Kaladjian, Arthur; Mazzola-Pomietto, Pascale; Jeanningros, Régine; Goodwin, Guy M; Azorin, Jean-Michel; Courtet, Philippe

2011-08-15

In bipolar disorder (BD), little is known about how deficits in neurocognitive functions such as decision-making are related to phase of illness. We predicted that manic, depressed, and euthymic bipolar patients (BPs) would display impaired decision-making, and we tested whether clinical characteristics could predict patients' decision-making performance. Subjects (N = 317; age range: 18-65 years) including 167 BPs (45 manic and 32 depressed inpatients, and 90 euthymic outpatients) and 150 age-, IQ-, and gender-matched healthy control (HC) participants, were included within three university psychiatric hospitals using a cross-sectional design. The relationship between predictor variables and decision-making was assessed by one-step multivariate analysis. The main outcome measures were overall decision-making ability on the Iowa Gambling Task (IGT) and an index of sensitivity to punishment frequency. Manic, depressed, and euthymic BPs selected significantly more cards from the risky decks than HCs (p < .001, p < .01, and p < .05, respectively), with no significant differences between the three BD groups. However, like HCs, BPs preferred decks that yielded infrequent penalties over those yielding frequent penalties. In multivariate analysis, decision-making impairment was significantly (p < .001) predicted by low level of education, high depressive scores, family history of BD, use of benzodiazepines, and nonuse of serotonin and norepinephrine reuptake inhibitor (SNRI) antidepressants. BPs have a trait-related impairment in decision-making that does not vary across illness phase. However, some subtle differences between the BD groups in the individual deck analyses may point to subtle state influences on reinforcement mechanisms, in addition to a more fundamental trait impairment in risk-sensitive decision making. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Lower Quarter Y-Balance Test Scores and Lower Extremity Injury in NCAA Division I Athletes.

PubMed

Lai, Wilson C; Wang, Dean; Chen, James B; Vail, Jeremy; Rugg, Caitlin M; Hame, Sharon L

2017-08-01

Functional movement tests that are predictive of injury risk in National Collegiate Athletic Association (NCAA) athletes are useful tools for sports medicine professionals. The Lower Quarter Y-Balance Test (YBT-LQ) measures single-leg balance and reach distances in 3 directions. To assess whether the YBT-LQ predicts the laterality and risk of sports-related lower extremity (LE) injury in NCAA athletes. Case-control study; Level of evidence, 3. The YBT-LQ was administered to 294 NCAA Division I athletes from 21 sports during preparticipation physical examinations at a single institution. Athletes were followed prospectively over the course of the corresponding season. Correlation analysis was performed between the laterality of reach asymmetry and composite scores (CS) versus the laterality of injury. Receiver operating characteristic (ROC) analysis was used to determine the optimal asymmetry cutoff score for YBT-LQ. A multivariate regression analysis adjusting for sex, sport type, body mass index, and history of prior LE surgery was performed to assess predictors of earlier and higher rates of injury. Neither the laterality of reach asymmetry nor the CS correlated with the laterality of injury. ROC analysis found optimal cutoff scores of 2, 9, and 3 cm for anterior, posteromedial, and posterolateral reach, respectively. All of these potential cutoff scores, along with a cutoff score of 4 cm used in the majority of prior studies, were associated with poor sensitivity and specificity. Furthermore, none of the asymmetric cutoff scores were associated with earlier or increased rate of injury in the multivariate analyses. YBT-LQ scores alone do not predict LE injury in this collegiate athlete population. Sports medicine professionals should be cautioned against using the YBT-LQ alone to screen for injury risk in collegiate athletes.

Clinical utility of an epigenetic assay to detect occult prostate cancer in histopathologically negative biopsies: results of the MATLOC study.

PubMed

Stewart, Grant D; Van Neste, Leander; Delvenne, Philippe; Delrée, Paul; Delga, Agnès; McNeill, S Alan; O'Donnell, Marie; Clark, James; Van Criekinge, Wim; Bigley, Joseph; Harrison, David J

2013-03-01

Concern about possible false-negative prostate biopsy histopathology findings often leads to rebiopsy. A quantitative methylation specific polymerase chain reaction assay panel, including GSTP1, APC and RASSF1, could increase the sensitivity of detecting cancer over that of pathological review alone, leading to a high negative predictive value and a decrease in unnecessary repeat biopsies. The MATLOC study blindly tested archived prostate biopsy needle core tissue samples of 498 subjects from the United Kingdom and Belgium with histopathologically negative prostate biopsies, followed by positive (cases) or negative (controls) repeat biopsy within 30 months. Clinical performance of the epigenetic marker panel, emphasizing negative predictive value, was assessed and cross-validated. Multivariate logistic regression was used to evaluate all risk factors. The epigenetic assay performed on the first negative biopsies of this retrospective review cohort resulted in a negative predictive value of 90% (95% CI 87-93). In a multivariate model correcting for patient age, prostate specific antigen, digital rectal examination and first biopsy histopathological characteristics the epigenetic assay was a significant independent predictor of patient outcome (OR 3.17, 95% CI 1.81-5.53). A multiplex quantitative methylation specific polymerase chain reaction assay determining the methylation status of GSTP1, APC and RASSF1 was strongly associated with repeat biopsy outcome up to 30 months after initial negative biopsy in men with suspicion of prostate cancer. Adding this epigenetic assay could improve the prostate cancer diagnostic process and decrease unnecessary repeat biopsies. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

What Factors are Predictive of Patient-reported Outcomes? A Prospective Study of 337 Shoulder Arthroplasties.

PubMed

Matsen, Frederick A; Russ, Stacy M; Vu, Phuong T; Hsu, Jason E; Lucas, Robert M; Comstock, Bryan A

2016-11-01

Although shoulder arthroplasties generally are effective in improving patients' comfort and function, the results are variable for reasons that are not well understood. We posed two questions: (1) What factors are associated with better 2-year outcomes after shoulder arthroplasty? (2) What are the sensitivities, specificities, and positive and negative predictive values of a multivariate predictive model for better outcome? Three hundred thirty-nine patients having a shoulder arthroplasty (hemiarthroplasty, arthroplasty for cuff tear arthropathy, ream and run arthroplasty, total shoulder or reverse total shoulder arthroplasty) between August 24, 2010 and December 31, 2012 consented to participate in this prospective study. Two patients were excluded because they were missing baseline variables. Forty-three patients were missing 2-year data. Univariate and multivariate analyses determined the relationship of baseline patient, shoulder, and surgical characteristics to a "better" outcome, defined as an improvement of at least 30% of the maximal possible improvement in the Simple Shoulder Test. The results were used to develop a predictive model, the accuracy of which was tested using a 10-fold cross-validation. After controlling for potentially relevant confounding variables, the multivariate analysis showed that the factors significantly associated with better outcomes were American Society of Anesthesiologists Class I (odds ratio [OR], 1.94; 95% CI, 1.03-3.65; p = 0.041), shoulder problem not related to work (OR, 5.36; 95% CI, 2.15-13.37; p < 0.001), lower baseline Simple Shoulder Test score (OR, 1.32; 95% CI, 1.23-1.42; p < 0.001), no prior shoulder surgery (OR, 1.79; 95% CI, 1.18-2.70; p = 0.006), humeral head not superiorly displaced on the AP radiograph (OR, 2.14; 95% CI, 1.15-4.02; p = 0.017), and glenoid type other than A1 (OR, 4.47; 95% CI, 2.24-8.94; p < 0.001). Neither preoperative glenoid version nor posterior decentering of the humeral head on the glenoid were associated with the outcomes. The model predictive of a better result was driven mainly by the six factors listed above. The area under the receiver operating characteristic curve generated from the cross-validated enhanced predictive model was 0.79 (generally values of 0.7 to 0.8 are considered fair and values of 0.8 to 0.9 are considered good). The false-positive fraction and the true-positive fraction depended on the cutoff probability selected (ie, the selected probability above which the prediction would be classified as a better outcome). A cutoff probability of 0.68 yielded the best performance of the model with cross-validation predictions of better outcomes for 236 patients (80%) and worse outcomes for 58 patients (20%); sensitivity of 91% (95% CI, 88%-95%); specificity of 65% (95% CI, 53%-77%); positive predictive value of 92% (95% CI, 88%-95%); and negative predictive value of 64% (95% CI, 51%-76%). We found six easy-to-determine preoperative patient and shoulder factors that were significantly associated with better outcomes of shoulder arthroplasty. A model based on these characteristics had good predictive properties for identifying patients likely to have a better outcome from shoulder arthroplasty. Future research could refine this model with larger patient populations from multiple practices. Level II, therapeutic study.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Structural analysis and design of multivariable control systems: An algebraic approach

NASA Technical Reports Server (NTRS)

Tsay, Yih Tsong; Shieh, Leang-San; Barnett, Stephen

1988-01-01

The application of algebraic system theory to the design of controllers for multivariable (MV) systems is explored analytically using an approach based on state-space representations and matrix-fraction descriptions. Chapters are devoted to characteristic lambda matrices and canonical descriptions of MIMO systems; spectral analysis, divisors, and spectral factors of nonsingular lambda matrices; feedback control of MV systems; and structural decomposition theories and their application to MV control systems.

Vectored Thrust Digital Flight Control for Crew Escape. Volume 2.

DTIC Science & Technology

1985-12-01

no. 24. Lecrique, J., A. Rault, M. Tessier and J.L. Testud (1978), - "Multivariable Regulation of a Thermal Power Plant Steam Generator," presented...and Extended Kalman Observers," presented at the Conf. Decision and Control, San Diego, CA. Testud , J.L. (1977), Commande Numerique Multivariable du

Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series.

PubMed

Dorota, Myszkowska

2013-03-01

The aim of the study was to construct the model forecasting the birch pollen season characteristics in Cracow on the basis of an 18-year data series. The study was performed using the volumetric method (Lanzoni/Burkard trap). The 98/95 % method was used to calculate the pollen season. The Spearman's correlation test was applied to find the relationship between the meteorological parameters and pollen season characteristics. To construct the predictive model, the backward stepwise multiple regression analysis was used including the multi-collinearity of variables. The predictive models best fitted the pollen season start and end, especially models containing two independent variables. The peak concentration value was predicted with the higher prediction error. Also the accuracy of the models predicting the pollen season characteristics in 2009 was higher in comparison with 2010. Both, the multi-variable model and one-variable model for the beginning of the pollen season included air temperature during the last 10 days of February, while the multi-variable model also included humidity at the beginning of April. The models forecasting the end of the pollen season were based on temperature in March-April, while the peak day was predicted using the temperature during the last 10 days of March.

A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.

PubMed

Liu, Zitao; Hauskrecht, Milos

2017-11-01

Building of an accurate predictive model of clinical time series for a patient is critical for understanding of the patient condition, its dynamics, and optimal patient management. Unfortunately, this process is not straightforward. First, patient-specific variations are typically large and population-based models derived or learned from many different patients are often unable to support accurate predictions for each individual patient. Moreover, time series observed for one patient at any point in time may be too short and insufficient to learn a high-quality patient-specific model just from the patient's own data. To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions. The framework relies on the adaptive model switching approach that at any point in time selects the most promising time series model out of the pool of many possible models, and consequently, combines advantages of the population, patient-specific and short-term individualized predictive models. We demonstrate that the adaptive model switching framework is very promising approach to support personalized time series prediction, and that it is able to outperform predictions based on pure population and patient-specific models, as well as, other patient-specific model adaptation strategies.

Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour

NASA Astrophysics Data System (ADS)

Eyarkai Nambi, Vijayaram; Thangavel, Kuladaisamy; Manickavasagan, Annamalai; Shahir, Sultan

2017-01-01

Prediction of ripeness level in climacteric fruits is essential for post-harvest handling. An index capable of predicting ripening level with minimum inputs would be highly beneficial to the handlers, processors and researchers in fruit industry. A study was conducted with Indian mango cultivars to develop a ripeness index and associated model. Changes in physicochemical, colour and textural properties were measured throughout the ripening period and the period was classified into five stages (unripe, early ripe, partially ripe, ripe and over ripe). Multivariate regression techniques like partial least square regression, principal component regression and multi linear regression were compared and evaluated for its prediction. Multi linear regression model with 12 parameters was found more suitable in ripening prediction. Scientific variable reduction method was adopted to simplify the developed model. Better prediction was achieved with either 2 or 3 variables (total soluble solids, colour and acidity). Cross validation was done to increase the robustness and it was found that proposed ripening index was more effective in prediction of ripening stages. Three-variable model would be suitable for commercial applications where reasonable accuracies are sufficient. However, 12-variable model can be used to obtain more precise results in research and development applications.

Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

PubMed

Du, Juan; Yang, Fang; Zhang, Zhiqiang; Hu, Jingze; Xu, Qiang; Hu, Jianping; Zeng, Fanyong; Lu, Guangming; Liu, Xinfeng

2018-05-15

An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.

A multivariate test of disease risk reveals conditions leading to disease amplification.

PubMed

Halliday, Fletcher W; Heckman, Robert W; Wilfahrt, Peter A; Mitchell, Charles E

2017-10-25

Theory predicts that increasing biodiversity will dilute the risk of infectious diseases under certain conditions and will amplify disease risk under others. Yet, few empirical studies demonstrate amplification. This contrast may occur because few studies have considered the multivariate nature of disease risk, which includes richness and abundance of parasites with different transmission modes. By combining a multivariate statistical model developed for biodiversity-ecosystem-multifunctionality with an extensive field manipulation of host (plant) richness, composition and resource supply to hosts, we reveal that (i) host richness alone could not explain most changes in disease risk, and (ii) shifting host composition allowed disease amplification, depending on parasite transmission mode. Specifically, as predicted from theory, the effect of host diversity on parasite abundance differed for microbes (more density-dependent transmission) and insects (more frequency-dependent transmission). Host diversity did not influence microbial parasite abundance, but nearly doubled insect parasite abundance, and this amplification effect was attributable to variation in host composition. Parasite richness was reduced by resource addition, but only in species-rich host communities. Overall, this study demonstrates that multiple drivers, related to both host community and parasite characteristics, can influence disease risk. Furthermore, it provides a framework for evaluating multivariate disease risk in other systems. © 2017 The Author(s).

A multivariate analysis of genetic constraints to life history evolution in a wild population of red deer.

PubMed

Walling, Craig A; Morrissey, Michael B; Foerster, Katharina; Clutton-Brock, Tim H; Pemberton, Josephine M; Kruuk, Loeske E B

2014-12-01

Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer (Cervus elaphus). We use factor analytic modeling of the genetic variance-covariance matrix ( G: ) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G: on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations. Copyright © 2014 Walling et al.

A Multivariate Analysis of Genetic Constraints to Life History Evolution in a Wild Population of Red Deer

PubMed Central

Walling, Craig A.; Morrissey, Michael B.; Foerster, Katharina; Clutton-Brock, Tim H.; Pemberton, Josephine M.; Kruuk, Loeske E. B.

2014-01-01

Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer (Cervus elaphus). We use factor analytic modeling of the genetic variance–covariance matrix (G) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations. PMID:25278555

Multivariate Analysis and Prediction of Dioxin-Furan ...

EPA Pesticide Factsheets

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

PubMed Central

Feinauer, Christoph; Procaccini, Andrea; Zecchina, Riccardo; Weigt, Martin; Pagnani, Andrea

2014-01-01

In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code. PMID:24663061

Application of advanced control techniques to aircraft propulsion systems

NASA Technical Reports Server (NTRS)

Lehtinen, B.

1984-01-01

Two programs are described which involve the application of advanced control techniques to the design of engine control algorithms. Multivariable control theory is used in the F100 MVCS (multivariable control synthesis) program to design controls which coordinate the control inputs for improved engine performance. A systematic method for handling a complex control design task is given. Methods of analytical redundancy are aimed at increasing the control system reliability. The F100 DIA (detection, isolation, and accommodation) program, which investigates the uses of software to replace or augment hardware redundancy for certain critical engine sensor, is described.

Practical Methods for the Compensation and Control of Multivariable Systems.

DTIC Science & Technology

1982-04-01

a constant gain element gji . To be more specific, let us consider a linear multivariable system whose dynamical behavior is specified by a (pxm...controllable via uk if Yi is fed back to uj via an arbitrary gain gji , as depicted in the figure below? It might be noted that only the outputs and inputs...modes controllable via uk(s) before feedback will remain -19- controllable via uk(s) irrespective of gji (although certain of these uk controllable

Power and sample size for multivariate logistic modeling of unmatched case-control studies.

PubMed

Gail, Mitchell H; Haneuse, Sebastien

2017-01-01

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

Robustness of reduced-order multivariable state-space self-tuning controller

NASA Technical Reports Server (NTRS)

Yuan, Zhuzhi; Chen, Zengqiang

1994-01-01

In this paper, we present a quantitative analysis of the robustness of a reduced-order pole-assignment state-space self-tuning controller for a multivariable adaptive control system whose order of the real process is higher than that of the model used in the controller design. The result of stability analysis shows that, under a specific bounded modelling error, the adaptively controlled closed-loop real system via the reduced-order state-space self-tuner is BIBO stable in the presence of unmodelled dynamics.

Analysis techniques for multivariate root loci. [a tool in linear control systems

NASA Technical Reports Server (NTRS)

Thompson, P. M.; Stein, G.; Laub, A. J.

1980-01-01

Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.

Influence of parental factors on adolescents' transition to first sexual intercourse in Nairobi, Kenya: a longitudinal study.

PubMed

Okigbo, Chinelo C; Kabiru, Caroline W; Mumah, Joyce N; Mojola, Sanyu A; Beguy, Donatien

2015-08-21

Several studies have demonstrated a link between young people's sexual behavior and levels of parental monitoring, parent-child communication, and parental discipline in Western countries. However, little is known about this association in African settings, especially among young people living in high poverty settings such as urban slums. The objective of the study was to assess the influence of parental factors (monitoring, communication, and discipline) on the transition to first sexual intercourse among unmarried adolescents living in urban slums in Kenya. Longitudinal data collected from young people living in two slums in Nairobi, Kenya were used. The sample was restricted to unmarried adolescents aged 12-19 years at Wave 1 (weighted n = 1927). Parental factors at Wave 1 were used to predict adolescents' transition to first sexual intercourse by Wave 2. Relevant covariates including the adolescents' age, sex, residence, school enrollment, religiosity, delinquency, and peer models for risk behavior were controlled for. Multivariate logistic regression models were used to assess the associations of interest. All analyses were conducted using Stata version 13. Approximately 6% of our sample transitioned to first sexual intercourse within the one-year study period; there was no sex difference in the transition rate. In the multivariate analyses, male adolescents who reported communication with their mothers were less likely to transition to first sexual intercourse compared to those who did not (p < 0.05). This association persisted even after controlling for relevant covariates (OR: ≤0.33; p < 0.05). However, parental monitoring, discipline, and communication with their fathers did not predict transition to first sexual intercourse for male adolescents. For female adolescents, parental monitoring, discipline, and communication with fathers predicted transition to first sexual intercourse; however, only communication with fathers remained statistically significant after controlling for relevant covariates (OR: 0.30; 95% C.I.: 0.13-0.68). This study provides evidence that cross-gender communication with parents is associated with a delay in the onset of sexual intercourse among slum-dwelling adolescents. Targeted adolescent sexual and reproductive health programmatic interventions that include parents may have significant impacts on delaying sexual debut, and possibly reducing sexual risk behaviors, among young people in high-risk settings such as slums.

Multivariate Models of Men's and Women's Partner Aggression

ERIC Educational Resources Information Center

O'Leary, K. Daniel; Smith Slep, Amy M.; O'Leary, Susan G.

2007-01-01

This exploratory study was designed to address how multiple factors drawn from varying focal models and ecological levels of influence might operate relative to each other to predict partner aggression, using data from 453 representatively sampled couples. The resulting cross-validated models predicted approximately 50% of the variance in men's…

Assessing the sensitivity and robustness of prediction models for apple firmness using spectral scattering technique

USDA-ARS?s Scientific Manuscript database

Spectral scattering is useful for nondestructive sensing of fruit firmness. Prediction models, however, are typically built using multivariate statistical methods such as partial least squares regression (PLSR), whose performance generally depends on the characteristics of the data. The aim of this ...

Flood-frequency prediction methods for unregulated streams of Tennessee, 2000

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include the regional-regression method and the newer region-of-influence method. The prediction methods were developed using stream-gage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. These methods, however, should not be used in heavily developed or storm-sewered basins with impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged rivers and streams of Tennessee. Regional-regression equations were derived by using both single-variable and multivariable regional-regression analysis. Contributing drainage area is the explanatory variable used in the single-variable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Deleted-residual standard error for the single-variable equations ranged from 32 to 65 percent. Deleted-residual standard error for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. The region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar sites selected from the study area. Explanatory variables that may be used in regression equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Deleted-residual standard error for the region-of-influence method tended to be only slightly smaller than those for the regional-regression method and ranged from 27 to 62 percent.

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

Forecasting paediatric malaria admissions on the Kenya Coast using rainfall.

PubMed

Karuri, Stella Wanjugu; Snow, Robert W

2016-01-01

Malaria is a vector-borne disease which, despite recent scaled-up efforts to achieve control in Africa, continues to pose a major threat to child survival. The disease is caused by the protozoan parasite Plasmodium and requires mosquitoes and humans for transmission. Rainfall is a major factor in seasonal and secular patterns of malaria transmission along the East African coast. The goal of the study was to develop a model to reliably forecast incidences of paediatric malaria admissions to Kilifi District Hospital (KDH). In this article, we apply several statistical models to look at the temporal association between monthly paediatric malaria hospital admissions, rainfall, and Indian Ocean sea surface temperatures. Trend and seasonally adjusted, marginal and multivariate, time-series models for hospital admissions were applied to a unique data set to examine the role of climate, seasonality, and long-term anomalies in predicting malaria hospital admission rates and whether these might become more or less predictable with increasing vector control. The proportion of paediatric admissions to KDH that have malaria as a cause of admission can be forecast by a model which depends on the proportion of malaria admissions in the previous 2 months. This model is improved by incorporating either the previous month's Indian Ocean Dipole information or the previous 2 months' rainfall. Surveillance data can help build time-series prediction models which can be used to anticipate seasonal variations in clinical burdens of malaria in stable transmission areas and aid the timing of malaria vector control.

Healthcare-associated infections are associated with insufficient dietary intake: an observational cross-sectional study.

PubMed

Thibault, Ronan; Makhlouf, Anne-Marie; Kossovsky, Michel P; Iavindrasana, Jimison; Chikhi, Marinette; Meyer, Rodolphe; Pittet, Didier; Zingg, Walter; Pichard, Claude

2015-01-01

Indicators to predict healthcare-associated infections (HCAI) are scarce. Malnutrition is known to be associated with adverse outcomes in healthcare but its identification is time-consuming and rarely done in daily practice. This cross-sectional study assessed the association between dietary intake, nutritional risk, and the prevalence of HCAI, in a general hospital population. Dietary intake was assessed by dedicated dieticians on one day for all hospitalized patients receiving three meals per day. Nutritional risk was assessed using Nutritional Risk Screening (NRS)-2002, and defined as a NRS score ≥ 3. Energy needs were calculated using 110% of Harris-Benedict formula. HCAIs were diagnosed based on the Center for Disease Control criteria and their association with nutritional risk and measured energy intake was done using a multivariate logistic regression analysis. From 1689 hospitalised patients, 1024 and 1091 were eligible for the measurement of energy intake and nutritional risk, respectively. The prevalence of HCAI was 6.8%, and 30.1% of patients were at nutritional risk. Patients with HCAI were more likely identified with decreased energy intake (i.e. ≤ 70% of predicted energy needs) (30.3% vs. 14.5%, P = 0.002). The proportion of patients at nutritional risk was not significantly different between patients with and without HCAI (35.6% vs.29.7%, P = 0.28), respectively. Measured energy intake ≤ 70% of predicted energy needs (odds ratio: 2.26; 95% CI: 1.24 to 4.11, P = 0.008) and moderate severity of the disease (odds ratio: 3.38; 95% CI: 1.49 to 7.68, P = 0.004) were associated with HCAI in the multivariate analysis. Measured energy intake ≤ 70% of predicted energy needs is associated with HCAI in hospitalised patients. This suggests that insufficient dietary intake could be a risk factor of HCAI, without excluding reverse causality. Randomized trials are needed to assess whether improving energy intake in patients identified with decreased dietary intake could be a novel strategy for HCAI prevention.

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

An Improved Method to Control the Critical Parameters of a Multivariable Control System

NASA Astrophysics Data System (ADS)

Subha Hency Jims, P.; Dharmalingam, S.; Wessley, G. Jims John

2017-10-01

The role of control systems is to cope with the process deficiencies and the undesirable effect of the external disturbances. Most of the multivariable processes are highly iterative and complex in nature. Aircraft systems, Modern Power Plants, Refineries, Robotic systems are few such complex systems that involve numerous critical parameters that need to be monitored and controlled. Control of these important parameters is not only tedious and cumbersome but also is crucial from environmental, safety and quality perspective. In this paper, one such multivariable system, namely, a utility boiler has been considered. A modern power plant is a complex arrangement of pipework and machineries with numerous interacting control loops and support systems. In this paper, the calculation of controller parameters based on classical tuning concepts has been presented. The controller parameters thus obtained and employed has controlled the critical parameters of a boiler during fuel switching disturbances. The proposed method can be applied to control the critical parameters like elevator, aileron, rudder, elevator trim rudder and aileron trim, flap control systems of aircraft systems.

A Statistical Approach to Thermal Management of Data Centers Under Steady State and System Perturbations

PubMed Central

Haaland, Ben; Min, Wanli; Qian, Peter Z. G.; Amemiya, Yasuo

2011-01-01

Temperature control for a large data center is both important and expensive. On the one hand, many of the components produce a great deal of heat, and on the other hand, many of the components require temperatures below a fairly low threshold for reliable operation. A statistical framework is proposed within which the behavior of a large cooling system can be modeled and forecast under both steady state and perturbations. This framework is based upon an extension of multivariate Gaussian autoregressive hidden Markov models (HMMs). The estimated parameters of the fitted model provide useful summaries of the overall behavior of and relationships within the cooling system. Predictions under system perturbations are useful for assessing potential changes and improvements to be made to the system. Many data centers have far more cooling capacity than necessary under sensible circumstances, thus resulting in energy inefficiencies. Using this model, predictions for system behavior after a particular component of the cooling system is shut down or reduced in cooling power can be generated. Steady-state predictions are also useful for facility monitors. System traces outside control boundaries flag a change in behavior to examine. The proposed model is fit to data from a group of air conditioners within an enterprise data center from the IT industry. The fitted model is examined, and a particular unit is found to be underutilized. Predictions generated for the system under the removal of that unit appear very reasonable. Steady-state system behavior also is predicted well. PMID:22076026

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle.

PubMed

Belay, T K; Dagnachew, B S; Kowalski, Z M; Ådnøy, T

2017-08-01

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

OS090. Performance of candidate clinical and biochemical markers in screening early in pregnancy to detect women at high risk to develop preeclampsia.

PubMed

Forest, J-C; Massé, J; Bujold, E; Rousseau, F; Charland, M; Thériault, S; Lafond, J; Giguère, Y

2012-07-01

The advent of early preventive measures, such as low-dose aspirin targeting women at high risk of preeclampsia (PE), emphasizes the need for better detection. Despite the emergence of promising biochemical markers linked to the pathophysiological processes, systematic reviews have shown that, until now, no single tests fulfill the criteria set by WHO for biomarkers to screen for a disease. However, recent literature reveals that by combining various clinical, biophysical and biochemical markers into multivariate algorithms, one can envisage to estimate the risk of PE with a performance that would reach clinical utility and cost-effectiveness, but this remains to be demonstrated in various environments and health care settings. To investigate, in a prospective study, the clinical utility of candidate biomarkers and clinical data to detect, early in pregnancy, women at risk to develop PE and to propose a multivariate prediction algorithm combining clinical parameters to biochemical markers. 7929 pregnant women prospectively recruited at the first prenatal visit, provided blood samples, clinical and sociodemographic information. 214 pregnant women developed hypertensive disorders of pregnancy (HDP) of which 88 had PE (1.2%), including 44 with severe PE (0.6%). A nested case-control study was performed including for each case of HDP two normal pregnancies matched for maternal age, gestational age at recruitment, ethnicity, parity, and smoking status. Based on the literature we selected the most promising markers in a multivariate logistic regression model: mean arterial pressure (MAP), BMI, placental growth factor (PlGF), soluble Flt-1, inhibin A and PAPP-A. Biomarker results measured between 10-18 weeks gestation were expressed as multiples of the median. Medians were determined for each gestational week. When combined with MAP at the time of blood sampling and BMI at the beginning of pregnancy, the four biochemical markers discriminate normal pregnancies from those with HDP. At a 5% false positive rate, 37% of the affected pregnancies would have been detected. However, considering the prevalence of HDP in our population, the positive predictive value would have been only 15%. If all the predicted positive women would have been proposed a preventive intervention, only one out 6.7 women could have potentially benefited. In the case of severe PE, performance was not improved, sensitivity was the same, but the positive predictive value decreased to 3% (lower prevalence of severe PE). In our low-risk Caucasian population, neither individual candidate markers nor multivariate risk algorithm using an a priori combination of selected markers reached a performance justifying implementation. This also emphasizes the necessity to take into consideration characteristics of the population and environment influencing prevalence before promoting wide implementation of such screening strategies. In a perspective of personalized medicine, it appears more than ever mandatory to tailor recommendations for HDP screening according not only to individual but also to population characteristics. Copyright © 2012. Published by Elsevier B.V.

Training or non-surgical factors-what determines a good surgical performance? A randomised controlled trial.

PubMed

Lindlohr, Cornelia; Lefering, R; Saad, S; Heiss, M M; Pape-Köhler, C

2017-06-01

Acquiring laparoscopic skills is a necessity for every young surgeon. Whether it is a talent or a non-surgical skill that determines the surgical performance of an endoscopic operation has been discussed for years. In other disciplines aptitude testing has become the norm. Airlines, for example, have implemented assessments to test the natural aptitude of future pilots to predict their performance later on. In the medical field, especially surgery, there are no similar comparable tests implemented or even available. This study investigates the influence of potential factors that may predict the successful performance of a complex laparoscopic operation, such as the surgeon's age, gender or learning method. This study focussed 70 surgical trainees. It was designed as a secondary analysis of data derived from a 2 × 2 factorial randomised controlled trial of practical training and/or multimedia training (four groups) in an experimental exercise. Both before and then after the training sessions, the participating trainees performed a laparoscopic cholecystectomy in a pelvitrainer. Surgical performance was then evaluated using a modified objective structured assessment of technical skills (OSATS). Participants were classified as 'Skilled' (high score in the pre-test), 'Good Learner' (increase from pre- to post-test) or 'Others' based on the OSATS results. Based on the results of the recorded performance, the training methods as well as non-surgical skills were eventually evaluated in a univariate and in a multivariate analysis. In the pre-training performance 11 candidates were categorised as 'Skilled' (15.7%), 35 participants as 'Good Learners' (50.0%) and 24 participants were classified as 'Others'. The univariate analysis showed that the age, a residency in visceral surgery, and participation in a multimedia training were significantly associated with this grouping. Multivariate analyses revealed that residency in visceral surgery was the most predictive factor for the 'Skilled' participants (p = 0.059), and multimedia training was most predictive for the 'Good Learner' (p = 0.006). Participants in the group of 'Others' who were neither 'Skilled' nor improved in the training phase were younger (p = 0.011) and did not receive multimedia (p < 0.001) or practical (p = 0.025) training. The type of learning method has been shown to be the most effective factor to improve laparoscopic skills, with multimedia training proving to be more effective than practical training.

Gain-scheduling multivariable LPV control of an irrigation canal system.

PubMed

Bolea, Yolanda; Puig, Vicenç

2016-07-01

The purpose of this paper is to present a multivariable linear parameter varying (LPV) controller with a gain scheduling Smith Predictor (SP) scheme applicable to open-flow canal systems. This LPV controller based on SP is designed taking into account the uncertainty in the estimation of delay and the variation of plant parameters according to the operating point. This new methodology can be applied to a class of delay systems that can be represented by a set of models that can be factorized into a rational multivariable model in series with left/right diagonal (multiple) delays, such as, the case of irrigation canals. A multiple pool canal system is used to test and validate the proposed control approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Community integration following multidisciplinary rehabilitation for traumatic brain injury.

PubMed

Goranson, Tamara E; Graves, Roger E; Allison, Deborah; La Freniere, Ron

2003-09-01

To determine the extent to which participation in a multidisciplinary rehabilitation programme and patient characteristics predict improvement in community integration following mild-to-moderate traumatic brain injury (TBI). A non-randomized case-control study was conducted employing a pre-test-post-test multiple regression design. Archival data for 42 patients with mild-to-moderate TBI who completed the Community Integration Questionnaire (CIQ) at intake and again 6-18 months later were analysed. Half the sample participated in an intensive outpatient rehabilitation programme that provided multi-modal interventions, while the other half received no rehabilitation. The two groups were matched on age, education and time since injury. On the CIQ Home Integration scale, participation in rehabilitation and female gender predicted better outcome. On the Productivity scale, patients with a lower age at injury had better outcome. Outcome on both of these scales, as well as on the Social Integration scale, was predicted by the baseline pre-test score (initial severity). Overall, multidisciplinary rehabilitation appeared to increase personal independence. It is also concluded that: (1) multivariate analysis can reveal the relative importance of multiple predictors of outcome; (2) different predictors may predict different aspects of outcome; and (3) more sensitive and specific outcome measures are needed.

A Valuable Tool in Predicting Poor Outcome due to Sepsis in Pediatric Intensive Care Unit: Tp-e/QT Ratio.

PubMed

Ozdemir, Rahmi; Isguder, Rana; Kucuk, Mehmet; Karadeniz, Cem; Ceylan, Gokhan; Katipoglu, Nagehan; Yilmazer, Murat Muhtar; Yozgat, Yilmaz; Mese, Timur; Agin, Hasan

2016-10-01

To assess the feasibility of 12-lead electrocardiographic (ECG) measures such as P wave dispersion (PWd), QT interval, QT dispersion (QTd), Tp-e interval, Tp-e/QT and Tp-e/QTc ratio in predicting poor outcome in patients diagnosed with sepsis in pediatric intensive care unit (PICU). Ninety-three patients diagnosed with sepsis, severe sepsis or septic shock and 103 age- and sex-matched healthy children were enrolled into the study. PWd, QT interval, QTd, Tp-e interval and Tp-e/QT, Tp-e/QTc ratios were obtained from a 12-lead electrocardiogram. PWd, QTd, Tp-e interval and Tp-e/QT, Tp-e/QTc ratios were significantly higher in septic patients compared with the controls. During the study period, 41 patients had died. In multivariate logistic regression analyses, only Tp-e/QT ratio was found to be an independent predictor of mortality. The ECG measurements can predict the poor outcome in patients with sepsis. The Tp-e/QT ratio may be a valuable tool in predicting mortality for patients with sepsis in the PICU. © The Author [2016]. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Adolescents' Reported Reasons for Alcohol and Marijuana Use as Predictors of Substance Use and Problems in Adulthood*

PubMed Central

Patrick, Megan E.; Schulenberg, John E.; O'malley, Patrick M.; Johnston, Lloyd D.; Bachman, Jerald G.

2011-01-01

Objective: The aim of this study was to examine how reasons for substance use at age 18 relate to alcohol and marijuana use at ages 18 and 35 and to symptoms of alcohol use disorder and marijuana use disorder at age 35. Method: Bivariate correlation and multivariate regression analyses were conducted to examine the prediction of substance use and misuse by social/recreational, coping with negative affect, compulsive, and drug effect reasons for alcohol and marijuana use. Control variables included gender, race/ethnicity, parent education, and previous substance use (for age 35 outcomes). Results: Social/recreational, coping, and drug effect reasons for drinking predicted symptoms of alcohol use disorder 17 years later. Reasons for marijuana use were generally associated only with concurrent marijuana use; an exception was that drug effect reasons predicted marijuana use disorder at age 35. Conclusions: The long-term longitudinal predictive power of reasons for alcohol use (and, to a lesser extent, for marijuana use) suggests that adolescents' self-reported reasons, in particular those involving regulating emotions and experiences, may be early risk factors for continued use and misuse of substances into adulthood. PMID:21138717

Concurrent Anemia and Elevated C-Reactive Protein Predicts HIV Clinical Treatment Failure, Including Tuberculosis, After Antiretroviral Therapy Initiation

PubMed Central

Shivakoti, Rupak; Yang, Wei-Teng; Gupte, Nikhil; Berendes, Sima; Rosa, Alberto La; Cardoso, Sandra W.; Mwelase, Noluthando; Kanyama, Cecilia; Pillay, Sandy; Samaneka, Wadzanai; Riviere, Cynthia; Sugandhavesa, Patcharaphan; Santos, Brento; Poongulali, Selvamuthu; Tripathy, Srikanth; Bollinger, Robert C.; Currier, Judith S.; Tang, Alice M.; Semba, Richard D.; Christian, Parul; Campbell, Thomas B.; Gupta, Amita

2015-01-01

Background. Anemia is a known risk factor for clinical failure following antiretroviral therapy (ART). Notably, anemia and inflammation are interrelated, and recent studies have associated elevated C-reactive protein (CRP), an inflammation marker, with adverse human immunodeficiency virus (HIV) treatment outcomes, yet their joint effect is not known. The objective of this study was to assess prevalence and risk factors of anemia in HIV infection and to determine whether anemia and elevated CRP jointly predict clinical failure post-ART. Methods. A case-cohort study (N = 470 [236 cases, 234 controls]) was nested within a multinational randomized trial of ART efficacy (Prospective Evaluation of Antiretrovirals in Resource Limited Settings [PEARLS]). Cases were incident World Health Organization stage 3, 4, or death by 96 weeks of ART treatment (clinical failure). Multivariable logistic regression was used to determine risk factors for pre-ART (baseline) anemia (females: hemoglobin <12.0 g/dL; males: hemoglobin <13.0 g/dL). Association of anemia as well as concurrent baseline anemia and inflammation (CRP ≥10 mg/L) with clinical failure were assessed using multivariable Cox models. Results. Baseline anemia prevalence was 51% with 15% prevalence of concurrent anemia and inflammation. In analysis of clinical failure, multivariate-adjusted hazard ratios were 6.41 (95% confidence interval [CI], 2.82–14.57) for concurrent anemia and inflammation, 0.77 (95% CI, .37–1.58) for anemia without inflammation, and 0.45 (95% CI, .11–1.80) for inflammation without anemia compared to those without anemia and inflammation. Conclusions. ART-naive, HIV-infected individuals with concurrent anemia and inflammation are at particularly high risk of failing treatment, and understanding the pathogenesis could lead to new interventions. Reducing inflammation and anemia will likely improve HIV disease outcomes. Alternatively, concurrent anemia and inflammation could represent individuals with occult opportunistic infections in need of additional screening. PMID:25828994

Dissolution testing of isoniazid, rifampicin, pyrazinamide and ethambutol tablets using near-infrared spectroscopy (NIRS) and multivariate calibration.

PubMed

de Oliveira Neves, Ana Carolina; Soares, Gustavo Mesquita; de Morais, Stéphanie Cavalcante; da Costa, Fernanda Saadna Lopes; Porto, Dayanne Lopes; de Lima, Kássio Michell Gomes

2012-01-05

This work utilized the near-infrared spectroscopy (NIRS) and multivariate calibration to measure the percentage drug dissolution of four active pharmaceutical ingredients (APIs) (isoniazid, rifampicin, pyrazinamide and ethambutol) in finished pharmaceutical products produced in the Federal University of Rio Grande do Norte (Brazil). The conventional analytical method employed in quality control tests of the dissolution by the pharmaceutical industry is high-performance liquid chromatography (HPLC). The NIRS is a reliable method that offers important advantages for the large-scale production of tablets and for non-destructive analysis. NIR spectra of 38 samples (in triplicate) were measured using a Bomen FT-NIR 160 MB in the range 1100-2500nm. Each spectrum was the average of 50 scans obtained in the diffuse reflectance mode. The dissolution test, which was initially carried out in 900mL of 0.1N hydrochloric acid at 37±0.5°C, was used to determine the percentage a drug that dissolved from each tablet measured at the same time interval (45min) at pH 6.8. The measurement of the four API was performed by HPLC (Shimadzu, Japan) in the gradiente mode. The influence of various spectral pretreatments (Savitzky-Golay smoothing, Multiplicative Scatter Correction (MSC), and Savitzky-Golay derivatives) and multivariate analysis using the partial least squares (PLS) regression algorithm was calculated by the Unscrambler 9.8 (Camo) software. The correlation coefficient (R(2)) for the HPLC determination versus predicted values (NIRS) ranged from 0.88 to 0.98. The root-mean-square error of prediction (RMSEP) obtained from PLS models were 9.99%, 8.63%, 8.57% and 9.97% for isoniazid, rifampicin, ethambutol and pyrazinamide, respectively, indicating that the NIR method is an effective and non-destructive tool for measurement of drug dissolution from tablets. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Neuropsychological Testing Predicts Cerebrospinal Fluid Aβ in Mild Cognitive Impairment (MCI)

PubMed Central

Kandel, Benjamin M.; Avants, Brian B.; Gee, James C.; Arnold, Steven E.; Wolk, David A.

2015-01-01

Background Psychometric tests predict conversion of Mild Cognitive Impairment (MCI) to probable Alzheimer's Disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear. Objective To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by CSF Aβ1–42, in patients with MCI, as compared to neuroimaging biomarkers. Methods We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status. Results The 30-minute delayed recall score of the Rey Auditory Verbal Learning Test (AVLT) was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67±0.02 and odds ratio of 2.5±0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67±0.03, OR 3.2±1.2), followed by hippocampal volume (AUC 0.64±0.02,,OR 2.4±0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving an AUC of 0.68±0.03 (OR 3.38±1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC. Conclusion Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers of AD pathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD. PMID:25881908

Impact of different variables on the outcome of patients with clinically confined prostate carcinoma: prediction of pathologic stage and biochemical failure using an artificial neural network.

PubMed

Ziada, A M; Lisle, T C; Snow, P B; Levine, R F; Miller, G; Crawford, E D

2001-04-15

The advent of advanced computing techniques has provided the opportunity to analyze clinical data using artificial intelligence techniques. This study was designed to determine whether a neural network could be developed using preoperative prognostic indicators to predict the pathologic stage and time of biochemical failure for patients who undergo radical prostatectomy. The preoperative information included TNM stage, prostate size, prostate specific antigen (PSA) level, biopsy results (Gleason score and percentage of positive biopsy), as well as patient age. All 309 patients underwent radical prostatectomy at the University of Colorado Health Sciences Center. The data from all patients were used to train a multilayer perceptron artificial neural network. The failure rate was defined as a rise in the PSA level > 0.2 ng/mL. The biochemical failure rate in the data base used was 14.2%. Univariate and multivariate analyses were performed to validate the results. The neural network statistics for the validation set showed a sensitivity and specificity of 79% and 81%, respectively, for the prediction of pathologic stage with an overall accuracy of 80% compared with an overall accuracy of 67% using the multivariate regression analysis. The sensitivity and specificity for the prediction of failure were 67% and 85%, respectively, demonstrating a high confidence in predicting failure. The overall accuracy rates for the artificial neural network and the multivariate analysis were similar. Neural networks can offer a convenient vehicle for clinicians to assess the preoperative risk of disease progression for patients who are about to undergo radical prostatectomy. Continued investigation of this approach with larger data sets seems warranted. Copyright 2001 American Cancer Society.

The Cancer of the Prostate Risk Assessment (CAPRA) score predicts biochemical recurrence in intermediate-risk prostate cancer treated with external beam radiotherapy (EBRT) dose escalation or low-dose rate (LDR) brachytherapy.

PubMed

Krishnan, Vimal; Delouya, Guila; Bahary, Jean-Paul; Larrivée, Sandra; Taussky, Daniel

2014-12-01

To study the prognostic value of the University of California, San Francisco Cancer of the Prostate Risk Assessment (CAPRA) score to predict biochemical failure (bF) after various doses of external beam radiotherapy (EBRT) and/or permanent seed low-dose rate (LDR) prostate brachytherapy (PB). We retrospectively analysed 345 patients with intermediate-risk prostate cancer, with PSA levels of 10-20 ng/mL and/or Gleason 7 including 244 EBRT patients (70.2-79.2 Gy) and 101 patients treated with LDR PB. The minimum follow-up was 3 years. No patient received primary androgen-deprivation therapy. bF was defined according to the Phoenix definition. Cox regression analysis was used to estimate the differences between CAPRA groups. The overall bF rate was 13% (45/345). The CAPRA score, as a continuous variable, was statistically significant in multivariate analysis for predicting bF (hazard ratio [HR] 1.37, 95% confidence interval [CI] 1.10-1.72, P = 0.006). There was a trend for a lower bF rate in patients treated with LDR PB when compared with those treated by EBRT ≤ 74 Gy (HR 0.234, 95% CI 0.05-1.03, P = 0.055) in multivariate analysis. In the subgroup of patients with a CAPRA score of 3-5, CAPRA remained predictive of bF as a continuous variable (HR 1.51, 95% CI 1.01-2.27, P = 0.047) in multivariate analysis. The CAPRA score is useful for predicting biochemical recurrence in patients treated for intermediate-risk prostate cancer with EBRT or LDR PB. It could help in treatment decisions. © 2013 The Authors. BJU International © 2013 BJU International.

Cardiovascular mortality prediction in veterans with arm exercise vs pharmacologic myocardial perfusion imaging.

PubMed

Martin, Wade H; Xian, Hong; Chandiramani, Pooja; Bainter, Emily; Klein, Andrew J P

2015-08-01

No data exist comparing outcome prediction from arm exercise vs pharmacologic myocardial perfusion imaging (MPI) stress test variables in patients unable to perform treadmill exercise. In this retrospective study, 2,173 consecutive lower extremity disabled veterans aged 65.4 ± 11.0years (mean ± SD) underwent either pharmacologic MPI (1730 patients) or arm exercise stress tests (443 patients) with MPI (n = 253) or electrocardiography alone (n = 190) between 1997 and 2002. Cox multivariate regression models and reclassification analysis by integrated discrimination improvement (IDI) were used to characterize stress test and MPI predictors of cardiovascular mortality at ≥10-year follow-up after inclusion of significant demographic, clinical, and other variables. Cardiovascular death occurred in 561 pharmacologic MPI and 102 arm exercise participants. Multivariate-adjusted cardiovascular mortality was predicted by arm exercise resting metabolic equivalents (hazard ratio [HR] 0.52, 95% CI 0.39-0.69, P < .001), 1-minute heart rate recovery (HR 0.61, 95% CI 0.44-0.86, P < .001), and pharmacologic and arm exercise delta (peak-rest) heart rate (both P < .001). Only an abnormal arm exercise MPI prognosticated cardiovascular death by multivariate Cox analysis (HR 1.98, 95% CI 1.04-3.77, P < .05). Arm exercise MPI defect number, type, and size provided IDI over covariates for prediction of cardiovascular mortality (IDI = 0.074-0.097). Only pharmacologic defect size prognosticated cardiovascular mortality (IDI = 0.022). Arm exercise capacity, heart rate recovery, and pharmacologic and arm exercise heart rate responses are robust predictors of cardiovascular mortality. Arm exercise MPI results are equivalent and possibly superior to pharmacologic MPI for cardiovascular mortality prediction in patients unable to perform treadmill exercise. Published by Elsevier Inc.

Parenting, relational aggression, and borderline personality features: associations over time in a Russian longitudinal sample.

PubMed

Nelson, David A; Coyne, Sarah M; Swanson, Savannah M; Hart, Craig H; Olsen, Joseph A

2014-08-01

Crick, Murray-Close, and Woods (2005) encouraged the study of relational aggression as a developmental precursor to borderline personality features in children and adolescents. A longitudinal study is needed to more fully explore this association, to contrast potential associations with physical aggression, and to assess generalizability across various cultural contexts. In addition, parenting is of particular interest in the prediction of aggression or borderline personality disorder. Early aggression and parenting experiences may differ in their long-term prediction of aggression or borderline features, which may have important implications for early intervention. The currrent study incorporated a longitudinal sample of preschool children (84 boys, 84 girls) living in intact, two-parent biological households in Voronezh, Russia. Teachers provided ratings of children's relational and physical aggression in preschool. Mothers and fathers also self-reported their engagement in authoritative, authoritarian, permissive, and psychological controlling forms of parenting with their preschooler. A decade later, 70.8% of the original child participants consented to a follow-up study in which they completed self-reports of relational and physical aggression and borderline personality features. The multivariate results of this study showed that preschool relational aggression in girls predicted adolescent relational aggression. Preschool aversive parenting (i.e., authoritarian, permissive, and psychologically controlling forms) significantly predicted aggression and borderline features in adolescent females. For adolescent males, preschool authoritative parenting served as a protective factor against aggression and borderline features, whereas authoritarian parenting was a risk factor for later aggression.

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Multivariable control of a twin lift helicopter system using the LQG/LTR design methodology

NASA Technical Reports Server (NTRS)

Rodriguez, A. A.; Athans, M.

1986-01-01

Guidelines for developing a multivariable centralized automatic flight control system (AFCS) for a twin lift helicopter system (TLHS) are presented. Singular value ideas are used to formulate performance and stability robustness specifications. A linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) design is obtained and evaluated.

Controlled Multivariate Evaluation of Open Education: Application of a Critical Model.

ERIC Educational Resources Information Center

Sewell, Alan F.; And Others

This paper continues previous reports of a controlled multivariate evaluation of a junior high school open-education program. A new method of estimating program objectives and implementation is presented, together with the nature and degree of obtained student outcomes. Open-program students were found to approve more highly of their learning…

Model transformations for state-space self-tuning control of multivariable stochastic systems

NASA Technical Reports Server (NTRS)

Shieh, Leang S.; Bao, Yuan L.; Coleman, Norman P.

1988-01-01

The design of self-tuning controllers for multivariable stochastic systems is considered analytically. A long-division technique for finding the similarity transformation matrix and transforming the estimated left MFD to the right MFD is developed; the derivation is given in detail, and the procedures involved are briefly characterized.

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.

Protein intakes are associated with reduced length of stay: a comparison between Enhanced Recovery After Surgery (ERAS) and conventional care after elective colorectal surgery.

PubMed

Yeung, Sophia E; Hilkewich, Leslee; Gillis, Chelsia; Heine, John A; Fenton, Tanis R

2017-07-01

Background: Protein can modulate the surgical stress response and postoperative catabolism. Enhanced Recovery After Surgery (ERAS) protocols are evidence-based care bundles that reduce morbidity. Objective: In this study, we compared protein adequacy as well as energy intakes, gut function, clinical outcomes, and how well nutritional variables predict length of hospital stay (LOS) in patients receiving ERAS protocols and conventional care. Design: We conducted a prospective cohort study in adult elective colorectal resection patients after conventional ( n = 46) and ERAS ( n = 69) care. Data collected included preoperative Malnutrition Screening Tool (MST) score, 3-d food records, postoperative nausea, LOS, and complications. Multivariable regression analysis assessed whether low protein intakes and the MST score were predictive of LOS. Results: Total protein intakes were significantly higher in the ERAS group due to the inclusion of oral nutrition supplements (conventional group: 0.33 g · kg -1 · d -1 ; ERAS group: 0.54 g · kg -1 · d -1 ; P < 0.02). This group difference in protein intake was maintained in a multivariable model that controlled for differences between baseline and surgical variables ( P = 0.001). Oral food intake did not differ between the 2 groups. The ERAS group had shorter LOS ( P = 0.049) and fewer total infectious complications ( P = 0.01). Nausea was a predictor of protein intake. Nutrition variables were independent predictors of earlier discharge after potential confounders were controlled for. Each unit increase in preoperative MST score predicted longer LOSs of 2.5 d (95% CI: 1.5, 3.5 d; P < 0.001), and the consumption of ≥60% of protein requirements during the first 3 d of hospitalization was associated with a shorter LOS of 4.4 d (95% CI: -6.8, -2.0 d; P < 0.001). Conclusions: ERAS patients consumed more protein due to the inclusion of oral nutrition supplements. However, total protein intake remained inadequate to meet recommendations. Consumption of ≥60% protein needs after surgery and MST scores were independent predictors of LOS. This trial was registered at clinicaltrials.gov as NCT02940665. © 2017 American Society for Nutrition.

Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the AddNeuroMed cohort

PubMed Central

Aguilar, Carlos; Muehlboeck, J-Sebastian; Mecocci, Patrizia; Vellas, Bruno; Tsolaki, Magda; Kloszewska, Iwona; Soininen, Hilkka; Lovestone, Simon; Wahlund, Lars-Olof; Simmons, Andrew; Westman, Eric

2014-01-01

Cross sectional studies of patients at risk of developing Alzheimer disease (AD) have identified several brain regions known to be prone to degeneration suitable as biomarkers, including hippocampal, ventricular, and whole brain volume. The aim of this study was to longitudinally evaluate an index based on morphometric measures derived from MRI data that could be used for classification of AD and healthy control subjects, as well as prediction of conversion from mild cognitive impairment (MCI) to AD. Patients originated from the AddNeuroMed project at baseline (119 AD, 119 MCI, 110 controls (CTL)) and 1-year follow-up (62 AD, 73 MCI, 79 CTL). Data consisted of 3D T1-weighted MR images, demographics, MMSE, ADAS-Cog, CERAD and CDR scores, and APOE e4 status. We computed an index using a multivariate classification model (AD vs. CTL), using orthogonal partial least squares to latent structures (OPLS). Sensitivity, specificity and AUC were determined. Performance of the classifier (AD vs. CTL) was high at baseline (10-fold cross-validation, 84% sensitivity, 91% specificity, 0.93 AUC) and at 1-year follow-up (92% sensitivity, 74% specificity, 0.93 AUC). Predictions of conversion from MCI to AD were good at baseline (77% of MCI converters) and at follow-up (91% of MCI converters). MCI carriers of the APOE e4 allele manifested more atrophy and presented a faster cognitive decline when compared to non-carriers. The derived index demonstrated a steady increase in atrophy over time, yielding higher accuracy in prediction at the time of clinical conversion. Neuropsychological tests appeared less sensitive to changes over time. However, taking the average of the two time points yielded better correlation between the index and cognitive scores as opposed to using cross-sectional data only. Thus, multivariate classification seemed to detect patterns of AD changes before conversion from MCI to AD and including longitudinal information is of great importance. PMID:25071554

Long-term effects of psychosocial work stress in midlife on health functioning after labor market exit--results from the GAZEL study.

PubMed

Wahrendorf, Morten; Sembajwe, Grace; Zins, Marie; Berkman, Lisa; Goldberg, Marcel; Siegrist, Johannes

2012-07-01

To study long-term effects of psychosocial work stress in mid-life on health functioning after labor market exit using two established work stress models. In the frame of the prospective French Gazel cohort study, data on psychosocial work stress were assessed using the full questionnaires measuring the demand-control-support model (in 1997 and 1999) and the effort-reward imbalance model (in 1998). In 2007, health functioning was assessed, using the Short Form 36 mental and physical component scores. Multivariate regressions were calculated to predict health functioning in 2007, controlling for age, gender, social position, and baseline self-perceived health. Consistent effects of both work stress models and their single components on mental and physical health functioning during retirement were observed. Effects remained significant after adjustment including baseline self-perceived health. Whereas the predictive power of both work stress models was similar in the case of the physical composite score, in the case of the mental health score, values of model fit were slightly higher for the effort-reward imbalance model (R(2): 0.13) compared with the demand-control model (R²: 0.11). Findings underline the importance of working conditions in midlife not only for health in midlife but also for health functioning after labor market exit.

Does Tobacco-Control Mass Media Campaign Exposure Prevent Relapse Among Recent Quitters?

PubMed Central

Bowe, Steven J.; Durkin, Sarah J.; Yong, Hua-Hie; Spittal, Matthew J.; Simpson, Julie A.; Borland, Ron

2013-01-01

Objective: To determine whether greater mass media campaign exposure may assist recent quitters to avoid relapse. Method: Using date of data collection and postcode, media market estimates of televised tobacco-control advertising exposure measured by gross ratings points (GRPs) were merged with a replenished cohort study of 443 Australians who had quit in the past year. Participants’ demographic and smoking characteristics prior to quitting, and advertising exposure in the period after quitting, were used to predict relapse 1 year later. Results: In multivariate analysis, each increase in exposure of 100 GRPs (i.e., 1 anti-smoking advertisement) in the three-month period after the baseline quit was associated with a 5% increase in the odds of not smoking at follow-up (OR = 1.05, 95% CI 1.02–1.07, p < 0.001). This relationship was linear and unmodified by length of time quit prior to the baseline interview. At the mean value of 1081 GRPs in the 3 months after the baseline-quit interview, the predicted probability of being quit at follow-up was 52%, whereas it was 41% for the minimum (0) and 74% for the maximum (3,541) GRPs. Conclusion: Greater exposure to tobacco-control mass media campaigns may reduce the likelihood of relapse among recent quitters. PMID:22949574

Persistent Hypogonadotropic Hypogonadism in Men After Severe Traumatic Brain Injury: Temporal Hormone Profiles and Outcome Prediction.

PubMed

Barton, David J; Kumar, Raj G; McCullough, Emily H; Galang, Gary; Arenth, Patricia M; Berga, Sarah L; Wagner, Amy K

2016-01-01

To (1) examine relationships between persistent hypogonadotropic hypogonadism (PHH) and long-term outcomes after severe traumatic brain injury (TBI); and (2) determine whether subacute testosterone levels can predict PHH. Level 1 trauma center at a university hospital. Consecutive sample of men with severe TBI between 2004 and 2009. Prospective cohort study. Post-TBI blood samples were collected during week 1, every 2 weeks until 26 weeks, and at 52 weeks. Serum hormone levels were measured, and individuals were designated as having PHH if 50% or more of samples met criteria for hypogonadotropic hypogonadism. At 6 and 12 months postinjury, we assessed global outcome, disability, functional cognition, depression, and quality of life. We recruited 78 men; median (interquartile range) age was 28.5 (22-42) years. Thirty-four patients (44%) had PHH during the first year postinjury. Multivariable regression, controlling for age, demonstrated PHH status predicted worse global outcome scores, more disability, and reduced functional cognition at 6 and 12 months post-TBI. Two-step testosterone screening for PHH at 12 to 16 weeks postinjury yielded a sensitivity of 79% and specificity of 100%. PHH status in men predicts poor outcome after severe TBI, and PHH can accurately be predicted at 12 to 16 weeks.

Novel hyperspectral prediction method and apparatus

NASA Astrophysics Data System (ADS)

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

2009-05-01

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

Persistent hypogonadotropic hypogonadism in men after severe traumatic brain injury: temporal hormone profiles and outcome prediction

PubMed Central

Barton, David J.; Kumar, Raj G.; McCullough, Emily H.; Galang, Gary; Arenth, Patricia M.; Berga, Sarah L.; Wagner, Amy K.

2015-01-01

Objective (1) Examine relationships between persistent hypogonadotropic hypogonadism (PHH) and long-term outcomes after severe traumatic brain injury (TBI); (2) determine if sub-acute testosterone levels can predict PHH. Setting Level 1 trauma center at a university hospital. Participants Consecutive sample of men with severe TBI between 2004 and 2009. Design Prospective cohort study. Main Measures Post-TBI blood samples were collected during week 1, every 2 weeks until 26 weeks, and at 52 weeks. Serum hormone levels were measured, and individuals were designated as having PHH if ≥50% of samples met criteria for hypogonadotropic hypogonadism. At 6 and 12 months post-injury, we assessed global outcome, disability, functional cognition, depression, and quality-of-life. Results We recruited 78 men; median (IQR) age was 28.5 (22–42) years. 34 patients (44%) had PHH during the first year post-injury. Multivariable regression, controlling for age, demonstrated PHH status predicted worse global outcome scores, more disability, and reduced functional cognition at 6 and 12 months post-TBI. Two-step testosterone screening for PHH at 12–16 weeks post-injury yielded a sensitivity of 79% and specificity of 100%. Conclusion PHH status in men predicts poor outcome after severe TBI, and PHH can accurately be predicted at 12–16 weeks. PMID:26360007

Infestation by Triatoma pallidipennis (Hemiptera: Reduviidae: Triatominae) is associated with housing characteristics in rural Mexico.

PubMed

Cohen, Justin M; Wilson, Mark L; Cruz-Celis, Adriana; Ordoñez, Rosalinda; Ramsey, Janine M

2006-11-01

Long-term control of Chagas disease requires not only interruption of the human transmission cycle of Trypanosoma cruzi Schyzotrypanum, Chagas, 1909 by controlling its domestic triatomine vectors but also surveillance to prevent reinfestation of residences from sylvatic or persistent peridomestic populations. Although a number of potential risk factors for infestation have been implicated in previous studies, the explanatory power of resulting models has been low. Two years after cessation of triatomine vector control efforts in the town of Chalcatzingo, Morelos, 78 environmental, socioecological, and spatial variables were analyzed for association with infestation by Triatoma pallidipennis Stal 1872 (Hemiptera: Reduviidae: Triatominae), the principal vector of T. cruzi. We studied 712 residences in this rural community to identify specific intradomestic and peridomestic risk factors that predicted infestation with T. pallidipennis. From numerous characteristics that were identified as correlated with infestation, we derived multivariate logistic regression models to predict residences that were more or less likely to be infested with T. pallidipennis. The most important risk factors for infestation included measurements of house age, upkeep, and spatial location in the town. The effects of certain risk factors on infestation were found to be modified by spatial characteristics of residences. The results of this study provide new information regarding risk factors for infestation by T. pallidipennis that may aid in designing sustainable disease control programs in rural Mexico.

Application of Maxent Multivariate Analysis to Define Climate-Change Effects on Species Distributions and Changes

DTIC Science & Technology

2014-09-01

approaches. Ecological Modelling Volume 200, Issues 1–2, 10, pp 1–19. Buhlmann, Kurt A ., Thomas S.B. Akre , John B. Iverson, Deno Karapatakis, Russell A ...statistical multivariate analysis to define the current and projected future range probability for species of interest to Army land managers. A software...15 Figure 4. RCW omission rate and predicted area as a function of the cumulative threshold

Early experiences building a software quality prediction model

NASA Technical Reports Server (NTRS)

Agresti, W. W.; Evanco, W. M.; Smith, M. C.

1990-01-01

Early experiences building a software quality prediction model are discussed. The overall research objective is to establish a capability to project a software system's quality from an analysis of its design. The technical approach is to build multivariate models for estimating reliability and maintainability. Data from 21 Ada subsystems were analyzed to test hypotheses about various design structures leading to failure-prone or unmaintainable systems. Current design variables highlight the interconnectivity and visibility of compilation units. Other model variables provide for the effects of reusability and software changes. Reported results are preliminary because additional project data is being obtained and new hypotheses are being developed and tested. Current multivariate regression models are encouraging, explaining 60 to 80 percent of the variation in error density of the subsystems.

On measures of association among genetic variables

PubMed Central

Gianola, Daniel; Manfredi, Eduardo; Simianer, Henner

2012-01-01

Summary Systems involving many variables are important in population and quantitative genetics, for example, in multi-trait prediction of breeding values and in exploration of multi-locus associations. We studied departures of the joint distribution of sets of genetic variables from independence. New measures of association based on notions of statistical distance between distributions are presented. These are more general than correlations, which are pairwise measures, and lack a clear interpretation beyond the bivariate normal distribution. Our measures are based on logarithmic (Kullback-Leibler) and on relative ‘distances’ between distributions. Indexes of association are developed and illustrated for quantitative genetics settings in which the joint distribution of the variables is either multivariate normal or multivariate-t, and we show how the indexes can be used to study linkage disequilibrium in a two-locus system with multiple alleles and present applications to systems of correlated beta distributions. Two multivariate beta and multivariate beta-binomial processes are examined, and new distributions are introduced: the GMS-Sarmanov multivariate beta and its beta-binomial counterpart. PMID:22742500

Pharmacodynamics and effectiveness of topical nitroglycerin at lowering blood pressure during autonomic dysreflexia.

PubMed

Solinsky, R; Bunnell, A E; Linsenmeyer, T A; Svircev, J N; Engle, A; Burns, S P

2017-10-01

Secondary analysis of prospectively collected observational data assessing the safety of an autonomic dysreflexia (AD) management protocol. To estimate the time to onset of action, time to full clinical effect (sustained systolic blood pressure (SBP) <160 mm Hg) and effectiveness of nitroglycerin ointment at lowering blood pressure for patients with spinal cord injuries experiencing AD. US Veterans Affairs inpatient spinal cord injury (SCI) unit. Episodes of AD recalcitrant to nonpharmacologic interventions that were given one to two inches of 2% topical nitroglycerin ointment were recorded. Pharmacodynamics as above and predictive characteristics (through a mixed multivariate logistic regression model) were calculated. A total of 260 episodes of pharmacologically managed AD were recorded in 56 individuals. Time to onset of action for nitroglycerin ointment was 9-11 min. Time to full clinical effect was 14-20 min. Topical nitroglycerin controlled SBP <160 mm Hg in 77.3% of pharmacologically treated AD episodes with the remainder requiring additional antihypertensive medications. A multivariate logistic regression model was unable to identify statistically significant factors to predict which patients would respond to nitroglycerin ointment (odds ratios 95% confidence intervals 0.29-4.93). The adverse event rate, entirely attributed to hypotension, was 3.6% with seven of the eight events resolving with close observation alone and one episode requiring normal saline. Nitroglycerin ointment has a rapid onset of action and time to full clinical effect with high efficacy and relatively low adverse event rate for patients with SCI experiencing AD.

The impact of HIV infection and socioeconomic factors on the incidence of gonorrhea: A county-level, US-wide analysis.

PubMed

Andreatos, Nikolaos; Grigoras, Christos; Shehadeh, Fadi; Pliakos, Elina Eleftheria; Stoukides, Georgianna; Port, Jenna; Flokas, Myrto Eleni; Mylonakis, Eleftherios

2017-01-01

Gonorrhea is the second most commonly reported identifiable disease in the United States (U.S.). Importantly, more than 25% of gonorrheal infections demonstrate antibiotic resistance, leading the Centers for Disease Control and Prevention (CDC) to classify gonorrhea as an "urgent threat". We examined the association of gonorrhea infection rates with the incidence of HIV and socioeconomic factors. A county-level multivariable model was then constructed. Multivariable analysis demonstrated that HIV incidence [Coefficient (Coeff): 1.26, 95% Confidence Interval (CI): 0.86, 1.66, P<0.001] exhibited the most powerful independent association with the incidence of gonorrhea and predicted 40% of the observed variation in gonorrhea infection rates. Sociodemographic factors like county urban ranking (Coeff: 0.12, 95% CI: 0.03, 0.20, P = 0.005), percentage of women (Coeff: 0.41, 95% CI: 0.28, 0.53, P<0.001) and percentage of individuals under the poverty line (Coeff: 0.45, 95% CI: 0.32, 0.57, P<0.001) exerted a secondary impact. A regression model that incorporated these variables predicted 56% of the observed variation in gonorrhea incidence (Pmodel<0.001, R2 model = 0.56). Gonorrhea and HIV infection exhibited a powerful correlation thus emphasizing the benefits of comprehensive screening for sexually transmitted infections (STIs) and the value of pre-exposure prophylaxis for HIV among patients visiting an STI clinic. Furthermore, sociodemographic factors also impacted gonorrhea incidence, thus suggesting another possible focus for public health initiatives.

A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System.

PubMed

Frohlich, Holger; Claes, Kasper; De Wolf, Catherine; Van Damme, Xavier; Michel, Anne

2018-05-01

Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Noldus Catwalk system, in which footprints are automatically captured and stored. We present a - to our knowledge - first machine learning based approach for the Catwalk system, which comprises a step decomposition, definition and extraction of meaningful features, multivariate step sequence alignment, feature selection, and training of different classifiers (gradient boosting machine, random forest, and elastic net). Using animal-wise leave-one-out cross validation we demonstrate that with our method we can reliable separate movement patterns of a putative Parkinson's disease animal model and several control groups. Furthermore, we show that we can predict the time point after and the type of different brain lesions and can even forecast the brain region, where the intervention was applied. We provide an in-depth analysis of the features involved into our classifiers via statistical techniques for model interpretation. A machine learning method for automated analysis of data from the Noldus Catwalk system was established. Our works shows the ability of machine learning to discriminate pharmacologically relevant animal groups based on their walking behavior in a multivariate manner. Further interesting aspects of the approach include the ability to learn from past experiments, improve with more data arriving and to make predictions for single animals in future studies.

Evaluation of an F100 multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Soeder, J. F.; Skira, C.

1977-01-01

The control evaluated has been designed for the F100-PW-100 turbofan engine. The F100 engine represents the current state-of-the-art in aircraft gas turbine technology. The control makes use of a multivariable, linear quadratic regulator. The evaluation procedure employed utilized a real-time hybrid computer simulation of the F100 engine and an implementation of the control logic on the NASA LeRC digital computer/controller. The results of the evaluation indicated that the control logic and its implementation will be capable of controlling the engine throughout its operating range.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Patterns and Predictors of Language and Literacy Abilities 4-10 Years in the Longitudinal Study of Australian Children

PubMed Central

Zubrick, Stephen R.; Taylor, Catherine L.; Christensen, Daniel

2015-01-01

Aims Oral language is the foundation of literacy. Naturally, policies and practices to promote children’s literacy begin in early childhood and have a strong focus on developing children’s oral language, especially for children with known risk factors for low language ability. The underlying assumption is that children’s progress along the oral to literate continuum is stable and predictable, such that low language ability foretells low literacy ability. This study investigated patterns and predictors of children’s oral language and literacy abilities at 4, 6, 8 and 10 years. The study sample comprised 2,316 to 2,792 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Six developmental patterns were observed, a stable middle-high pattern, a stable low pattern, an improving pattern, a declining pattern, a fluctuating low pattern, and a fluctuating middle-high pattern. Most children (69%) fit a stable middle-high pattern. By contrast, less than 1% of children fit a stable low pattern. These results challenged the view that children’s progress along the oral to literate continuum is stable and predictable. Findings Multivariate logistic regression was used to investigate risks for low literacy ability at 10 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. Predictors were modelled as risk variables with the lowest level of risk as the reference category. In the multivariate model, substantial risks for low literacy ability at 10 years, in order of descending magnitude, were: low school readiness, Aboriginal and/or Torres Strait Islander status and low language ability at 8 years. Moderate risks were high temperamental reactivity, low language ability at 4 years, and low language ability at 6 years. The following risk factors were not statistically significant in the multivariate model: Low maternal consistency, low family income, health care card, child not read to at home, maternal smoking, maternal education, family structure, temperamental persistence, and socio-economic area disadvantage. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude did not do particularly well in predicting low literacy ability at 10 years. PMID:26352436

Reporting and Methodology of Multivariable Analyses in Prognostic Observational Studies Published in 4 Anesthesiology Journals: A Methodological Descriptive Review.

PubMed

Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric

2015-10-01

Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate <36% (i.e., the 25th percentile), with some of them not identified in the STROBE checklist: blinded evaluation of the outcome (11.9%), reason for sample size (15.1%), handling of missing data (36.0%), assessment of colinearity (17.4%), assessment of interactions (13.9%), and calibration (34.9%). When reported, a few methodological shortcomings were observed, both in explanatory and predictive studies, such as an insufficient number of events of the outcome (44.6%), exclusion of cases with missing data (93.6%), or categorization of continuous variables (65.1%.). The reporting of multivariable analysis was fairly good and could be further improved by checking reporting guidelines and EQUATOR Network website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.

Is the bronchodilator test an useful tool to measure asthma control?

PubMed

Ferrer Galván, Marta; Javier Alvarez Gutiérrez, Francisco; Romero Falcón, Auxiliadora; Romero Romero, Beatriz; Sáez, Antonia; Medina Gallardo, Juan Francisco

2017-05-01

Asthma control includes the control of symptoms and future risk. We sought to evaluate the usefulness of the degree of spirometric reversibility of the forced expiratory volume in one second (FEV 1 ) as the target parameter of control. Patients with bronchial asthma were followed up for one year. The clinical, functional, inflammatory and control parameters of the asthma were collected. The area under the curve (AUC) was estimated to establish the cutoff point of the post-bronchodilator FEV 1 reversibility in relation to non-control asthma. In the univariate analysis, the differences between groups were studied based on the degree of estimated reversibility. Factors with a significance <0.1 were included in the multivariate analysis by binary logistic regression. A total of 407 patients with a mean age of 38.1 ± 16.7 years were included. When the patients were grouped into controlled and non-controlled groups, compared with post-bronchodilator FEV 1 reversibility, the cutoff point obtained for the non-controlled group was ≥10% (sensitivity: 65.8%, specificity: 48.4%, positive predictive value: 69.5%, and AUC: 0.619 [0.533-0.700], p < 0.01). In the year-long follow-up of this group (post-bronchodilator FEV 1 ≥10), an increased use of relief medication was observed, along with a significantly progressive drop in post-bronchodilator FEV 1 and post-bronchodilator FEV 1 /FVC (forced expiratory volume in one second/forced vital capacity). Spirometric reversibility can be useful in assessing control in asthmatic patients and can predict future risk parameters. The cutoff point related to the non-control of asthma found in our work was ≥10%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Pareidolias in REM Sleep Behavior Disorder: A Possible Predictive Marker of Lewy Body Diseases?

PubMed

Sasai-Sakuma, Taeko; Nishio, Yoshiyuki; Yokoi, Kayoko; Mori, Etsuro; Inoue, Yuichi

2017-02-01

To investigate conditions and clinical significance of pareidolias in patients with idiopathic rapid eyemovent (REM) sleep behavior disorder (iRBD). This cross-sectional study examined 202 patients with iRBD (66.8 ± 8.0 yr, 58 female) and 46 healthy control subjects (64.7 ± 5.8 years, 14 females). They underwent the Pareidolia test, a newly developed instrument for evoking pareidolias, video polysomnography, olfactory tests, and Addenbrooke's cognitive examination-revised. Results show that 53.5% of iRBD patients exhibited one or more pareidolic responses: The rate was higher than control subjects showed (21.7%). The pictures evoking pareidolic responses were more numerous for iRBD patients than for control subjects (1.2 ± 1.8 vs. 0.4 ± 0.8, p < .001). Subgroup analyses revealed that iRBD patients with pareidolic responses had higher amounts of REM sleep without atonia (RWA), with lower sleep efficiency, lower cognitive function, and older age than subjects without pareidolic responses. Results of multivariate analyses show the number of pareidolic responses as a factor associated with decreased cognitive function in iRBD patients with better predictive accuracy. Morbidity length and severity of iRBD, olfactory function, and the amount of RWA were not factors associated with better predictive accuracy. Half or more of the iRBD patients showed pareidolic responses. The responses were proven to be associated more intimately with their cognitive decline than clinical or physiological variables related to RBD. Pareidolias in iRBD are useful as a predictive marker of future development of Lewy body diseases. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Tuning algorithms for fractional order internal model controllers for time delay processes

NASA Astrophysics Data System (ADS)

Muresan, Cristina I.; Dutta, Abhishek; Dulf, Eva H.; Pinar, Zehra; Maxim, Anca; Ionescu, Clara M.

2016-03-01

This paper presents two tuning algorithms for fractional-order internal model control (IMC) controllers for time delay processes. The two tuning algorithms are based on two specific closed-loop control configurations: the IMC control structure and the Smith predictor structure. In the latter, the equivalency between IMC and Smith predictor control structures is used to tune a fractional-order IMC controller as the primary controller of the Smith predictor structure. Fractional-order IMC controllers are designed in both cases in order to enhance the closed-loop performance and robustness of classical integer order IMC controllers. The tuning procedures are exemplified for both single-input-single-output as well as multivariable processes, described by first-order and second-order transfer functions with time delays. Different numerical examples are provided, including a general multivariable time delay process. Integer order IMC controllers are designed in each case, as well as fractional-order IMC controllers. The simulation results show that the proposed fractional-order IMC controller ensures an increased robustness to modelling uncertainties. Experimental results are also provided, for the design of a multivariable fractional-order IMC controller in a Smith predictor structure for a quadruple-tank system.

Anxiety after completion of treatment for early-stage breast cancer: a systematic review to identify candidate predictors and evaluate multivariable model development.

PubMed

Harris, Jenny; Cornelius, Victoria; Ream, Emma; Cheevers, Katy; Armes, Jo

2017-07-01

The purpose of this review was to identify potential candidate predictors of anxiety in women with early-stage breast cancer (BC) after adjuvant treatments and evaluate methodological development of existing multivariable models to inform the future development of a predictive risk stratification model (PRSM). Databases (MEDLINE, Web of Science, CINAHL, CENTRAL and PsycINFO) were searched from inception to November 2015. Eligible studies were prospective, recruited women with stage 0-3 BC, used a validated anxiety outcome ≥3 months post-treatment completion and used multivariable prediction models. Internationally accepted quality standards were used to assess predictive risk of bias and strength of evidence. Seven studies were identified: five were observational cohorts and two secondary analyses of RCTs. Variability of measurement and selective reporting precluded meta-analysis. Twenty-one candidate predictors were identified in total. Younger age and previous mental health problems were identified as risk factors in ≥3 studies. Clinical variables (e.g. treatment, tumour grade) were not identified as predictors in any studies. No studies adhered to all quality standards. Pre-existing vulnerability to mental health problems and younger age increased the risk of anxiety after completion of treatment for BC survivors, but there was no evidence that chemotherapy was a predictor. Multiple predictors were identified but many lacked reproducibility or were not measured across studies, and inadequate reporting did not allow full evaluation of the multivariable models. The use of quality standards in the development of PRSM within supportive cancer care would improve model quality and performance, thereby allowing professionals to better target support for patients.

Quantitative analysis of essential oils in perfume using multivariate curve resolution combined with comprehensive two-dimensional gas chromatography.

PubMed

de Godoy, Luiz Antonio Fonseca; Hantao, Leandro Wang; Pedroso, Marcio Pozzobon; Poppi, Ronei Jesus; Augusto, Fabio

2011-08-05

The use of multivariate curve resolution (MCR) to build multivariate quantitative models using data obtained from comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) is presented and evaluated. The MCR algorithm presents some important features, such as second order advantage and the recovery of the instrumental response for each pure component after optimization by an alternating least squares (ALS) procedure. A model to quantify the essential oil of rosemary was built using a calibration set containing only known concentrations of the essential oil and cereal alcohol as solvent. A calibration curve correlating the concentration of the essential oil of rosemary and the instrumental response obtained from the MCR-ALS algorithm was obtained, and this calibration model was applied to predict the concentration of the oil in complex samples (mixtures of the essential oil, pineapple essence and commercial perfume). The values of the root mean square error of prediction (RMSEP) and of the root mean square error of the percentage deviation (RMSPD) obtained were 0.4% (v/v) and 7.2%, respectively. Additionally, a second model was built and used to evaluate the accuracy of the method. A model to quantify the essential oil of lemon grass was built and its concentration was predicted in the validation set and real perfume samples. The RMSEP and RMSPD obtained were 0.5% (v/v) and 6.9%, respectively, and the concentration of the essential oil of lemon grass in perfume agreed to the value informed by the manufacturer. The result indicates that the MCR algorithm is adequate to resolve the target chromatogram from the complex sample and to build multivariate models of GC×GC-FID data. Copyright © 2011 Elsevier B.V. All rights reserved.

Multivariate logistic regression for predicting total culturable virus presence at the intake of a potable-water treatment plant: novel application of the atypical coliform/total coliform ratio.

PubMed

Black, L E; Brion, G M; Freitas, S J

2007-06-01

Predicting the presence of enteric viruses in surface waters is a complex modeling problem. Multiple water quality parameters that indicate the presence of human fecal material, the load of fecal material, and the amount of time fecal material has been in the environment are needed. This paper presents the results of a multiyear study of raw-water quality at the inlet of a potable-water plant that related 17 physical, chemical, and biological indices to the presence of enteric viruses as indicated by cytopathic changes in cell cultures. It was found that several simple, multivariate logistic regression models that could reliably identify observations of the presence or absence of total culturable virus could be fitted. The best models developed combined a fecal age indicator (the atypical coliform [AC]/total coliform [TC] ratio), the detectable presence of a human-associated sterol (epicoprostanol) to indicate the fecal source, and one of several fecal load indicators (the levels of Giardia species cysts, coliform bacteria, and coprostanol). The best fit to the data was found when the AC/TC ratio, the presence of epicoprostanol, and the density of fecal coliform bacteria were input into a simple, multivariate logistic regression equation, resulting in 84.5% and 78.6% accuracies for the identification of the presence and absence of total culturable virus, respectively. The AC/TC ratio was the most influential input variable in all of the models generated, but producing the best prediction required additional input related to the fecal source and the fecal load. The potential for replacing microbial indicators of fecal load with levels of coprostanol was proposed and evaluated by multivariate logistic regression modeling for the presence and absence of virus.

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

Predictive factors of clinical response in steroid-refractory ulcerative colitis treated with granulocyte-monocyte apheresis

PubMed Central

D'Ovidio, Valeria; Meo, Donatella; Viscido, Angelo; Bresci, Giampaolo; Vernia, Piero; Caprilli, Renzo

2011-01-01

AIM: To identify factors predicting the clinical response of ulcerative colitis patients to granulocyte-monocyte apheresis (GMA). METHODS: Sixty-nine ulcerative colitis patients (39 F, 30 M) dependent upon/refractory to steroids were treated with GMA. Steroid dependency, clinical activity index (CAI), C reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), values at baseline, use of immunosuppressant, duration of disease, and age and extent of disease were considered for statistical analysis as predictive factors of clinical response. Univariate and multivariate logistic regression models were used. RESULTS: In the univariate analysis, CAI (P = 0.039) and ESR (P = 0.017) levels at baseline were singled out as predictive of clinical remission. In the multivariate analysis steroid dependency [Odds ratio (OR) = 0.390, 95% Confidence interval (CI): 0.176-0.865, Wald 5.361, P = 0.0160] and low CAI levels at baseline (4 < CAI < 7) (OR = 0.770, 95% CI: 0.425-1.394, Wald 3.747, P = 0.028) proved to be effective as factors predicting clinical response. CONCLUSION: GMA may be a valid therapeutic option for steroid-dependent ulcerative colitis patients with mild-moderate disease and its clinical efficacy seems to persist for 12 mo. PMID:21528055

[Predictive factors of complications during CT-guided transthoracic biopsy].

PubMed

Fontaine-Delaruelle, C; Souquet, P-J; Gamondes, D; Pradat, E; de Leusse, A; Ferretti, G R; Couraud, S

2017-04-01

CT-guided transthoracic core-needle biopsy (TTNB) is frequently used for the diagnosis of lung nodules. The aim of this study is to describe TTNBs' complications and to investigate predictive factors of complications. All consecutive TTNBs performed in three centers between 2006 and 2012 were included. Binary logistic regression was used for multivariate analysis. Overall, 970 TTNBs were performed in 929 patients. The complication rate was 34% (life-threatening complication in 6%). The most frequent complications were pneumothorax (29% included 4% which required chest-tube) and hemoptysis (5%). The mortality rate was 0.1% (n=1). In multivariate analysis, predictive factor for a complication was small target size (AOR=0.984; 95% CI [0.976-0.992]; P<0.001). This predictive factor was also found for occurrence of life-threatening complication (AOR=0.982; [0.965-0.999]; P=0.037), of pneumothorax (AOR=0.987; [0.978-0.995]; P=0.002) and of hemoptysis (AOR=0.973; [0.951-0.997]; P=0.024). One complication occurred in one-third of TTNBs. The proportion of life-threatening complication was 6%. A small lesion size was predictive of complication occurrence. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

The evolution of multivariate maternal effects.

PubMed

Kuijper, Bram; Johnstone, Rufus A; Townley, Stuart

2014-04-01

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

The Evolution of Multivariate Maternal Effects

PubMed Central

Kuijper, Bram; Johnstone, Rufus A.; Townley, Stuart

2014-01-01

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations. PMID:24722346

Dissection of genomic correlation matrices using multivariate factor analysis in dairy and dual-purpose cattle breeds

USDA-ARS?s Scientific Manuscript database

SNP effects estimated in genomic selection programs allow for the prediction of direct genomic values (DGV) both at genome-wide and chromosomal level. As a consequence, genome-wide (G_GW) or chromosomal (G_CHR) correlation matrices between genomic predictions for different traits can be calculated. ...

Admissions Roulette: Predictive Factors for Success in Practice

ERIC Educational Resources Information Center

Pfouts, Jane H.; Henley, H. Carl, Jr.

1977-01-01

A multivariate predictive index of student field performance to be used as an admissions tool in graduate schools of social work is described. It measures the effect on field performance of (1) a measure of the student's intellectual ability, (2) undergraduate school quality, (3) prior work experience, and (4) student sex. (Author/LBH)

Comparison of Employer Factors in Disability and Other Employment Discrimination Charges

ERIC Educational Resources Information Center

Nazarov, Zafar E.; von Schrader, Sarah

2014-01-01

Purpose: We explore whether certain employer characteristics predict Americans with Disabilities Act (ADA) charges and whether the same characteristics predict receipt of the Age Discrimination in Employment Act and Title VII of the Civil Rights Act charges. Method: We estimate a set of multivariate regressions using the ordinary least squares…

Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield

USDA-ARS?s Scientific Manuscript database

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat (Triticum aestivum L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect s...

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities.

PubMed

Madaniyazi, Lina; Guo, Yuming; Chen, Renjie; Kan, Haidong; Tong, Shilu

2016-01-01

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well. Copyright © 2015 Elsevier Ltd. All rights reserved.

Histological changes associated with neoadjuvant chemotherapy are predictive of nodal metastases in high-risk prostate cancer patients

PubMed Central

O’Brien, Catherine; True, Lawrence D.; Higano, Celestia S.; Rademacher, Brooks L. S.; Garzotto, Mark; Beer, Tomasz M.

2011-01-01

Clinical trials are evaluating the effect of neoadjuvant chemotherapy on men with high risk prostate cancer. Little is known about the clinical significance of post-chemotherapy tumor histopathology. We assessed the prognostic and predictive value of histological features (intraductal carcinoma, vacuolated cell morphology, inconspicuous glands, cribriform architecture, and inconspicuous cancer cells) observed in 50 high-risk prostate cancers treated with pre-prostatectomy docetaxel and mitoxantrone. At a median follow-up of 65 months, the overall relapse-free survival (RFS) at 2 and 5 years was 65% and 49%, respectively. In univariate analyses (using Kaplan-Meier method and log-rank tests) intraductal (p=0.001) and cribriform (p=0.014) histologies were associated with shorter RFS. In multivariate analyses, using Cox’s proportional hazards regression, baseline PSA (p=0.004), lymph node metastases (p<0.001), and cribriform histology (p=0.007) were associated with shorter RFS. In multivariable logistic regression analysis, only intraductal pattern (p=0.007) predicted lymph node metastases. Intraductal and cribriform histologies apparently predict post-chemotherapy outcome. PMID:20231619

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

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

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

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

Predictive and Prognostic Factors in Definition of Risk Groups in Endometrial Carcinoma

PubMed Central

Sorbe, Bengt

2012-01-01

Background. The aim was to evaluate predictive and prognostic factors in a large consecutive series of endometrial carcinomas and to discuss pre- and postoperative risk groups based on these factors. Material and Methods. In a consecutive series of 4,543 endometrial carcinomas predictive and prognostic factors were analyzed with regard to recurrence rate and survival. The patients were treated with primary surgery and adjuvant radiotherapy. Two preoperative and three postoperative risk groups were defined. DNA ploidy was included in the definitions. Eight predictive or prognostic factors were used in multivariate analyses. Results. The overall recurrence rate of the complete series was 11.4%. Median time to relapse was 19.7 months. In a multivariate logistic regression analysis, FIGO grade, myometrial infiltration, and DNA ploidy were independent and statistically predictive factors with regard to recurrence rate. The 5-year overall survival rate was 73%. Tumor stage was the single most important factor with FIGO grade on the second place. DNA ploidy was also a significant prognostic factor. In the preoperative risk group definitions three factors were used: histology, FIGO grade, and DNA ploidy. Conclusions. DNA ploidy was an important and significant predictive and prognostic factor and should be used both in preoperative and postoperative risk group definitions. PMID:23209924

Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates

PubMed Central

Kou, Peng Meng; Pallassana, Narayanan; Bowden, Rebeca; Cunningham, Barry; Joy, Abraham; Kohn, Joachim; Babensee, Julia E.

2011-01-01

Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response towards the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R2prediction = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R2prediction = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection. PMID:22136715

Prognostic Significance of Carbohydrate Antigen 19-9 in Unresectable Locally Advanced Pancreatic Cancer Treated With Dose-Escalated Intensity Modulated Radiation Therapy and Concurrent Full-Dose Gemcitabine: Analysis of a Prospective Phase 1/2 Dose Escalation Study

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

Vainshtein, Jeffrey M., E-mail: jvainsh@med.umich.edu; Schipper, Matthew; Zalupski, Mark M.

2013-05-01

Purpose: Although established in the postresection setting, the prognostic value of carbohydrate antigen 19-9 (CA19-9) in unresectable locally advanced pancreatic cancer (LAPC) is less clear. We examined the prognostic utility of CA19-9 in patients with unresectable LAPC treated on a prospective trial of intensity modulated radiation therapy (IMRT) dose escalation with concurrent gemcitabine. Methods and Materials: Forty-six patients with unresectable LAPC were treated at the University of Michigan on a phase 1/2 trial of IMRT dose escalation with concurrent gemcitabine. CA19-9 was obtained at baseline and during routine follow-up. Cox models were used to assess the effect of baseline factorsmore » on freedom from local progression (FFLP), distant progression (FFDP), progression-free survival (PFS), and overall survival (OS). Stepwise forward regression was used to build multivariate predictive models for each endpoint. Results: Thirty-eight patients were eligible for the present analysis. On univariate analysis, baseline CA19-9 and age predicted OS, CA19-9 at baseline and 3 months predicted PFS, gross tumor volume (GTV) and black race predicted FFLP, and CA19-9 at 3 months predicted FFDP. On stepwise multivariate regression modeling, baseline CA19-9, age, and female sex predicted OS; baseline CA19-9 and female sex predicted both PFS and FFDP; and GTV predicted FFLP. Patients with baseline CA19-9 ≤90 U/mL had improved OS (median 23.0 vs 11.1 months, HR 2.88, P<.01) and PFS (14.4 vs 7.0 months, HR 3.61, P=.001). CA19-9 progression over 90 U/mL was prognostic for both OS (HR 3.65, P=.001) and PFS (HR 3.04, P=.001), and it was a stronger predictor of death than either local progression (HR 1.46, P=.42) or distant progression (HR 3.31, P=.004). Conclusions: In patients with unresectable LAPC undergoing definitive chemoradiation therapy, baseline CA19-9 was independently prognostic even after established prognostic factors were controlled for, whereas CA19-9 progression strongly predicted disease progression and death. Future trials should stratify by baseline CA19-9 and incorporate CA19-9 progression as a criterion for progressive disease.« less

Evaluation of in-line Raman data for end-point determination of a coating process: Comparison of Science-Based Calibration, PLS-regression and univariate data analysis.

PubMed

Barimani, Shirin; Kleinebudde, Peter

2017-10-01

A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

Laser ablation molecular isotopic spectroscopy (LAMIS) towards the determination of multivariate LODs via PLS calibration model of 10B and 11B Boric acid mixtures

NASA Astrophysics Data System (ADS)

Harris, C. D.; Profeta, Luisa T. M.; Akpovo, Codjo A.; Johnson, Lewis; Stowe, Ashley C.

2017-05-01

A calibration model was created to illustrate the detection capabilities of laser ablation molecular isotopic spectroscopy (LAMIS) discrimination in isotopic analysis. The sample set contained boric acid pellets that varied in isotopic concentrations of 10B and 11B. Each sample set was interrogated with a Q-switched Nd:YAG ablation laser operating at 532 nm. A minimum of four band heads of the β system B2∑ -> Χ2∑transitions were identified and verified with previous literature on BO molecular emission lines. Isotopic shifts were observed in the spectra for each transition and used as the predictors in the calibration model. The spectra along with their respective 10/11B isotopic ratios were analyzed using Partial Least Squares Regression (PLSR). An IUPAC novel approach for determining a multivariate Limit of Detection (LOD) interval was used to predict the detection of the desired isotopic ratios. The predicted multivariate LOD is dependent on the variation of the instrumental signal and other composites in the calibration model space.

The time frame of Epstein-Barr virus latent membrane protein-1 gene to disappear in nasopharyngeal swabs after initiation of primary radiotherapy is an independently significant prognostic factor predicting local control for patients with nasopharyngeal carcinoma

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

Lin, S.-Y.; Chang, K.-P.; Graduate Institute of Clinical Medical Sciences, Chang Gung University, Linkou, Taiwan

Purpose: The presence of Epstein-Barr virus latent membrane protein-1 (LMP-1) gene in nasopharyngeal swabs indicates the presence of nasopharyngeal carcinoma (NPC) mucosal tumor cells. This study was undertaken to investigate whether the time taken for LMP-1 to disappear after initiation of primary radiotherapy (RT) was inversely associated with NPC local control. Methods and Materials: During July 1999 and October 2002, there were 127 nondisseminated NPC patients receiving serial examinations of nasopharyngeal swabbing with detection of LMP-1 during the RT course. The time for LMP-1 regression was defined as the number of days after initiation of RT for LMP-1 results tomore » turn negative. The primary outcome was local control, which was represented by freedom from local recurrence. Results: The time for LMP-1 regression showed a statistically significant influence on NPC local control both univariately (p < 0.0001) and multivariately (p = 0.004). In multivariate analysis, the administration of chemotherapy conferred a significantly more favorable local control (p = 0.03). Advanced T status ({>=} T2b), overall treatment time of external photon radiotherapy longer than 55 days, and older age showed trends toward being poor prognosticators. The time for LMP-1 regression was very heterogeneous. According to the quartiles of the time for LMP-1 regression, we defined the pattern of LMP-1 regression as late regression if it required 40 days or more. Kaplan-Meier plots indicated that the patients with late regression had a significantly worse local control than those with intermediate or early regression (p 0.0129). Conclusion: Among the potential prognostic factors examined in this study, the time for LMP-1 regression was the most independently significant factor that was inversely associated with NPC local control.« less

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

Comparison of two metrological approaches for the prediction of human haptic perception

NASA Astrophysics Data System (ADS)

Neumann, Annika; Frank, Daniel; Vondenhoff, Thomas; Schmitt, Robert

2016-06-01

Haptic perception is regarded as a key component of customer appreciation and acceptance for various products. The prediction of customers’ haptic perception is of interest both during product development and production phases. This paper presents the results of a multivariate analysis between perceived roughness and texture related surface measurements, to examine whether perceived roughness can be accurately predicted using technical measurements. Studies have shown that standardized measurement parameters, such as the roughness coefficients (e.g. Rz or Ra), do not show a one-dimensional linear correlation with the human perception (of roughness). Thus, an alternative measurement method was compared to standard measurements of roughness, in regard to its capability of predicting perceived roughness through technical measurements. To estimate perceived roughness, an experimental study was conducted in which 102 subjects evaluated four sets of 12 different geometrical surface structures regarding their relative perceived roughness. The two different metrological procedures were examined in relation to their capability to predict the perceived roughness of the subjects stated within the study. The standardized measurements of the surface roughness were made using a structured light 3D-scanner. As an alternative method, surface induced vibrations were measured by a finger-like sensor during robot-controlled traverse over a surface. The presented findings provide a better understanding of the predictability of human haptic perception using technical measurements.

A multivariable model for predicting the frictional behaviour and hydration of the human skin.

PubMed

Veijgen, N K; van der Heide, E; Masen, M A

2013-08-01

The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These two coefficients of friction show a strong correlation. Consequently the two multivariable models resemble, with the static coefficient of friction being on average 18% lower than the dynamic coefficient of friction. The multivariable models in this study can be used to describe the data set that was the basis for this study. Care should be taken when generalising these results. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Remote Multivariable Control Design Using a Competition Game

ERIC Educational Resources Information Center

Atanasijevic-Kunc, M.; Logar, V.; Karba, R.; Papic, M.; Kos, A.

2011-01-01

In this paper, some approaches to teaching multivariable control design are discussed, with special attention being devoted to a step-by-step transition to e-learning. The approach put into practice and presented here is developed through design projects, from which one is chosen as a competition game and is realized using the E-CHO system,…

New multivariable capabilities of the INCA program

NASA Technical Reports Server (NTRS)

Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.

1989-01-01

The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

Multivariable speed synchronisation for a parallel hybrid electric vehicle drivetrain

NASA Astrophysics Data System (ADS)

Alt, B.; Antritter, F.; Svaricek, F.; Schultalbers, M.

2013-03-01

In this article, a new drivetrain configuration of a parallel hybrid electric vehicle is considered and a novel model-based control design strategy is given. In particular, the control design covers the speed synchronisation task during a restart of the internal combustion engine. The proposed multivariable synchronisation strategy is based on feedforward and decoupled feedback controllers. The performance and the robustness properties of the closed-loop system are illustrated by nonlinear simulation results.

Declining prevalence of HIV-1 drug resistance in antiretroviral treatment-exposed individuals in Western Europe.

PubMed

De Luca, Andrea; Dunn, David; Zazzi, Maurizio; Camacho, Ricardo; Torti, Carlo; Fanti, Iuri; Kaiser, Rolf; Sönnerborg, Anders; Codoñer, Francisco M; Van Laethem, Kristel; Vandamme, Anne-Mieke; Bansi, Loveleen; Ghisetti, Valeria; van de Vijver, David A M C; Asboe, David; Prosperi, Mattia C F; Di Giambenedetto, Simona

2013-04-15

HIV-1 drug resistance represents a major obstacle to infection and disease control. This retrospective study analyzes trends and determinants of resistance in antiretroviral treatment (ART)-exposed individuals across 7 countries in Europe. Of 20 323 cases, 80% carried at least one resistance mutation: these declined from 81% in 1997 to 71% in 2008. Predicted extensive 3-class resistance was rare (3.2% considering the cumulative genotype) and peaked at 4.5% in 2005, decreasing thereafter. The proportion of cases exhausting available drug options dropped from 32% in 2000 to 1% in 2008. Reduced risk of resistance over calendar years was confirmed by multivariable analysis.

Clinical-genetic model predicts incident impulse control disorders in Parkinson's disease.

PubMed

Kraemmer, Julia; Smith, Kara; Weintraub, Daniel; Guillemot, Vincent; Nalls, Mike A; Cormier-Dequaire, Florence; Moszer, Ivan; Brice, Alexis; Singleton, Andrew B; Corvol, Jean-Christophe

2016-10-01

Impulse control disorders (ICD) are commonly associated with dopamine replacement therapy (DRT) in patients with Parkinson's disease (PD). Our aims were to estimate ICD heritability and to predict ICD by a candidate genetic multivariable panel in patients with PD. Data from de novo patients with PD, drug-naïve and free of ICD behaviour at baseline, were obtained from the Parkinson's Progression Markers Initiative cohort. Incident ICD behaviour was defined as positive score on the Questionnaire for Impulsive-Compulsive Disorders in PD. ICD heritability was estimated by restricted maximum likelihood analysis on whole exome sequencing data. 13 candidate variants were selected from the DRD2, DRD3, DAT1, COMT, DDC, GRIN2B, ADRA2C, SERT, TPH2, HTR2A, OPRK1 and OPRM1 genes. ICD prediction was evaluated by the area under the curve (AUC) of receiver operating characteristic (ROC) curves. Among 276 patients with PD included in the analysis, 86% started DRT, 40% were on dopamine agonists (DA), 19% reported incident ICD behaviour during follow-up. We found heritability of this symptom to be 57%. Adding genotypes from the 13 candidate variants significantly increased ICD predictability (AUC=76%, 95% CI (70% to 83%)) compared to prediction based on clinical variables only (AUC=65%, 95% CI (58% to 73%), p=0.002). The clinical-genetic prediction model reached highest accuracy in patients initiating DA therapy (AUC=87%, 95% CI (80% to 93%)). OPRK1, HTR2A and DDC genotypes were the strongest genetic predictive factors. Our results show that adding a candidate genetic panel increases ICD predictability, suggesting potential for developing clinical-genetic models to identify patients with PD at increased risk of ICD development and guide DRT management. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-03-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 +/- 9.2 and 14.1 +/- 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 +/- 6.9 and 14.2 +/- 8.2 mL s(-1) in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi.

Brain natriuretic peptide predicts functional outcome in ischemic stroke

PubMed Central

Rost, Natalia S; Biffi, Alessandro; Cloonan, Lisa; Chorba, John; Kelly, Peter; Greer, David; Ellinor, Patrick; Furie, Karen L

2011-01-01

Background Elevated serum levels of brain natriuretic peptide (BNP) have been associated with cardioembolic (CE) stroke and increased post-stroke mortality. We sought to determine whether BNP levels were associated with functional outcome after ischemic stroke. Methods We measured BNP in consecutive patients aged ≥18 years admitted to our Stroke Unit between 2002–2005. BNP quintiles were used for analysis. Stroke subtypes were assigned using TOAST criteria. Outcomes were measured as 6-month modified Rankin Scale score (“good outcome” = 0–2 vs. “poor”) as well as mortality. Multivariate logistic regression was used to assess association between the quintiles of BNP and outcomes. Predictive performance of BNP as compared to clinical model alone was assessed by comparing ROC curves. Results Of 569 ischemic stroke patients, 46% were female; mean age was 67.9 ± 15 years. In age- and gender-adjusted analysis, elevated BNP was associated with lower ejection fraction (p<0.0001) and left atrial dilatation (p<0.001). In multivariate analysis, elevated BNP decreased the odds of good functional outcome (OR 0.64, 95%CI 0.41–0.98) and increased the odds of death (OR 1.75, 95%CI 1.36–2.24) in these patients. Addition of BNP to multivariate models increased their predictive performance for functional outcome (p=0.013) and mortality (p<0.03) after CE stroke. Conclusions Serum BNP levels are strongly associated with CE stroke and functional outcome at 6 months after ischemic stroke. Inclusion of BNP improved prediction of mortality in patients with CE stroke. PMID:22116811

Radiotherapy and Hyperthermia for Treatment of Primary Locally Advanced Cervix Cancer: Results in 378 Patients

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

Franckena, Martine; Lutgens, Ludy C.; Koper, Peter C.

2009-01-01

Purpose: To report response rate, pelvic tumor control, survival, and late toxicity after treatment with combined radiotherapy and hyperthermia (RHT) for patients with locally advanced cervical carcinoma (LACC) and compare the results with other published series. Methods and Materials: From 1996 to 2005, a total of 378 patients with LACC (International Federation of Gynecology and Obstetrics Stage IB2-IVA) were treated with RHT. External beam radiotherapy (RT) was applied to 46-50.4 Gy and combined with brachytherapy. The hyperthermia (HT) was prescribed once weekly. Primary end points were complete response (CR) and local control. Secondary end points were overall survival, disease-specific survival,more » and late toxicity. Patient, tumor, and treatment characteristics predictive for the end points were identified in univariate and multivariate analyses. Results: Overall, a CR was achieved in 77% of patients. At 5 years, local control, disease-specific survival, and incidence of late toxicity Common Terminology Criteria for Adverse Events Grade 3 or higher were 53%, 47%, and 12%, respectively. In multivariate analysis, number of HT treatments emerged as a predictor of outcome in addition to commonly identified prognostic factors. Conclusions: The CR, local control, and survival rates are similar to previously observed results of RHT in the randomized Dutch Deep Hyperthermia Trial. Reported treatment results for currently applied combined treatment modalities (i.e., RT with chemotherapy and/or HT) do not permit definite conclusions about which combination is superior. The present results confirm previously shown beneficial effects from adding HT to RT and justify the application of RHT as first-line treatment in patients with LACC as an alternative to chemoradiation.« less