Using a topographic index to distribute variable source area runoff predicted with the SCS curve-number equation

NASA Astrophysics Data System (ADS)

Lyon, Steve W.; Walter, M. Todd; Gérard-Marchant, Pierre; Steenhuis, Tammo S.

2004-10-01

Because the traditional Soil Conservation Service curve-number (SCS-CN) approach continues to be used ubiquitously in water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed and tested a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Predicting the location of source areas is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point-source pollution. The method presented here used the traditional SCS-CN approach to predict runoff volume and spatial extent of saturated areas and a topographic index, like that used in TOPMODEL, to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was applied to two subwatersheds of the Delaware basin in the Catskill Mountains region of New York State and one watershed in south-eastern Australia to produce runoff-probability maps. Observed saturated area locations in the watersheds agreed with the distributed CN-VSA method. Results showed good agreement with those obtained from the previously validated soil moisture routing (SMR) model. When compared with the traditional SCS-CN method, the distributed CN-VSA method predicted a similar total volume of runoff, but vastly different locations of runoff generation. Thus, the distributed CN-VSA approach provides a physically based method that is simple enough to be incorporated into water quality models, and other tools that currently use the traditional SCS-CN method, while still adhering to the principles of VSA hydrology.

Fatty Liver Index and Lipid Accumulation Product Can Predict Metabolic Syndrome in Subjects without Fatty Liver Disease

PubMed Central

Cheng, Yuan-Lung; Wang, Yuan-Jen; Lan, Keng-Hsin; Huo, Teh-Ia; Hsieh, Wei-Yao; Hou, Ming-Chih; Lee, Fa-Yauh; Wu, Jaw-Ching; Lee, Shou-Dong

2017-01-01

Background. Fatty liver index (FLI) and lipid accumulation product (LAP) are indexes originally designed to assess the risk of fatty liver and cardiovascular disease, respectively. Both indexes have been proven to be reliable markers of subsequent metabolic syndrome; however, their ability to predict metabolic syndrome in subjects without fatty liver disease has not been clarified. Methods. We enrolled consecutive subjects who received health check-up services at Taipei Veterans General Hospital from 2002 to 2009. Fatty liver disease was diagnosed by abdominal ultrasonography. The ability of the FLI and LAP to predict metabolic syndrome was assessed by analyzing the area under the receiver operating characteristic (AUROC) curve. Results. Male sex was strongly associated with metabolic syndrome, and the LAP and FLI were better than other variables to predict metabolic syndrome among the 29,797 subjects. Both indexes were also better than other variables to detect metabolic syndrome in subjects without fatty liver disease (AUROC: 0.871 and 0.879, resp.), and the predictive power was greater among women. Conclusion. Metabolic syndrome increases the cardiovascular disease risk. The FLI and LAP could be used to recognize the syndrome in both subjects with and without fatty liver disease who require lifestyle modifications and counseling. PMID:28194177

Predicting fiber refractive index from a measured preform index profile

NASA Astrophysics Data System (ADS)

Kiiveri, P.; Koponen, J.; Harra, J.; Novotny, S.; Husu, H.; Ihalainen, H.; Kokki, T.; Aallos, V.; Kimmelma, O.; Paul, J.

2018-02-01

When producing fiber lasers and amplifiers, silica glass compositions consisting of three to six different materials are needed. Due to the varying needs of different applications, substantial number of different glass compositions are used in the active fiber structures. Often it is not possible to find material parameters for theoretical models to estimate thermal and mechanical properties of those glass compositions. This makes it challenging to predict accurately fiber core refractive index values, even if the preform index profile is measured. Usually the desired fiber refractive index value is achieved experimentally, which is expensive. To overcome this problem, we analyzed statistically the changes between the measured preform and fiber index values. We searched for correlations that would help to predict the Δn-value change from preform to fiber in a situation where we don't know the values of the glass material parameters that define the change. Our index change models were built using the data collected from preforms and fibers made by the Direct Nanoparticle Deposition (DND) technology.

Framework for making better predictions by directly estimating variables' predictivity.

PubMed

Lo, Adeline; Chernoff, Herman; Zheng, Tian; Lo, Shaw-Hwa

2016-12-13

We propose approaching prediction from a framework grounded in the theoretical correct prediction rate of a variable set as a parameter of interest. This framework allows us to define a measure of predictivity that enables assessing variable sets for, preferably high, predictivity. We first define the prediction rate for a variable set and consider, and ultimately reject, the naive estimator, a statistic based on the observed sample data, due to its inflated bias for moderate sample size and its sensitivity to noisy useless variables. We demonstrate that the [Formula: see text]-score of the PR method of VS yields a relatively unbiased estimate of a parameter that is not sensitive to noisy variables and is a lower bound to the parameter of interest. Thus, the PR method using the [Formula: see text]-score provides an effective approach to selecting highly predictive variables. We offer simulations and an application of the [Formula: see text]-score on real data to demonstrate the statistic's predictive performance on sample data. We conjecture that using the partition retention and [Formula: see text]-score can aid in finding variable sets with promising prediction rates; however, further research in the avenue of sample-based measures of predictivity is much desired.

A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

PubMed

Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin; Brehm Christensen, Peer; Langeland, Nina; Buhl, Mads Rauning; Pedersen, Court; Mørch, Kristine; Wejstål, Rune; Norkrans, Gunnar; Lindh, Magnus; Färkkilä, Martti; Westin, Johan

2014-01-01

Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV) infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI) in the paper, was based on the model: Log-odds (predicting cirrhosis) = -12.17+ (age × 0.11) + (BMI (kg/m(2)) × 0.23) + (D7-lathosterol (μg/100 mg cholesterol)×(-0.013)) + (Platelet count (x10(9)/L) × (-0.018)) + (Prothrombin-INR × 3.69). The area under the ROC curve (AUROC) for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96). The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98). In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

The predictive value of the heart-rate-variability derived Analgesia Nociception Index in children anaesthetised with sevoflurane - an observational pilot-study.

PubMed

Weber, Frank; Geerts, Noortje J E; Roeleveld, Hilde G; Warmenhoven, Annejet T; Liebrand, Chantal A

2018-05-13

The heart rate variability (HRV) derived Analgesia Nociception Index (ANI ™ ) is a continuous non-invasive tool to assess the nociception/anti-nociception balance in unconscious patients. It has been shown to be superior to hemodynamic variables in detecting insufficient anti-nociception in children, while little is known about its predictive value. The primary objective of this prospective observational pilot study in paediatric surgical patients under sevoflurane anaesthesia, was to compare the predictive value of the ANI and heart rate to help decide to give additional opioids. The paediatric anaesthesiologist in charge was blinded to ANI values. In patients with an ANI value <50 (indicating insufficient anti-nociception) at the moment of decision, ANI values dropped from ±55 (indicating sufficient anti-nociception) to ±35, starting 60 sec. before decision. Within 120 sec. after administration of fentanyl (1 mcg/kg), ANI values returned to ±60. This phenomenon was only observed in the ANI values derived from HRV data averaged over 2 min. Heart rate remained unchanged. In patients with ANI values ≥50 at the time of decision, opioid administration had no effect on ANI or heart rate. The same accounts for morphine for postoperative analgesia and fentanyl in case of intraoperative movement. This study provides evidence of a better predictive value of the ANI in detecting insufficient anti-nociception in paediatric surgical patients than heart rate. The same accounts for depicting re-establishment of sufficient anti-nociception after opioid drug administration. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Short-term variability in body weight predicts long-term weight gain.

PubMed

Lowe, Michael R; Feig, Emily H; Winter, Samantha R; Stice, Eric

2015-11-01

Body weight in lower animals and humans is highly stable despite a very large flux in energy intake and expenditure over time. Conversely, the existence of higher-than-average variability in weight may indicate a disruption in the mechanisms responsible for homeostatic weight regulation. In a sample chosen for weight-gain proneness, we evaluated whether weight variability over a 6-mo period predicted subsequent weight change from 6 to 24 mo. A total of 171 nonobese women were recruited to participate in this longitudinal study in which weight was measured 4 times over 24 mo. The initial 3 weights were used to calculate weight variability with the use of a root mean square error approach to assess fluctuations in weight independent of trajectory. Linear regression analysis was used to examine whether weight variability in the initial 6 mo predicted weight change 18 mo later. Greater weight variability significantly predicted amount of weight gained. This result was unchanged after control for baseline body mass index (BMI) and BMI change from baseline to 6 mo and for measures of disinhibition, restrained eating, and dieting. Elevated weight variability in young women may signal the degradation of body weight regulatory systems. In an obesogenic environment this may eventuate in accelerated weight gain, particularly in those with a genetic susceptibility toward overweight. Future research is needed to evaluate the reliability of weight variability as a predictor of future weight gain and the sources of its predictive effect. The trial on which this study is based is registered at clinicaltrials.gov as NCT00456131. © 2015 American Society for Nutrition.

Use of the Hardman index in predicting mortality in endovascular repair of ruptured abdominal aortic aneurysms.

PubMed

Conroy, Daniel M; Altaf, Nishath; Goode, Steve D; Braithwaite, Bruce D; MacSweeney, Shane T; Richards, Toby

2011-12-01

The Hardman index is a predictor of 30-day mortality after open ruptured abdominal aneurysm repair through the use of preoperative patient factors. The aim of this study was to assess the Hardman index in patients undergoing endovascular repair of ruptured aortic aneurysms. A retrospective analysis of 95 patients undergoing emergency endovascular repairs of computed tomography-confirmed ruptured aneurysms from 1994 to 2008 in a university hospital was performed. All relevant patient variables, calculations of the Hardman index, and the incidence of 30-day mortality were collected in these patients. Correlation of the relationship between each variable and the overall score with the incidence of 30-day mortality was undertaken. The 24-hour mortality was 16% and 30-day mortality 36%. Increasing scores on the Hardman index showed an increasing mortality rate. Thirty-day mortality in patients with a score of 0 to 2 was 30.5%, and in those with a score of ≥3 was 69.2% (P = .01, risk ratio = 2.26, 95% confidence interval = 0.98 to 5.17). This is lower than predicted in both patient groups based on Hardman index score. Loss of consciousness was the only statistically significant independent predictor of 30-day mortality with a risk ratio of 3.16 (95% confidence interval = 2.00-4.97, P < .001). These data suggest that the Hardman index can predict an increased risk of 30-day mortality from endovascular repairs of ruptured aortic aneurysms. However, mortality from endovascular repair is much lower than would be predicted in open repair and it therefore cannot be used clinically as a tool for exclusion from intervention.

Climate Prediction Center - Outlooks Index

Science.gov Websites

Temperature and Precipitation Outlooks 3-7 Day Excessive Heat Outlooks (Weather Prediction Center) 6-10 Day Excessive Heat Outlook 8-14 Day Excessive Heat Outlook 6-10 Day Wind Chill Index Outlooks 8-14 Day Wind

Variability between Clarke's angle and Chippaux-Smirak index for the diagnosis of flat feet

PubMed Central

Gonzalez-Martin, Cristina; Seoane-Pillado, Teresa; Lopez-Calviño, Beatriz; Pertega-Diaz, Sonia; Gil-Guillen, Vicente

2017-01-01

Abstract Background: The measurements used in diagnosing biomechanical pathologies vary greatly. The aim of this study was to determine the concordance between Clarke's angle and Chippaux-Smirak index, and to determine the validity of Clarke's angle using the Chippaux-Smirak index as a reference. Methods: Observational study in a random population sample (n= 1,002) in A Coruña (Spain). After informed patient consent and ethical review approval, a study was conducted of anthropometric variables, Charlson comorbidity score, and podiatric examination (Clarke's angle and Chippaux-Smirak index). Descriptive analysis and multivariate logistic regression were performed. Results: The prevalence of flat feet, using a podoscope, was 19.0% for the left foot and 18.9% for the right foot, increasing with age. The prevalence of flat feet according to the Chippaux-Smirak index or Clarke's angle increases significantly, reaching 62.0% and 29.7% respectively. The concordance (kappa I) between the indices according to age groups varied between 0.25-0.33 (left foot) and 0.21-0.30 (right foot). The intraclass correlation coefficient (ICC) between the Chippaux-Smirak index and Clarke's angle was -0.445 (left foot) and -0.424 (right foot). After adjusting for age, body mass index (BMI), comorbidity score and gender, the only variable with an independent effect to predict discordance was the BMI (OR= 0.969; 95% CI: 0.940-0.998). Conclusion: There is little concordance between the indices studied for the purpose of diagnosing foot arch pathologies. In turn, Clarke's angle has a limited sensitivity in diagnosing flat feet, using the Chippaux-Smirak index as a reference. This discordance decreases with higher BMI values. PMID:28559643

Variability between Clarke's angle and Chippaux-Smirak index for the diagnosis of flat feet.

PubMed

Gonzalez-Martin, Cristina; Pita-Fernandez, Salvador; Seoane-Pillado, Teresa; Lopez-Calviño, Beatriz; Pertega-Diaz, Sonia; Gil-Guillen, Vicente

2017-03-30

The measurements used in diagnosing biomechanical pathologies vary greatly. The aim of this study was to determine the concordance between Clarke's angle and Chippaux-Smirak index, and to determine the validity of Clarke's angle using the Chippaux-Smirak index as a reference. Observational study in a random population sample (n= 1,002) in A Coruña (Spain). After informed patient consent and ethical review approval, a study was conducted of anthropometric variables, Charlson comorbidity score, and podiatric examination (Clarke's angle and Chippaux-Smirak index). Descriptive analysis and multivariate logistic regression were performed. The prevalence of flat feet, using a podoscope, was 19.0% for the left foot and 18.9% for the right foot, increasing with age. The prevalence of flat feet according to the Chippaux-Smirak index or Clarke's angle increases significantly, reaching 62.0% and 29.7% respectively. The concordance (kappa I) between the indices according to age groups varied between 0.25-0.33 (left foot) and 0.21-0.30 (right foot). The intraclass correlation coefficient (ICC) between the Chippaux-Smirak index and Clarke's angle was -0.445 (left foot) and -0.424 (right foot). After adjusting for age, body mass index (BMI), comorbidity score and gender, the only variable with an independent effect to predict discordance was the BMI (OR= 0.969; 95% CI: 0.940-0.998). There is little concordance between the indices studied for the purpose of diagnosing foot arch pathologies. In turn, Clarke's angle has a limited sensitivity in diagnosing flat feet, using the Chippaux-Smirak index as a reference. This discordance decreases with higher BMI values.

Development of an Echocardiographic Risk-Stratification Index to Predict Heart Failure in Patients With Stable Coronary Artery Disease

PubMed Central

Stevens, Steven M.; Farzaneh-Far, Ramin; Na, Beeya; Whooley, Mary A.; Schiller, Nelson B.

2009-01-01

OBJECTIVES We sought to determine which transthoracic echocardiographic (TTE) measurements most strongly predict heart failure (HF) and to develop an index for risk stratification in outpatients with coronary artery disease (CAD). BACKGROUND Many TTE measurements have been shown to be predictive of HF, and they might be useful if aggregated into a risk-prediction index. METHODS We performed TTE in 1,024 outpatients with stable CAD enrolled in the Heart and Soul study and followed them for 4.4 years. With Cox proportional hazard models, we evaluated the association of 15 TTE measurements with subsequent HF hospital stay. Those measurements that independently predicted HF were combined into an index. Variables were defined as normal or abnormal on the basis of dichotomous cutoffs determined from the American Society of Echocardiography. Abnormal variables in each measurement were assigned points on the basis of strength of association with HF. RESULTS Of the 15 variables, 5 measurements were independent predictors of HF: left ventricular mass index (LVMI), left atrial volume index (LAVI), mitral regurgitation (MR), left ventricular outflow tract velocity-time integral (VTILVOT), and diastolic dysfunction (DD). In multivariate analysis, each of the 5 measurements independently predicted HF: LVMI >90 g/m2 (hazard ratio [HR]: 4.1; 95% confidence interval [CI]: 2.3 to 7.2, p < 0.0001); pseudo-normal or restrictive DD (HR: 2.9; 95% CI: 1.8 to 4.5, p < 0.0001); VTILVOT <22 mm (HR: 2.2; 95% CI: 1.4 to 3.5, p = 0.0004); mild, moderate, or severe MR (HR: 1.8; 95% CI: 1.2 to 2.8, p = 0.009); and LAVI >29 ml/m2 (HR: 1.6; 95% CI: 1.0 to 2.5, p = 0.06). Combining these measurements, the Heart Failure Index ranged from 0 to 8, representing risk as follows: 3 points for LVMI, 2 points for DD, and 1 point for VTILVOT, MR, and LAVI. Among participants with 0 to 2 points: 4% had HF hospital stays (reference); 3 to 4 points: 10% (HR: 2.4; 95% CI: 1.3 to 4.4, p = 0.003); 5 to 6 points

The TyG index may predict the development of cardiovascular events.

PubMed

Sánchez-Íñigo, Laura; Navarro-González, David; Fernández-Montero, Alejandro; Pastrana-Delgado, Juan; Martínez, Jose Alfredo

2016-02-01

Cardiovascular disease (CVD) is the worldwide leading cause of morbidity and mortality. An early risk detection of apparently healthy people before CVD onset has clinical relevance in the prevention of cardiovascular events. We evaluated the association between the product of fasting plasma glucose and triglycerides (TyG index) and CVD. A total of 5014 patients of the Vascular Metabolic CUN cohort (VMCUN cohort) were followed up during a median period of 10 years. We used a Cox proportional-hazard ratio with repeated measures to estimate the risk of incidence of CVD across quintiles of the TyG index, calculated as ln[fasting triglycerides (mg/dL) × fasting plasma glucose (mg(dL)/2], and plotted a receiver-operating characteristics (ROC) curve to compare a prediction model fitted on the variables used in the Framingham risk score, a new model containing the Framingham variables with the TyG index, and the risk of coronary heart disease. A higher level of TyG index was significantly associated with an increased risk of developing CVD independent of confounding factors with a value of 2·32 (95% CI: 1·65-3·26) for those in the highest quintile and 1·52 (95% CI: 1·07-2·16) for those in the fourth quintile. The areas under the curve (AUC) of the ROC plots were 0·708 (0·68-0·73) for the Framingham model and 0·719 (0·70-0·74) for the Framingham + TyG index model (P = 0·014). The TyG index, a simple measure reflecting insulin resistance, might be useful to early identify individuals at a high risk of developing a cardiovascular event. © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

Short-term variability in body weight predicts long-term weight gain1

PubMed Central

Lowe, Michael R; Feig, Emily H; Winter, Samantha R; Stice, Eric

2015-01-01

Background: Body weight in lower animals and humans is highly stable despite a very large flux in energy intake and expenditure over time. Conversely, the existence of higher-than-average variability in weight may indicate a disruption in the mechanisms responsible for homeostatic weight regulation. Objective: In a sample chosen for weight-gain proneness, we evaluated whether weight variability over a 6-mo period predicted subsequent weight change from 6 to 24 mo. Design: A total of 171 nonobese women were recruited to participate in this longitudinal study in which weight was measured 4 times over 24 mo. The initial 3 weights were used to calculate weight variability with the use of a root mean square error approach to assess fluctuations in weight independent of trajectory. Linear regression analysis was used to examine whether weight variability in the initial 6 mo predicted weight change 18 mo later. Results: Greater weight variability significantly predicted amount of weight gained. This result was unchanged after control for baseline body mass index (BMI) and BMI change from baseline to 6 mo and for measures of disinhibition, restrained eating, and dieting. Conclusions: Elevated weight variability in young women may signal the degradation of body weight regulatory systems. In an obesogenic environment this may eventuate in accelerated weight gain, particularly in those with a genetic susceptibility toward overweight. Future research is needed to evaluate the reliability of weight variability as a predictor of future weight gain and the sources of its predictive effect. The trial on which this study is based is registered at clinicaltrials.gov as NCT00456131. PMID:26354535

Ambivalence About Interpersonal Problems and Traits Predicts Cross-Situational Variability of Social Behavior.

PubMed

Erickson, Thane M; Newman, Michelle G; Peterson, Jessica; Scarsella, Gina

2015-08-01

Multiple theoretical perspectives suggest that maladjusted personality is characterized by not only distress, but also opposing or "ambivalent" self-perceptions and behavioral lability across social interactions. However, the degree to which ambivalence about oneself predicts cross-situational variability in social behavior has not been examined empirically. Using the interpersonal circumplex (IPC) as a nomological framework, the present study investigated the extent to which endorsing opposing or "ambivalent" tendencies on IPC measures predicted variability in social behavior across a range of hypothetical interpersonal scenarios (Part 1; N = 288) and naturalistic social interactions (Part 2; N = 192). Ambivalent responding for interpersonal problems and traits was associated with measures of distress, maladaptive interpersonal tendencies, and greater variability of social behavior across both hypothetical and daily social interactions, though more consistently for interpersonal problems. More conservative tests suggested that ambivalence predicted some indexes of behavioral variability even when accounting for mean levels and squared means of social behaviors, vector length, gender, and depressive symptoms. Results suggest that processes theorized as typifying personality disorder may apply more broadly to personality maladjustment occurring outside of clinical samples. © 2014 Wiley Periodicals, Inc.

Perioperative Near-Infrared Spectroscopy Monitoring in Neonates With Congenital Heart Disease: Relationship of Cerebral Tissue Oxygenation Index Variability With Neurodevelopmental Outcome.

PubMed

Spaeder, Michael C; Klugman, Darren; Skurow-Todd, Kami; Glass, Penny; Jonas, Richard A; Donofrio, Mary T

2017-03-01

To evaluate the value of perioperative cerebral near-infrared spectroscopy monitoring using variability analysis in the prediction of neurodevelopmental outcomes in neonates undergoing surgery for congenital heart disease. Retrospective cohort study. Urban, academic, tertiary-care children's hospital. Neonates undergoing surgery with cardiopulmonary bypass for congenital heart disease. Perioperative monitoring of continuous cerebral tissue oxygenation index by near-infrared spectroscopy and subsequent neurodevelopmental testing at 6, 15, and 21 months of age. We developed a new measure, cerebral tissue oxygenation index variability, using the root mean of successive squared differences of averaged 1-minute cerebral tissue oxygenation index values for both the intraoperative and first 24-hours postoperative phases of monitoring. There were 62 neonates who underwent cerebral tissue oxygenation index monitoring during surgery for congenital heart disease and 44 underwent subsequent neurodevelopmental testing (12 did not survive until testing and six were lost to follow-up). Among the 44 monitored patients who underwent neurodevelopmental testing, 20 (45%) had abnormal neurodevelopmental indices. Patients with abnormal neurodevelopmental indices had lower postoperative cerebral tissue oxygenation index variability when compared with patients with normal indices (p = 0.01). Adjusting for class of congenital heart disease and duration of deep hypothermic circulatory arrest, lower postoperative cerebral tissue oxygenation index variability was associated with poor neurodevelopmental outcome (p = 0.02). We found reduced postoperative cerebral tissue oxygenation index variability in neonatal survivors of congenital heart disease surgery with poor neurodevelopmental outcomes. We hypothesize that reduced cerebral tissue oxygenation index variability may be a surrogate for impaired cerebral metabolic autoregulation in the immediate postoperative period. Further research is

The Index Offence Representation Scales; a predictive clinical tool in the management of dangerous, violent patients with personality disorder?

PubMed

McGauley, Gill; Ferris, Scott; Marin-Avellan, Luisa; Fonagy, Peter

2013-10-01

Forensic mental health professionals attach considerable importance to their patient's description of his or her index offence. Despite this, there is no systematic approach to examining and formulating the patient's offence narrative. To use the index offence narratives and capacity to mentalize of violent offender-patients with personality disorder to develop a tool to predict their progress and to evaluate that tool. In a prospective, cohort study, the index offence narratives of 66 violent high security hospital patients with personality disorder were obtained from a semi-structured interview and used to generate the Index Offence Representational Scales (IORS). The predictive validity of these scales was investigated across a range of outcome variables, controlling for the association between initial and final value of the dependent variable. The degree to which patients held internal representations of interpersonal violence and malevolence, as measured by the IORS, predicted subsequent violent behaviour. In contrast to their actual aggressive behaviour, these patients rated themselves as having fewer symptoms on the Symptom Checklist-90-R (SCL-90-R) and fewer problems in interpersonal relationships on the Inventory of Interpersonal Problems. A more empathic victim representation on the IORS predicted better engagement with treatment. The IORS show promise for helping clinicians formulate the early institutional pathway of seriously violent people with personality disorder, particularly with respect to their overt aggression and prosocial engagement. Replication studies are, however, indicated. Copyright © 2013 John Wiley & Sons, Ltd.

Discriminative ability of commonly used indices to predict adverse outcomes after poster lumbar fusion: a comparison of demographics, ASA, the modified Charlson Comorbidity Index, and the modified Frailty Index.

PubMed

Ondeck, Nathaniel T; Bohl, Daniel D; Bovonratwet, Patawut; McLynn, Ryan P; Cui, Jonathan J; Shultz, Blake N; Lukasiewicz, Adam M; Grauer, Jonathan N

2018-01-01

As research tools, the American Society of Anesthesiologists (ASA) physical status classification system, the modified Charlson Comorbidity Index (mCCI), and the modified Frailty Index (mFI) have been associated with complications following spine procedures. However, with respect to clinical use for various adverse outcomes, no known study has compared the predictive performance of these indices specifically following posterior lumbar fusion (PLF). This study aimed to compare the discriminative ability of ASA, mCCI, and mFI, as well as demographic factors including age, body mass index, and gender for perioperative adverse outcomes following PLF. A retrospective review of prospectively collected data was performed. Patients undergoing elective PLF with or without interbody fusion were extracted from the 2011-2014 American College of Surgeons National Surgical Quality Improvement Program (NSQIP). Perioperative adverse outcome variables assessed included the occurrence of minor adverse events, severe adverse events, infectious adverse events, any adverse event, extended length of hospital stay, and discharge to higher-level care. Patient comorbidity indices and characteristics were delineated and assessed for discriminative ability in predicting perioperative adverse outcomes using an area under the curve analysis from the receiver operating characteristics curves. In total, 16,495 patients were identified who met the inclusion criteria. The most predictive comorbidity index was ASA and demographic factor was age. Of these two factors, age had the larger discriminative ability for three out of the six adverse outcomes and ASA was the most predictive for one out of six adverse outcomes. A combination of the most predictive demographic factor and comorbidity index resulted in improvements in discriminative ability over the individual components for five of the six outcome variables. For PLF, easily obtained patient ASA and age have overall similar or better

Variable Lifting Index for Manual-Lifting Risk Assessment: A Preliminary Validation Study.

PubMed

Battevi, Natale; Pandolfi, Monica; Cortinovis, Ivan

2016-08-01

The aim of this study was to evaluate the efficacy of the new Variable Lifting Index (VLI) method, theoretically based on the Revised National Institute for Occupational Safety and Health [NIOSH] Lifting Equation (RNLE), in predicting the risk of acute low-back pain (LBP) in the past 12 months. A new risk variable termed the VLI for assessing variable manual lifting has been developed, but there has been no epidemiological study that evaluates the relationship between the VLI and LBP. A sample of 3,402 study participants from 16 companies in different industrial sectors was analyzed. Of the participants, 2,374 were in the risk exposure group involving manual materials handling (MMH), and 1,028 were in the control group without MMH. The VLI was calculated for each participant in the exposure group using a systematic approach. LBP information was collected by occupational physicians at the study sites. The risk of acute LBP was estimated by calculating the odds ratio (OR) between levels of the risk exposure and the control group using a logistic regression analysis. Both crude and adjusted ORs for body mass index, gender, and age were analyzed. Both crude and adjusted ORs showed a dose-response relationship. As the levels of VLI increased, the risk of LBP increased. This risk relationship existed when VLI was greater than 1. The VLI method can be used to assess the risk of acute LBP, although further studies are needed to confirm the outcome and to define better VLI categories. © 2016, Human Factors and Ergonomics Society.

Pulmonary edema predictive scoring index (PEPSI), a new index to predict risk of reperfusion pulmonary edema and improvement of hemodynamics in percutaneous transluminal pulmonary angioplasty.

PubMed

Inami, Takumi; Kataoka, Masaharu; Shimura, Nobuhiko; Ishiguro, Haruhisa; Yanagisawa, Ryoji; Taguchi, Hiroki; Fukuda, Keiichi; Yoshino, Hideaki; Satoh, Toru

2013-07-01

This study sought to identify useful predictors for hemodynamic improvement and risk of reperfusion pulmonary edema (RPE), a major complication of this procedure. Percutaneous transluminal pulmonary angioplasty (PTPA) has been reported to be effective for the treatment of chronic thromboembolic pulmonary hypertension (CTEPH). PTPA has not been widespread because RPE has not been well predicted. We included 140 consecutive procedures in 54 patients with CTEPH. The flow appearance of the target vessels was graded into 4 groups (Pulmonary Flow Grade), and we proposed PEPSI (Pulmonary Edema Predictive Scoring Index) = (sum total change of Pulmonary Flow Grade scores) × (baseline pulmonary vascular resistance). Correlations between occurrence of RPE and 11 variables, including hemodynamic parameters, number of target vessels, and PEPSI, were analyzed. Hemodynamic parameters significantly improved after median observation period of 6.4 months, and the sum total changes in Pulmonary Flow Grade scores were significantly correlated with the improvement in hemodynamics. Multivariate analysis revealed that PEPSI was the strongest factor correlated with the occurrence of RPE (p < 0.0001). Receiver-operating characteristic curve analysis demonstrated PEPSI to be a useful marker of the risk of RPE (cutoff value 35.4, negative predictive value 92.3%). Pulmonary Flow Grade score is useful in determining therapeutic efficacy, and PEPSI is highly supportive to reduce the risk of RPE after PTPA. Using these 2 indexes, PTPA could become a safe and common therapeutic strategy for CTEPH. Copyright © 2013 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Prediction of Baseflow Index of Catchments using Machine Learning Algorithms

NASA Astrophysics Data System (ADS)

Yadav, B.; Hatfield, K.

2017-12-01

We present the results of eight machine learning techniques for predicting the baseflow index (BFI) of ungauged basins using a surrogate of catchment scale climate and physiographic data. The tested algorithms include ordinary least squares, ridge regression, least absolute shrinkage and selection operator (lasso), elasticnet, support vector machine, gradient boosted regression trees, random forests, and extremely randomized trees. Our work seeks to identify the dominant controls of BFI that can be readily obtained from ancillary geospatial databases and remote sensing measurements, such that the developed techniques can be extended to ungauged catchments. More than 800 gauged catchments spanning the continental United States were selected to develop the general methodology. The BFI calculation was based on the baseflow separated from daily streamflow hydrograph using HYSEP filter. The surrogate catchment attributes were compiled from multiple sources including digital elevation model, soil, landuse, climate data, other publicly available ancillary and geospatial data. 80% catchments were used to train the ML algorithms, and the remaining 20% of the catchments were used as an independent test set to measure the generalization performance of fitted models. A k-fold cross-validation using exhaustive grid search was used to fit the hyperparameters of each model. Initial model development was based on 19 independent variables, but after variable selection and feature ranking, we generated revised sparse models of BFI prediction that are based on only six catchment attributes. These key predictive variables selected after the careful evaluation of bias-variance tradeoff include average catchment elevation, slope, fraction of sand, permeability, temperature, and precipitation. The most promising algorithms exceeding an accuracy score (r-square) of 0.7 on test data include support vector machine, gradient boosted regression trees, random forests, and extremely randomized

Variability in Predictions from Online Tools: A Demonstration Using Internet-Based Melanoma Predictors.

PubMed

Zabor, Emily C; Coit, Daniel; Gershenwald, Jeffrey E; McMasters, Kelly M; Michaelson, James S; Stromberg, Arnold J; Panageas, Katherine S

2018-02-22

Prognostic models are increasingly being made available online, where they can be publicly accessed by both patients and clinicians. These online tools are an important resource for patients to better understand their prognosis and for clinicians to make informed decisions about treatment and follow-up. The goal of this analysis was to highlight the possible variability in multiple online prognostic tools in a single disease. To demonstrate the variability in survival predictions across online prognostic tools, we applied a single validation dataset to three online melanoma prognostic tools. Data on melanoma patients treated at Memorial Sloan Kettering Cancer Center between 2000 and 2014 were retrospectively collected. Calibration was assessed using calibration plots and discrimination was assessed using the C-index. In this demonstration project, we found important differences across the three models that led to variability in individual patients' predicted survival across the tools, especially in the lower range of predictions. In a validation test using a single-institution data set, calibration and discrimination varied across the three models. This study underscores the potential variability both within and across online tools, and highlights the importance of using methodological rigor when developing a prognostic model that will be made publicly available online. The results also reinforce that careful development and thoughtful interpretation, including understanding a given tool's limitations, are required in order for online prognostic tools that provide survival predictions to be a useful resource for both patients and clinicians.

Efficacy of the Omega-3 Index in predicting non-alcoholic fatty liver disease in overweight and obese adults: a pilot study.

PubMed

Parker, Helen M; O'Connor, Helen T; Keating, Shelley E; Cohn, Jeffrey S; Garg, Manohar L; Caterson, Ian D; George, Jacob; Johnson, Nathan A

2015-09-14

Non-alcoholic fatty liver disease (NAFLD) is an independent predictor of CVD in otherwise healthy individuals. Low n-3 PUFA intake has been associated with the presence of NAFLD; however, the relationship between a biomarker of n-3 status - the Omega-3 Index - and liver fat is yet to be elucidated. A total of eighty overweight adults (fifty-six men) completed the anthropometric and biochemical measurements, including the Omega-3 Index, and underwent proton magnetic resonance spectroscopy assessment of liver fat. Bivariate correlations and multiple regression analyses were performed with reference to prediction of liver fat percentage. The mean Omega-3 Index was high in both NAFLD (intrahepatic lipid concentration≥5·5 %) and non-NAFLD groups. The Omega-3 Index, BMI, waist circumference, glucose, insulin, TAG, high-sensitive C-reactive protein (hsCRP) and alanine aminotransferase (ALT) were positively correlated, and HDL and erythrocyte n-6:n-3 ratio negatively correlated with liver fat concentration. Regression analysis found that simple anthropometric and demographic variables (waist, age) accounted for 31 % of the variance in liver fat and the addition of traditional cardiometabolic blood markers (TAG, HDL, hsCRP and ALT) increased the predictive power to 43 %. The addition of the novel erythrocyte fatty acid variable (Omega-3 Index) to the model only accounted for a further 3 % of the variance (P=0·049). In conclusion, the Omega-3 Index was associated with liver fat concentration but did not improve the overall capacity of demographic, anthropometric and blood markers to predict NAFLD.

Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

PubMed

Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

2016-06-01

The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.

Prediction of County-Level Corn Yields Using an Energy-Crop Growth Index.

NASA Astrophysics Data System (ADS)

Andresen, Jeffrey A.; Dale, Robert F.; Fletcher, Jerald J.; Preckel, Paul V.

1989-01-01

Weather conditions significantly affect corn yields. while weather remains as the major uncontrolled variable in crop production, an understanding of the influence of weather on yields can aid in early and accurate assessment of the impact of weather and climate on crop yields and allow for timely agricultural extension advisories to help reduce farm management costs and improve marketing, decisions. Based on data for four representative countries in Indiana from 1960 to 1984 (excluding 1970 because of the disastrous southern corn leaf blight), a model was developed to estimate corn (Zea mays L.) yields as a function of several composite soil-crop-weather variables and a technology-trend marker, applied nitrogen fertilizer (N). The model was tested by predicting corn yields for 15 other counties. A daily energy-crop growth (ECG) variable in which different weights were used for the three crop-weather variables which make up the daily ECG-solar radiation intercepted by the canopy, a temperature function, and the ratio of actual to potential evapotranspiration-performed better than when the ECG components were weighted equally. The summation of the weighted daily ECG over a relatively short period (36 days spanning silk) was found to provide the best index for predicting county average corn yield. Numerical estimation results indicate that the ratio of actual to potential evapotranspiration (ET/PET) is much more important than the other two ECG factors in estimating county average corn yield in Indiana.

Disturbance metrics predict a wetland Vegetation Index of Biotic Integrity

USGS Publications Warehouse

Stapanian, Martin A.; Mack, John; Adams, Jean V.; Gara, Brian; Micacchion, Mick

2013-01-01

Indices of biological integrity of wetlands based on vascular plants (VIBIs) have been developed in many areas in the USA. Knowledge of the best predictors of VIBIs would enable management agencies to make better decisions regarding mitigation site selection and performance monitoring criteria. We use a novel statistical technique to develop predictive models for an established index of wetland vegetation integrity (Ohio VIBI), using as independent variables 20 indices and metrics of habitat quality, wetland disturbance, and buffer area land use from 149 wetlands in Ohio, USA. For emergent and forest wetlands, predictive models explained 61% and 54% of the variability, respectively, in Ohio VIBI scores. In both cases the most important predictor of Ohio VIBI score was a metric that assessed habitat alteration and development in the wetland. Of secondary importance as a predictor was a metric that assessed microtopography, interspersion, and quality of vegetation communities in the wetland. Metrics and indices assessing disturbance and land use of the buffer area were generally poor predictors of Ohio VIBI scores. Our results suggest that vegetation integrity of emergent and forest wetlands could be most directly enhanced by minimizing substrate and habitat disturbance within the wetland. Such efforts could include reducing or eliminating any practices that disturb the soil profile, such as nutrient enrichment from adjacent farm land, mowing, grazing, or cutting or removing woody plants.

Cortical response variability as a developmental index of selective auditory attention

PubMed Central

Strait, Dana L.; Slater, Jessica; Abecassis, Victor; Kraus, Nina

2014-01-01

Attention induces synchronicity in neuronal firing for the encoding of a given stimulus at the exclusion of others. Recently, we reported decreased variability in scalp-recorded cortical evoked potentials to attended compared with ignored speech in adults. Here we aimed to determine the developmental time course for this neural index of auditory attention. We compared cortical auditory-evoked variability with attention across three age groups: preschoolers, school-aged children and young adults. Results reveal an increased impact of selective auditory attention on cortical response variability with development. Although all three age groups have equivalent response variability to attended speech, only school-aged children and adults have a distinction between attend and ignore conditions. Preschoolers, on the other hand, demonstrate no impact of attention on cortical responses, which we argue reflects the gradual emergence of attention within this age range. Outcomes are interpreted in the context of the behavioral relevance of cortical response variability and its potential to serve as a developmental index of cognitive skill. PMID:24267508

The interannual variability of the Haines Index over North America

Treesearch

Lejiang Yu; Shiyuan Zhong; Xindi Bian; Warren E. Heilman; Joseph J. Charney

2013-01-01

The Haines index (HI) is a fire-weather index that is widely used as an indicator of the potential for dry, low-static-stability air in the lower atmosphere to contribute to erratic fire behavior or large fire growth. This study examines the interannual variability of HI over North America and its relationship to indicators of large-scale circulation anomalies. The...

A simplified donor risk index for predicting outcome after deceased donor kidney transplantation.

PubMed

Watson, Christopher J E; Johnson, Rachel J; Birch, Rhiannon; Collett, Dave; Bradley, J Andrew

2012-02-15

We sought to determine the deceased donor factors associated with outcome after kidney transplantation and to develop a clinically applicable Kidney Donor Risk Index. Data from the UK Transplant Registry on 7620 adult recipients of adult deceased donor kidney transplants between 2000 and 2007 inclusive were analyzed. Donor factors potentially influencing transplant outcome were investigated using Cox regression, adjusting for significant recipient and transplant factors. A United Kingdom Kidney Donor Risk Index was derived from the model and validated. Donor age was the most significant factor predicting poor transplant outcome (hazard ratio for 18-39 and 60+ years relative to 40-59 years was 0.78 and 1.49, respectively, P<0.001). A history of donor hypertension was also associated with increased risk (hazard ratio 1.30, P=0.001), and increased donor body weight, longer hospital stay before death, and use of adrenaline were also significantly associated with poorer outcomes up to 3 years posttransplant. Other donor factors including donation after circulatory death, history of cardiothoracic disease, diabetes history, and terminal creatinine were not significant. A donor risk index based on the five significant donor factors was derived and confirmed to be prognostic of outcome in a validation cohort (concordance statistic 0.62). An index developed in the United States by Rao et al., Transplantation 2009; 88: 231-236, included 15 factors and gave a concordance statistic of 0.63 in the UK context, suggesting that our much simpler model has equivalent predictive ability. A Kidney Donor Risk Index based on five donor variables provides a clinically useful tool that may help with organ allocation and informed consent.

Building and verifying a severity prediction model of acute pancreatitis (AP) based on BISAP, MEWS and routine test indexes.

PubMed

Ye, Jiang-Feng; Zhao, Yu-Xin; Ju, Jian; Wang, Wei

2017-10-01

To discuss the value of the Bedside Index for Severity in Acute Pancreatitis (BISAP), Modified Early Warning Score (MEWS), serum Ca2+, similarly hereinafter, and red cell distribution width (RDW) for predicting the severity grade of acute pancreatitis and to develop and verify a more accurate scoring system to predict the severity of AP. In 302 patients with AP, we calculated BISAP and MEWS scores and conducted regression analyses on the relationships of BISAP scoring, RDW, MEWS, and serum Ca2+ with the severity of AP using single-factor logistics. The variables with statistical significance in the single-factor logistic regression were used in a multi-factor logistic regression model; forward stepwise regression was used to screen variables and build a multi-factor prediction model. A receiver operating characteristic curve (ROC curve) was constructed, and the significance of multi- and single-factor prediction models in predicting the severity of AP using the area under the ROC curve (AUC) was evaluated. The internal validity of the model was verified through bootstrapping. Among 302 patients with AP, 209 had mild acute pancreatitis (MAP) and 93 had severe acute pancreatitis (SAP). According to single-factor logistic regression analysis, we found that BISAP, MEWS and serum Ca2+ are prediction indexes of the severity of AP (P-value<0.001), whereas RDW is not a prediction index of AP severity (P-value>0.05). The multi-factor logistic regression analysis showed that BISAP and serum Ca2+ are independent prediction indexes of AP severity (P-value<0.001), and MEWS is not an independent prediction index of AP severity (P-value>0.05); BISAP is negatively related to serum Ca2+ (r=-0.330, P-value<0.001). The constructed model is as follows: ln()=7.306+1.151*BISAP-4.516*serum Ca2+. The predictive ability of each model for SAP follows the order of the combined BISAP and serum Ca2+ prediction model>Ca2+>BISAP. There is no statistical significance for the predictive ability of

Improving the prediction of African savanna vegetation variables using time series of MODIS products

NASA Astrophysics Data System (ADS)

Tsalyuk, Miriam; Kelly, Maggi; Getz, Wayne M.

2017-09-01

African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid

Predictive Value of Beat-to-Beat QT Variability Index across the Continuum of Left Ventricular Dysfunction: Competing Risks of Non-cardiac or Cardiovascular Death, and Sudden or Non-Sudden Cardiac Death

PubMed Central

Tereshchenko, Larisa G.; Cygankiewicz, Iwona; McNitt, Scott; Vazquez, Rafael; Bayes-Genis, Antoni; Han, Lichy; Sur, Sanjoli; Couderc, Jean-Philippe; Berger, Ronald D.; de Luna, Antoni Bayes; Zareba, Wojciech

2012-01-01

Background The goal of this study was to determine the predictive value of beat-to-beat QT variability in heart failure (HF) patients across the continuum of left ventricular dysfunction. Methods and Results Beat-to-beat QT variability index (QTVI), heart rate variance (LogHRV), normalized QT variance (QTVN), and coherence between heart rate variability and QT variability have been measured at rest during sinus rhythm in 533 participants of the Muerte Subita en Insuficiencia Cardiaca (MUSIC) HF study (mean age 63.1±11.7; males 70.6%; LVEF >35% in 254 [48%]) and in 181 healthy participants from the Intercity Digital Electrocardiogram Alliance (IDEAL) database. During a median of 3.7 years of follow-up, 116 patients died, 52 from sudden cardiac death (SCD). In multivariate competing risk analyses, the highest QTVI quartile was associated with cardiovascular death [hazard ratio (HR) 1.67(95%CI 1.14-2.47), P=0.009] and in particular with non-sudden cardiac death [HR 2.91(1.69-5.01), P<0.001]. Elevated QTVI separated 97.5% of healthy individuals from subjects at risk for cardiovascular [HR 1.57(1.04-2.35), P=0.031], and non-sudden cardiac death in multivariate competing risk model [HR 2.58(1.13-3.78), P=0.001]. No interaction between QTVI and LVEF was found. QTVI predicted neither non-cardiac death (P=0.546) nor SCD (P=0.945). Decreased heart rate variability (HRV) rather than increased QT variability was the reason for increased QTVI in this study. Conclusions Increased QTVI due to depressed HRV predicts cardiovascular mortality and non-sudden cardiac death, but neither SCD nor excracardiac mortality in HF across the continuum of left ventricular dysfunction. Abnormally augmented QTVI separates 97.5% of healthy individuals from HF patients at risk. PMID:22730411

Usefulness of the Hardman index in predicting outcome after endovascular repair of ruptured abdominal aortic aneurysms.

PubMed

Karkos, Christos D; Karamanos, Dimitrios; Papazoglou, Konstantinos O; Kantas, Alexandros S; Theochari, Evangelia G; Kamparoudis, Apostolos G; Gerassimidis, Thomas S

2008-10-01

The Hardman index, which has five variables, has been recommended as a predictor of outcome after open repair of ruptured abdominal aortic aneurysms (RAAAs). It has been reported that the presence of three or more variables is uniformly fatal. The aim of this study was to test the same model in an independent series of RAAA patients undergoing endovascular repair. A consecutive series of 41 patients undergoing endovascular repair for RAAA during an 8-year period was analyzed retrospectively. Thirty-day mortality and patient variables, including the five Hardman risk factors of age >76 years, serum creatinine >190 micromol/L, hemoglobin <9 g/dL, loss of consciousness, and electrocardiographic (ECG) evidence of ischemia, were recorded. The Hardman index and a revised version of the index with four variables (without ECG ischemia) were calculated and related to clinical outcome. Operative mortality was 41% (17 of 41). On univariate analysis, only age >76 years (P = .01) and the use of local anesthesia (P < .0001) were statistically significant. Loss of consciousness (P = .05) showed a trend toward a higher mortality, albeit not statistically significant. On multivariate analysis, the use of local anesthesia was the only significant predictor of survival (odds ratio [OR], 0.03; 95% confidence interval [CI], 0.003-0.25, P = .001). Again, loss of consciousness showed an association with a higher chance of dying but did not achieve statistical significance (OR, 6.30; 95% CI, 0.93-42.51, P = .059). The original and revised versions of the Hardman index were both significantly associated with death (P = .02 and P = .001, chi(2) test for trend). The cumulative effect of 0, 1, 2, and >/=3 risk factors on mortality was 0%, 27%, 36%, and 71% for the original index, and 12.5%, 21%, 60%, and 78% for the revised version, respectively. Four and two patients with a score of >/=3 in each version of the index survived endovascular repair. The Hardman index, with or without

Site index prediction tables for oak in northwestern West Virginia

Treesearch

Neil Lamson

1980-01-01

Prediction tables for even-aged stands of white, chestnut, northern red, scarlet, and black oaks can be used to estimate the site index of forest land in 13 counties of northwestern West Virginia. The half-width of the 95 percent confidence interval of the predicted site index is included; it can be used to determine the number of sample trees necessary to attain given...

LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data

PubMed Central

van Walraven, Carl; Wong, Jenna; Forster, Alan J

2012-01-01

Background Death or urgent readmission after hospital discharge is a common adverse event that can be used to compare outcomes of care between institutions. To accurately adjust for risk and to allow for interhospital comparisons of readmission rates, we used administrative data to derive and internally validate an extension of the LACE index, a previously validated index for 30-day death or urgent readmission. Methods We randomly selected 500 000 medical and surgical patients discharged to the community from any Ontario hospital between 1 April 2003 and 31 March 2009. We derived a logistic regression model on 250 000 randomly selected patients from this group and modified the final model into an index scoring system, the LACE+ index. We internally validated the LACE+ index using data from the remaining 250 000 patients and compared its performance with that of the original LACE index. Results Within 30 days of discharge to the community, 33 825 (6.8%) of the patients had died or had been urgently readmitted. In addition to the variables included in the LACE index (length of stay in hospital [L], acuity of admission [A], comorbidity [C] and emergency department utilization in the 6 months before admission [E]), the LACE+ index incorporated patient age and sex, teaching status of the discharge hospital, acute diagnoses and procedures performed during the index admission, number of days on alternative level of care during the index admission, and number of elective and urgent admissions to hospital in the year before the index admission. The LACE+ index was highly discriminative (C statistic 0.771, 95% confidence interval 0.767–0.775), was well calibrated across most of its range of scores and had a model performance that exceeded that of the LACE index. Interpretation The LACE+ index can be used to predict the risk of postdischarge death or urgent readmission on the basis of administrative data for the Ontario population. Its performance exceeds that of the LACE

Quantifying human disturbance in watersheds: Variable selection and performance of a GIS-based disturbance index for predicting the biological condition of perennial streams

USGS Publications Warehouse

Falcone, James A.; Carlisle, Daren M.; Weber, Lisa C.

2010-01-01

Characterizing the relative severity of human disturbance in watersheds is often part of stream assessments and is frequently done with the aid of Geographic Information System (GIS)-derived data. However, the choice of variables and how they are used to quantify disturbance are often subjective. In this study, we developed a number of disturbance indices by testing sets of variables, scoring methods, and weightings of 33 potential disturbance factors derived from readily available GIS data. The indices were calibrated using 770 watersheds located in the western United States for which the severity of disturbance had previously been classified from detailed local data by the United States Environmental Protection Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP). The indices were calibrated by determining which variable or variable combinations and aggregation method best differentiated between least- and most-disturbed sites. Indices composed of several variables performed better than any individual variable, and best results came from a threshold method of scoring using six uncorrelated variables: housing unit density, road density, pesticide application, dam storage, land cover along a mainstem buffer, and distance to nearest canal/pipeline. The final index was validated with 192 withheld watersheds and correctly classified about two-thirds (68%) of least- and most-disturbed sites. These results provide information about the potential for using a disturbance index as a screening tool for a priori ranking of watersheds at a regional/national scale, and which landscape variables and methods of combination may be most helpful in doing so.

Variability and predictability of the streamflows in Coastal and Andean Ecuador

NASA Astrophysics Data System (ADS)

Quishpe-Vásquez, César; Córdoba-Machado, Samir; Palomino-Lemus, Reiner; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

The main objective of this study is to examine the variability and the predictability in available water resources in Coastal and Andean Ecuador. For this aim, we use the streamflow data from a network of hydrological stations, provided by the National Institute of Meteorology and Hydrology of Ecuador (IHNAMI), distributed over the Ecuadorian territory and strategically located in the watersheds of its main rivers. A number of 20 stations with a continuous period of daily data covering a period of 42 years (1973-2015) were selected. To analyze the spatio-temporal variability of streamflow in Ecuador, principal component analysis (PCA) along with a study of trends have been applied to the streamflow data at monthly time scales. The significance and magnitude of trends have been analyzed using Man-Kendall test and Sen slope. Moreover, to analyze the predictability of the streamflow, the spatio-temporal effects of the ENSO phenomenon on the country have been evaluated through a correlation analysis using different lags between different El Niño indices (Niño 1+2, Niño Modoki, Trans-Niño and Niño 3.4) and the seasonal streamflow. The results show two main regions that differ in terms of variability. We found that the variations in water resources have a close relationship between the magnitude and the seasonal distribution of the streamflow and the ENSO. However, each index shows a different impact on the streamflow depending on the season and the region. In general, the higher correlations between the ENSO indices and the streamflow are observed in the stations closer to the coast. KEY WORDS: Ecuador streamflow; trends; PCA; variability; predictability; ENSO. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Prediction of Mortality with A Body Shape Index in Young Asians: Comparison with Body Mass Index and Waist Circumference.

PubMed

Lee, Da-Young; Lee, Mi-Yeon; Sung, Ki-Chul

2018-06-01

This paper investigated the impact of A Body Shape Index (ABSI) on the risk of all-cause mortality compared with the impact of waist circumference (WC) and body mass index (BMI). This paper reviewed data of 213,569 Korean adults who participated in health checkups between 2002 and 2012 at Kangbuk Samsung Hospital in Seoul, Korea. A multivariate Cox proportional hazard analysis was performed on the BMI, WC, and ABSI z score continuous variables as well as quintiles. During 1,168,668.7 person-years, 1,107 deaths occurred. As continuous variables, a significant positive relationship with the risk of all-cause death was found only in ABSI z scores after adjustment for age, sex, current smoking, alcohol consumption, regular exercise, presence of diabetes or hypertension, and history of cardiovascular diseases. In Cox analysis of quintiles, quintile 5 of the ABSI z score showed significantly increased hazard ratios (HRs) for mortality risk (HR [95% CI] was 1.32 [1.05-1.66]), whereas the risk for all-cause mortality, on the other hand, decreased in quintiles 3 through 5 of BMI and WC compared with their first quintiles after adjusting for several confounders. This study showed that the predictive value of ABSI for mortality risk was strong for a sample of young Asian participants and that its usefulness was better than BMI or WC. © 2018 The Obesity Society.

The PAPAS index: a novel index for the prediction of hepatitis C-related fibrosis.

PubMed

Ozel, Banu D; Poyrazoğlu, Orhan K; Karaman, Ahmet; Karaman, Hatice; Altinkaya, Engin; Sevinç, Eylem; Zararsiz, Gökmen

2015-08-01

Several noninvasive tests have been developed to determine the degree of hepatic fibrosis in patients with chronic hepatitis C (CHC) without performing liver biopsy. This study aimed to determine the performance of the PAPAS (Platelet/Age/Phosphatase/AFP/AST) index in patients with CHC for the prediction of significant fibrosis and cirrhosis and to compare it with other noninvasive tests. To date, no study has evaluated the application of the PAPAS index in CHC-associated liver fibrosis. This retrospective study included 137 consecutive patients with CHC who had undergone a percutaneous liver biopsy before treatment. The aspartate aminotransferase/platelet ratio (APRI), aspartate aminotransferase/alanine transaminase ratio (AAR), age-platelet index (API), FIB4, cirrhosis discriminate score (CDS), the Göteborg University cirrhosis index (GUCI), and PAPAS were calculated and compared with the diagnostic accuracies of all fibrosis indices between the groups F0-F2 (no-mild fibrosis) versus F3-F6 (significant fibrosis) and F0-F4 (no cirrhosis) versus F5-F6 (cirrhosis). To predict significant fibrosis, the area under curve (95% confidence interval) for FIB4 was 0.727 followed by GUCI (0.721), PAPAS≈APRI≈CDS (0.716), and API (0.68). To predict cirrhosis, the area under curve (95% confidence interval) for FIB4 was calculated to be 0.735, followed by GUCI (0.723), PAPAS≈APRI≈CDS≈(0.71), and API (0.66). No statistically significant difference was observed among these predictors to exclude both significant fibrosis and cirrhosis (P>0.05). The diagnostic capability of the PAPAS index has moderate efficiency and was not superior to other fibrosis markers for the identification of fibrosis in CHC patients. There is a need for more comprehensive prospective studies to help determine the diagnostic value of PAPAS for liver fibrosis.

Association between different measurements of blood pressure variability by ABP monitoring and ankle-brachial index.

PubMed

Wittke, Estefânia; Fuchs, Sandra C; Fuchs, Flávio D; Moreira, Leila B; Ferlin, Elton; Cichelero, Fábio T; Moreira, Carolina M; Neyeloff, Jeruza; Moreira, Marina B; Gus, Miguel

2010-11-05

Blood pressure (BP) variability has been associated with cardiovascular outcomes, but there is no consensus about the more effective method to measure it by ambulatory blood pressure monitoring (ABPM). We evaluated the association between three different methods to estimate BP variability by ABPM and the ankle brachial index (ABI). In a cross-sectional study of patients with hypertension, BP variability was estimated by the time rate index (the first derivative of SBP over time), standard deviation (SD) of 24-hour SBP; and coefficient of variability of 24-hour SBP. ABI was measured with a doppler probe. The sample included 425 patients with a mean age of 57 ± 12 years, being 69.2% women, 26.1% current smokers and 22.1% diabetics. Abnormal ABI (≤ 0.90 or ≥ 1.40) was present in 58 patients. The time rate index was 0.516 ± 0.146 mmHg/min in patients with abnormal ABI versus 0.476 ± 0.124 mmHg/min in patients with normal ABI (P = 0.007). In a logistic regression model the time rate index was associated with ABI, regardless of age (OR = 6.9, 95% CI = 1.1- 42.1; P = 0.04). In a multiple linear regression model, adjusting for age, SBP and diabetes, the time rate index was strongly associated with ABI (P < 0.01). None of the other indexes of BP variability were associated with ABI in univariate and multivariate analyses. Time rate index is a sensible method to measure BP variability by ABPM. Its performance for risk stratification of patients with hypertension should be explored in longitudinal studies.

Lymphatic invasion and the Shields index in predicting melanoma metastases.

PubMed

Špirić, Zorica; Erić, Mirela; Eri, Živka

2017-11-01

Findings of the prognostic significance of lymphatic invasion are contradictory. To determine an as efficient cutaneous melanoma metastasis predictor as possible, Shields et al. created a new prognostic index. This study aimed to examine whether the lymphatic invasion analysis and the Shields index calculation can be used in predicting lymph node status in patients with cutaneous melanoma. Lymphatic invasion of 100 melanoma specimens was detected by dual immunohistochemistry staining for the lymphatic endothelial marker D2-40 and melanoma cell S-100 protein. The Shields index was calculated as a logarithm by multiplying the melanoma thickness, square of peritumoural lymphatic vessel density and the number "2" for the present lymphatic invasion. No statistically significant difference was observed between lymph node metastatic and nonmetastatic melanomas regarding the lymphatic invasion. Metastatic melanomas showed a significantly higher Shields index value than nonmetastatic melanomas (p = 0.00). Area under the receiver operator characteristic (ROC) curve (AUC) proved that the Shields index (AUC = 0.86, 95% confidence interval (CI) 0.79-0.93, p = 0.00) was the most accurate predictor of lymph node status, followed by the melanoma thickness (AUC = 0.76, 95% CI 0.67-0.86, p = 0.00) and American Joint Committee on Cancer (AJCC) staging (AUC = 0.75, 95% CI 0.66-0.85, p = 0.00), while lymphatic invasion was not successful in predicting (AUC = 0.56, 95% CI 0.45-0.67, p = 0.31). The Shields index achieved 81.3% sensitivity and 75% specificity (cut-off mean value). Our findings show that D2-40/S-100 immunohistochemical analysis of lymphatic invasion cannot be used for predicting the lymph node status, while the Shields index calculation predicts disease outcome more accurately than the melanoma thickness and AJCC staging. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights

The Bird Community Resilience Index: a novel remote sensing-based biodiversity variable for quantifying ecological integrity

NASA Astrophysics Data System (ADS)

Michel, N. L.; Wilsey, C.; Burkhalter, C.; Trusty, B.; Langham, G.

2017-12-01

Scalable indicators of biodiversity change are critical to reporting overall progress towards national and global targets for biodiversity conservation (e.g. Aichi Targets) and sustainable development (SDGs). These essential biodiversity variables capitalize on new remote sensing technologies and growth of community science participation. Here we present a novel biodiversity metric quantifying resilience of bird communities and, by extension, of their associated ecological communities. This metric adds breadth to the community composition class of essential biodiversity variables that track trends in condition and vulnerability of ecological communities. We developed this index for use with North American grassland birds, a guild that has experienced stronger population declines than any other avian guild, in order to evaluate gains from the implementation of best management practices on private lands. The Bird Community Resilience Index was designed to incorporate the full suite of species-specific responses to management actions, and be flexible enough to work across broad climatic, land cover, and bird community gradients (i.e., grasslands from northern Mexico through Canada). The Bird Community Resilience Index consists of four components: density estimates of grassland and arid land birds; weighting based on conservation need; a functional diversity metric to incorporate resiliency of bird communities and their ecosystems; and a standardized scoring system to control for interannual variation caused by extrinsic factors (e.g., climate). We present an analysis of bird community resilience across ranches in the Northern Great Plains region of the United States. As predicted, Bird Community Resilience was higher in lands implementing best management practices than elsewhere. While developed for grassland birds, this metric holds great potential for use as an Essential Biodiversity Variable for community composition in a variety of habitat.

Idiopathic Pulmonary Fibrosis: Gender-Age-Physiology Index Stage for Predicting Future Lung Function Decline.

PubMed

Salisbury, Margaret L; Xia, Meng; Zhou, Yueren; Murray, Susan; Tayob, Nabihah; Brown, Kevin K; Wells, Athol U; Schmidt, Shelley L; Martinez, Fernando J; Flaherty, Kevin R

2016-02-01

Idiopathic pulmonary fibrosis is a progressive lung disease with variable course. The Gender-Age-Physiology (GAP) Index and staging system uses clinical variables to stage mortality risk. It is unknown whether clinical staging predicts future decline in pulmonary function. We assessed whether the GAP stage predicts future pulmonary function decline and whether interval pulmonary function change predicts mortality after accounting for stage. Patients with idiopathic pulmonary fibrosis (N = 657) were identified retrospectively at three tertiary referral centers, and baseline GAP stages were assessed. Mixed models were used to describe average trajectories of FVC and diffusing capacity of the lung for carbon monoxide (Dlco). Multivariable Cox proportional hazards models were used to assess whether declines in pulmonary function ≥ 10% in 6 months predict mortality after accounting for GAP stage. Over a 2-year period, GAP stage was not associated with differences in yearly lung function decline. After accounting for stage, a 10% decrease in FVC or Dlco over 6 months independently predicted death or transplantation (FVC hazard ratio, 1.37; Dlco hazard ratio, 1.30; both, P ≤ .03). Patients with GAP stage 2 with declining pulmonary function experienced a survival profile similar to patients with GAP stage 3, with 1-year event-free survival of 59.3% (95% CI, 49.4-67.8) vs 56.9% (95% CI, 42.2-69.1). Baseline GAP stage predicted death or lung transplantation but not the rate of future pulmonary function decline. After accounting for GAP stage, a decline of ≥ 10% over 6 months independently predicted death or lung transplantation. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

The dynamic relationship between Bursa Malaysia composite index and macroeconomic variables

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Rose, Farid Zamani Che; Rahman, Rosmanjawati Abd.

2017-08-01

This study investigates and analyzes the long run and short run relationships between Bursa Malaysia Composite index (KLCI) and nine macroeconomic variables in a VAR/VECM framework. After regression analysis seven out the nine macroeconomic variables are chosen for further analysis. The use of Johansen-Juselius Cointegration and Vector Error Correction Model (VECM) technique indicate that there are long run relationships between the seven macroeconomic variables and KLCI. Meanwhile, Granger causality test shows that bidirectional relationship between KLCI and oil price. Furthermore, after 12 months the shock on KLCI are explained by innovations of the seven macroeconomic variables. This indicate the close relationship between macroeconomic variables and KLCI.

Stratified aspartate aminotransferase-to-platelet ratio index accurately predicts survival in hepatocellular carcinoma patients undergoing curative liver resection.

PubMed

Yang, Hao-Jie; Jiang, Jing-Hang; Yang, Yu-Ting; Guo, Zhe; Li, Ji-Jia; Liu, Xuan-Han; Lu, Fei; Zeng, Feng-Hua; Ye, Jin-Song; Zhang, Ke-Lan; Chen, Neng-Zhi; Xiang, Bang-De; Li, Le-Qun

2017-03-01

The aspartate aminotransferase-to-platelet ratio index has been reported to predict prognosis of patients with hepatocellular carcinoma. This study examined the prognostic potential of stratified aspartate aminotransferase-to-platelet ratio index for hepatocellular carcinoma patients undergoing curative liver resection. A total of 661 hepatocellular carcinoma patients were retrieved and the associations between aspartate aminotransferase-to-platelet ratio index and clinicopathological variables and survivals (overall survival and disease-free survival) were analyzed. Higher aspartate aminotransferase-to-platelet ratio index quartiles were significantly associated with poorer overall survival (p = 0.002) and disease-free survival (p = 0.001). Multivariate analysis showed aspartate aminotransferase-to-platelet ratio index to be an independent risk factor for overall survival (p = 0.018) and disease-free survival (p = 0.01). Patients in the highest aspartate aminotransferase-to-platelet ratio index quartile were at 44% greater risk of death than patients in the first quartile (hazard ratio = 1.445, 95% confidence interval = 1.081 - 1.931, p = 0.013), as well as 49% greater risk of recurrence (hazard ratio = 1.49, 95% confidence interval = 1.112-1.998, p = 0.008). Subgroup analysis also showed aspartate aminotransferase-to-platelet ratio index to be an independent predictor of poor overall survival and disease-free survival in patients positive for hepatitis B surface antigen or with cirrhosis (both p < 0.05). Similar results were obtained when aspartate aminotransferase-to-platelet ratio index was analyzed as a dichotomous variable with cutoff values of 0.25 and 0.62. Elevated preoperative aspartate aminotransferase-to-platelet ratio index may be independently associated with poor overall survival and disease-free survival in hepatocellular carcinoma patients following curative resection.

11. 28'X40' original vellum, VariableAngle Launcher, 'INDEX TO Drawings' drawn ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

11. 28'X40' original vellum, Variable-Angle Launcher, 'INDEX TO Drawings' drawn at no scale (P.W.DWG.No. 1781). - Variable Angle Launcher Complex, CA State Highway 39 at Morris Reservior, Azusa, Los Angeles County, CA

Vesicoureteral Reflux Index: Predicting Primary Vesicoureteral Reflux Resolution in Children Diagnosed after Age 24 Months.

PubMed

Garcia-Roig, Michael; Ridley, Derrick E; McCracken, Courtney; Arlen, Angela M; Cooper, Christopher S; Kirsch, Andrew J

2017-04-01

The Vesicoureteral Reflux Index is a validated tool that reliably predicts spontaneous resolution of reflux or at least 2 grades of improvement for patients diagnosed before age 24 months. We evaluated the Vesicoureteral Reflux Index in children older than 2 years. Patients younger than 18 years who were diagnosed with primary vesicoureteral reflux after age 24 months and had undergone 2 or more voiding cystourethrograms were identified. Disease severity was scored using the Vesicoureteral Reflux Index, a 6-point scale based on gender, reflux grade, ureteral abnormalities and reflux timing. Proportional subdistribution hazard models for competing risks identified variables associated with resolution/improvement at different time points. A total of 21 males and 250 females met inclusion criteria. Mean ± SD age was 4.0 ± 2.1 years and patients had a median vesicoureteral reflux grade of 2. The Vesicoureteral Reflux Index score improved by 1 point in 1 patient (100%), 2 points in 25 (67.6%), 3 points in 48 (37%), 4 points in 18 (21.4%) and 5 to 6 points in 4 (18.2%). Female gender (p = 0.005) and vesicoureteral reflux timing (late filling, p = 0.002; early/mid filling, p <0.001) independently predicted nonresolution. Median resolution time based on Vesicoureteral Reflux Index score was 2 months or less in 15.6% of patients (95% CI 11.0-13.8), 3 months in 34.7% (95% CI 25.4-44.1), 4 months in 55.9% (95% CI 40.1 to infinity) and 5 months or more in 30.3% (95% CI 29.5 to infinity). High grade (IV or V) reflux was not associated with resolution at any point. Ureteral abnormalities were associated with lack of resolution in the first 12 to 18 months (HR 0.29, 95% CI 0.29-0.80) but not in later followup. Vesicoureteral Reflux Index scores of 3, 4 and 5 were significantly associated with lack of resolution/improvement compared to scores of 2 or less (p = 0.031). The Vesicoureteral Reflux Index reliably predicts primary vesicoureteral reflux improvement/resolution in

Model-data assimilation of multiple phenological observations to constrain and predict leaf area index.

PubMed

Viskari, Toni; Hardiman, Brady; Desai, Ankur R; Dietze, Michael C

2015-03-01

Our limited ability to accurately simulate leaf phenology is a leading source of uncertainty in models of ecosystem carbon cycling. We evaluate if continuously updating canopy state variables with observations is beneficial for predicting phenological events. We employed ensemble adjustment Kalman filter (EAKF) to update predictions of leaf area index (LAI) and leaf extension using tower-based photosynthetically active radiation (PAR) and moderate resolution imaging spectrometer (MODIS) data for 2002-2005 at Willow Creek, Wisconsin, USA, a mature, even-aged, northern hardwood, deciduous forest. The ecosystem demography model version 2 (ED2) was used as the prediction model, forced by offline climate data. EAKF successfully incorporated information from both the observations and model predictions weighted by their respective uncertainties. The resulting. estimate reproduced the observed leaf phenological cycle in the spring and the fall better than a parametric model prediction. These results indicate that during spring the observations contribute most in determining the correct bud-burst date, after which the model performs well, but accurately modeling fall leaf senesce requires continuous model updating from observations. While the predicted net ecosystem exchange (NEE) of CO2 precedes tower observations and unassimilated model predictions in the spring, overall the prediction follows observed NEE better than the model alone. Our results show state data assimilation successfully simulates the evolution of plant leaf phenology and improves model predictions of forest NEE.

The effect of year-to-year variability of leaf area index on Variable Infiltration Capacity model performance and simulation of runoff

NASA Astrophysics Data System (ADS)

Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

2015-09-01

This study assessed the effect of using observed monthly leaf area index (LAI) on hydrological model performance and the simulation of runoff using the Variable Infiltration Capacity (VIC) hydrological model in the Goulburn-Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) leaf area index dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash-Sutcliffe efficiency, the logarithm transformed flow Nash-Sutcliffe efficiency and the percentage bias. Finally, the deviation of the simulated monthly runoff using the observed monthly LAI from simulated runoff using long-term mean monthly LAI was computed. The VIC model predicted monthly runoff in the selected sub-catchments with model efficiencies ranging from 61.5% to 95.9% during calibration (1982-1997) and 59% to 92.4% during validation (1998-2012). Our results suggest systematic improvements, from 4% to 25% in Nash-Sutcliffe efficiency, in sparsely forested sub-catchments when the VIC model was calibrated with observed monthly LAI instead of long-term mean monthly LAI. There was limited systematic improvement in tree dominated sub-catchments. The results also suggest that the model overestimation or underestimation of runoff during wet and dry periods can be reduced to 25 mm and 35 mm respectively by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

Construction of prediction intervals for Palmer Drought Severity Index using bootstrap

NASA Astrophysics Data System (ADS)

Beyaztas, Ufuk; Bickici Arikan, Bugrayhan; Beyaztas, Beste Hamiye; Kahya, Ercan

2018-04-01

In this study, we propose an approach based on the residual-based bootstrap method to obtain valid prediction intervals using monthly, short-term (three-months) and mid-term (six-months) drought observations. The effects of North Atlantic and Arctic Oscillation indexes on the constructed prediction intervals are also examined. Performance of the proposed approach is evaluated for the Palmer Drought Severity Index (PDSI) obtained from Konya closed basin located in Central Anatolia, Turkey. The finite sample properties of the proposed method are further illustrated by an extensive simulation study. Our results revealed that the proposed approach is capable of producing valid prediction intervals for future PDSI values.

Correlation between indexes of autonomic maneuvers and heart rate variability in hemodialysis patients.

PubMed

Vieira, Carlos Felipe Delmondes; Lima, Márcia Maria Oliveira; Costa, Henrique Silveira; Diniz, Karen Marina Alves; Guião, João Paulo Lemos; Alves, Frederico Lopes; Maciel, Emílio Henrique; Brandao, Vanessa Gomes; Figueiredo, Pedro Henrique Scheidt

2016-06-01

The autonomic maneuvers are simple methods to evaluate autonomic balance, but the association between autonomic maneuvers and heart rate variability (HRV) in hemodialysis patients remains unknown. This study aimed to evaluate the correlation between HRV and respiratory sinus arrhythmia (RSA) and Valsalva maneuver (VM) indexes in hemodialysis patients and to compare two methods for RSA indexes acquisitions. Forty-eight volunteers on hemodialysis (66.7 % men) were evaluated by VM, RSA, and 24 h Holter monitoring. At the VM, the Valsalva index (VI) was the variable considered. In the RSA, the ratio and difference between the RR intervals of inspiratory and expiratory phase (E:I and E-I, respectively) were considered by traditional form (average of respiratory cycles) and independent respiratory cycles (E:Iindep and E-Iindep). The HRV indexes evaluated were standard deviation of all normal RR intervals (SDNN), standard deviation of sequential 5-min RR interval means (SDANN), root mean square of the successive differences (rMSSD) and percentage of adjacent RR intervals with difference of duration greater than 50 ms (pNN50). The SDNN, SDANN showed significant correlation with all classic indexes of RSA (E:I: r = 0.62, 0.55, respectively, E-I: r = 0.64, 0.57, respectively), E:Iindep (r = 0.59, 0.54, respectively), E-Iindep (r = 0.47, 0.43, respectively) and VI (r = 0.42, 0.34, respectively). Significant correlation of rMSSD with E:I (r = 0.37), E-I (r = 0.41) and E:Iindep (r = 0.34) was also observed. There was no association of any variable with pNN50. Have been show high values for all variables of independent cycles method (p < 0.05). The autonomic maneuvers, especially RSA, are useful methods to evaluate cardiac autonomic function in hemodialysis patients. The acquisition of the RSA index by independent cycles should not be used in this population.

Neurocognitive functioning predicts frailty index in HIV.

PubMed

Oppenheim, Hannah; Paolillo, Emily W; Moore, Raeanne C; Ellis, Ronald J; Letendre, Scott L; Jeste, Dilip V; Grant, Igor; Moore, David J

2018-06-06

To evaluate the association between a frailty index (i.e., scale of accumulated deficits) and neurocognitive functioning among persons living with HIV/AIDS (PLWHA). Observational, cross-sectional data were gathered from the University of California, San Diego, HIV Neurobehavioral Research Program from 2002 to 2016. Eight hundred eleven PLWHA aged 18 to 79 years completed comprehensive physical, neuropsychological, and neuromedical evaluations. The frailty index was composed of 26 general and HIV-specific health maintenance measures, and reflects the proportion of accumulated deficits from 0 (no deficits) to 1 (all 26 deficits). Multiple linear regression was used to examine the association between continuous frailty index scores and neurocognitive functioning. Participants had a mean age of 44.6 years (11.2), and were mostly male (86.9%) and white (60.2%) with a mean frailty index of 0.26 (0.11). Over the study period, prevalence of HIV-related components (e.g., low CD4) decreased, while non-HIV comorbidities (e.g., diabetes) increased. There were no changes in the frailty index by study year. Higher frailty index was associated with worse global neurocognitive functioning, even after adjusting for covariates (age, employment, and premorbid intellectual functioning; b = -0.007; 95% confidence interval [CI] = -0.0112 to -0.003; p < 0.001). The cognitive domains of verbal fluency (b = -0.004; 95% CI = -0.006 to -0.002), executive functioning (b = -0.004; 95% CI = -0.006 to -0.002), processing speed (b = -0.005; 95% CI = -0.007 to -0.003), and motor skills (b = -0.006; 95% CI = -0.007 to -0.005) also significantly predicted worse frailty index score ( p values <0.001). A frailty index can standardize how clinicians identify PLWHA who may be at higher risk of neurocognitive impairment. © 2018 American Academy of Neurology.

Development of an Integrated Moisture Index for predicting species composition

Treesearch

Louis R. Iverson; Charles T. Scott; Martin E. Dale; Anantha Prasad

1996-01-01

A geographic information system (GIS) approach was used to develop an Integrated Moisture Index (IMI), which was used to predict species composition for Ohio forests. Several landscape features (a slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and the water-holding capacity of the soil) were derived from elevation and soils...

Identify the dominant variables to predict stream water temperature

NASA Astrophysics Data System (ADS)

Chien, H.; Flagler, J.

2016-12-01

Stream water temperature is a critical variable controlling water quality and the health of aquatic ecosystems. Accurate prediction of water temperature and the assessment of the impacts of environmental variables on water temperature variation are critical for water resources management, particularly in the context of water quality and aquatic ecosystem sustainability. The objective of this study is to measure stream water temperature and air temperature and to examine the importance of streamflow on stream water temperature prediction. The measured stream water temperature and air temperature will be used to test two hypotheses: 1) streamflow is a relatively more important factor than air temperature in regulating water temperature, and 2) by combining air temperature and streamflow data stream water temperature can be more accurately estimated. Water and air temperature data loggers are placed at two USGS stream gauge stations #01362357and #01362370, located in the upper Esopus Creek watershed in Phonecia, NY. The ARIMA (autoregressive integrated moving average) time series model is used to analyze the measured water temperature data, identify the dominant environmental variables, and predict the water temperature with identified dominant variable. The preliminary results show that streamflow is not a significant variable in predicting stream water temperature at both USGS gauge stations. Daily mean air temperature is sufficient to predict stream water temperature at this site scale.

AST-platelet ratio index, Forns index and FIB-4 in the prediction of significant fibrosis and cirrhosis in patients with chronic hepatitis C.

PubMed

Güzelbulut, Fatih; Çetınkaya, Züleyha Akkan; Sezıklı, Mesut; Yaşar, Bülent; Ozkara, Selvinaz; Övünç, Ayşe Oya Kurdaş

2011-06-01

The aim of this study was to evaluate the diagnostic accuracy of aspartate aminotransferase-platelet ratio index, the Forns index and FIB-4 for the assessment of hepatic fibrosis in chronic hepatitis C patients by comparison with liver biopsy. We retrospectively reviewed our computerized data of chronic hepatitis C patients who admitted to the Gastroenterology Clinic between 2004 and 2008. Treatment-naive chronic hepatitis C patients who had undergone liver biopsy and had laboratory test results allowing the calculation of aspartate aminotransferase-platelet ratio index, the Forns index and FIB-4 were included in this study. The degree of fibrosis was scored according to the METAVIR staging system. Significant fibrosis was defined as F2-4 and cirrhosis as F4. Aspartate aminotransferase-platelet ratio index, the Forns index and FIB-4 were calculated based on the original studies. Tests results were compared between groups F0-1 (no or mild fibrosis) versus F2-4 (significant fibrosis) and F03 (no cirrhosis) versus F4 (cirrhosis). One hundred and fifty patients with chronic hepatitis C were included in this study. The areas under the ROC curves of the Forns index, aspartate aminotransferase-platelet ratio index and FIB-4 to predict significant fibrosis were 0.795, 0.774 and 0.764, respectively. The area under the ROC curves of the Forns index, aspartate aminotransferase-platelet ratio index and FIB-4 to predict cirrhosis were 0.879, 0.839 and 0.874, respectively. The Forns index, aspartate aminotransferase-platelet ratio index and FIB-4 were accurate noninvasive blood tests to predict the presence or absence of significant fibrosis and cirrhosis in half of the chronic hepatitis C patients. The Forns index was slightly better than the aspartate aminotransferase-platelet ratio index and FIB-4 in the prediction of significant fibrosis and cirrhosis.

Site index prediction tables for black, scarlet and white oaks in southeastern Missouri.

Treesearch

Robert A. McQuilkin

1974-01-01

Site index prediction tables for black, scarlet, and white oaks for southeastern Missouri are presented based on site index/height regressions of data from 741 sectioned trees. Formulae for site index conversion between species and confidence intervals for mean stand site index estimates are also presented.

Predicting 1-Year Change in Body Mass Index among College Students

ERIC Educational Resources Information Center

Adams, Troy; Rini, Angela

2007-01-01

Objective: Despite beliefs about weight gain in college, few researchers have evaluated this phenomenon. Participants: Participants were 18- to 31-year-old students at a midwestern university. The dependent variable was body mass index (BMI) change. Methods: The authors extracted predictor variables from a Health Risk Appraisal. These included…

Estimating search engine index size variability: a 9-year longitudinal study.

PubMed

van den Bosch, Antal; Bogers, Toine; de Kunder, Maurice

One of the determining factors of the quality of Web search engines is the size of their index. In addition to its influence on search result quality, the size of the indexed Web can also tell us something about which parts of the WWW are directly accessible to the everyday user. We propose a novel method of estimating the size of a Web search engine's index by extrapolating from document frequencies of words observed in a large static corpus of Web pages. In addition, we provide a unique longitudinal perspective on the size of Google and Bing's indices over a nine-year period, from March 2006 until January 2015. We find that index size estimates of these two search engines tend to vary dramatically over time, with Google generally possessing a larger index than Bing. This result raises doubts about the reliability of previous one-off estimates of the size of the indexed Web. We find that much, if not all of this variability can be explained by changes in the indexing and ranking infrastructure of Google and Bing. This casts further doubt on whether Web search engines can be used reliably for cross-sectional webometric studies.

Predictive Variables of Half-Marathon Performance for Male Runners

PubMed Central

Gómez-Molina, Josué; Ogueta-Alday, Ana; Camara, Jesus; Stickley, Christoper; Rodríguez-Marroyo, José A.; García-López, Juan

2017-01-01

The aims of this study were to establish and validate various predictive equations of half-marathon performance. Seventy-eight half-marathon male runners participated in two different phases. Phase 1 (n = 48) was used to establish the equations for estimating half-marathon performance, and Phase 2 (n = 30) to validate these equations. Apart from half-marathon performance, training-related and anthropometric variables were recorded, and an incremental test on a treadmill was performed, in which physiological (VO2max, speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) were registered. In Phase 1, half-marathon performance could be predicted to 90.3% by variables related to training and anthropometry (Equation 1), 94.9% by physiological variables (Equation 2), 93.7% by biomechanical parameters (Equation 3) and 96.2% by a general equation (Equation 4). Using these equations, in Phase 2 the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively). The proposed equations and their validation showed a high prediction of half-marathon performance in long distance male runners, considered from different approaches. Furthermore, they improved the prediction performance of previous studies, which makes them a highly practical application in the field of training and performance. Key points The present study obtained four equations involving anthropometric, training, physiological and biomechanical variables to estimate half-marathon performance. These equations were validated in a different population, demonstrating narrows ranges of prediction than previous studies and also their consistency. As a novelty, some biomechanical variables (i.e. step length and step rate at RCT, and maximal step length) have been related to half-marathon performance. PMID:28630571

Robust check loss-based variable selection of high-dimensional single-index varying-coefficient model

NASA Astrophysics Data System (ADS)

Song, Yunquan; Lin, Lu; Jian, Ling

2016-07-01

Single-index varying-coefficient model is an important mathematical modeling method to model nonlinear phenomena in science and engineering. In this paper, we develop a variable selection method for high-dimensional single-index varying-coefficient models using a shrinkage idea. The proposed procedure can simultaneously select significant nonparametric components and parametric components. Under defined regularity conditions, with appropriate selection of tuning parameters, the consistency of the variable selection procedure and the oracle property of the estimators are established. Moreover, due to the robustness of the check loss function to outliers in the finite samples, our proposed variable selection method is more robust than the ones based on the least squares criterion. Finally, the method is illustrated with numerical simulations.

Extended resource allocation index for link prediction of complex network

NASA Astrophysics Data System (ADS)

Liu, Shuxin; Ji, Xinsheng; Liu, Caixia; Bai, Yi

2017-08-01

Recently, a number of similarity-based methods have been proposed to predict the missing links in complex network. Among these indices, the resource allocation index performs very well with lower time complexity. However, it ignores potential resources transferred by local paths between two endpoints. Motivated by the resource exchange taking places between endpoints, an extended resource allocation index is proposed. Empirical study on twelve real networks and three synthetic dynamic networks has shown that the index we proposed can achieve a good performance, compared with eight mainstream baselines.

Predictive Variables of Half-Marathon Performance for Male Runners.

PubMed

Gómez-Molina, Josué; Ogueta-Alday, Ana; Camara, Jesus; Stickley, Christoper; Rodríguez-Marroyo, José A; García-López, Juan

2017-06-01

The aims of this study were to establish and validate various predictive equations of half-marathon performance. Seventy-eight half-marathon male runners participated in two different phases. Phase 1 (n = 48) was used to establish the equations for estimating half-marathon performance, and Phase 2 (n = 30) to validate these equations. Apart from half-marathon performance, training-related and anthropometric variables were recorded, and an incremental test on a treadmill was performed, in which physiological (VO 2max , speed at the anaerobic threshold, peak speed) and biomechanical variables (contact and flight times, step length and step rate) were registered. In Phase 1, half-marathon performance could be predicted to 90.3% by variables related to training and anthropometry (Equation 1), 94.9% by physiological variables (Equation 2), 93.7% by biomechanical parameters (Equation 3) and 96.2% by a general equation (Equation 4). Using these equations, in Phase 2 the predicted time was significantly correlated with performance (r = 0.78, 0.92, 0.90 and 0.95, respectively). The proposed equations and their validation showed a high prediction of half-marathon performance in long distance male runners, considered from different approaches. Furthermore, they improved the prediction performance of previous studies, which makes them a highly practical application in the field of training and performance.

How does spatial variability of climate affect catchment streamflow predictions?

EPA Science Inventory

Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

The c-index is not proper for the evaluation of $t$-year predicted risks.

PubMed

Blanche, Paul; Kattan, Michael W; Gerds, Thomas A

2018-02-16

We show that the widely used concordance index for time to event outcome is not proper when interest is in predicting a $t$-year risk of an event, for example 10-year mortality. In the situation with a fixed prediction horizon, the concordance index can be higher for a misspecified model than for a correctly specified model. Impropriety happens because the concordance index assesses the order of the event times and not the order of the event status at the prediction horizon. The time-dependent area under the receiver operating characteristic curve does not have this problem and is proper in this context.

A needs index for mental health care.

PubMed

Glover, G R; Robin, E; Emami, J; Arabscheibani, G R

1998-02-01

The study aimed to develop a mental illness needs index to help local managers, district purchasers and national policy makers in allocating resources. Formulae were developed by regression analysis using 1991 census data to predict the period prevalence of acute psychiatric admission from electoral wards. Census variables used were chosen on the basis of an established association with mental illness rates. Data from one English Health Service region were analysed for patterns common to wards at hospital catchment area level and patterns common to district health authorities at regional level. The North East Thames region was chosen as the setting for the study, with 7096 patients being admitted during 1991. In most, but not all, catchment areas reasonable prediction of the pattern of admission prevalence was possible using the variables chosen. However, different population characteristics predicted admission prevalence in rural and urban areas. Prediction methods based on one or two variables are thus unlikely to work in both settings. A Mental Illness Needs Index (MINI) based on social isolation, poverty, unemployment, permanent sickness and temporary and insecure housing predicted differences in admission prevalence between wards at catchment area level better than Jarman's Underprivileged Area (UPA) score [1] and between districts at regional level better than the UPA score and comparably to the York Psychiatric Index [2] (adjusted r2 at regional level (MINI 0.82, UPA 0.53, York index 0.70). District admission prevalence rates vary by a factor of three between rural and inner city areas; this difference may not fully reflect the variation in the cost of providing care. It did not prove possible to incorporate factors related to bed availability in the models used; reasons for this are discussed. Data covering other aspects of mental health care in addition to hospital admission are needed for more satisfactory modelling.

Environmental stochasticity controls soil erosion variability

PubMed Central

Kim, Jongho; Ivanov, Valeriy Y.; Fatichi, Simone

2016-01-01

Understanding soil erosion by water is essential for a range of research areas but the predictive skill of prognostic models has been repeatedly questioned because of scale limitations of empirical data and the high variability of soil loss across space and time scales. Improved understanding of the underlying processes and their interactions are needed to infer scaling properties of soil loss and better inform predictive methods. This study uses data from multiple environments to highlight temporal-scale dependency of soil loss: erosion variability decreases at larger scales but the reduction rate varies with environment. The reduction of variability of the geomorphic response is attributed to a ‘compensation effect’: temporal alternation of events that exhibit either source-limited or transport-limited regimes. The rate of reduction is related to environment stochasticity and a novel index is derived to reflect the level of variability of intra- and inter-event hydrometeorologic conditions. A higher stochasticity index implies a larger reduction of soil loss variability (enhanced predictability at the aggregated temporal scales) with respect to the mean hydrologic forcing, offering a promising indicator for estimating the degree of uncertainty of erosion assessments. PMID:26925542

Perfusion index and plethysmographic variability index in patients with interscalene nerve catheters.

PubMed

Sebastiani, Anne; Philippi, Larissa; Boehme, Stefan; Closhen, Dorothea; Schmidtmann, Irene; Scherhag, Anton; Markstaller, Klaus; Engelhard, Kristin; Pestel, Gunther

2012-12-01

Interscalene nerve blocks provide adequate analgesia, but there are no objective criteria for early assessment of correct catheter placement. In the present study, pulse oximetry technology was used to evaluate changes in the perfusion index (PI) in both blocked and unblocked arms, and changes in the plethysmographic variability index (PVI) were evaluated once mechanical ventilation was instituted. The PI and PVI values were assessed using a Radical-7™ finger pulse oximetry device (Masimo Corp., Irvine, CA, USA) in both arms of 30 orthopedic patients who received an interscalene catheter at least 25 min before induction of general anesthesia. Data were evaluated at baseline, on application of local anesthetics; five, ten, and 15 min after onset of interscalene nerve blocks; after induction of general anesthesia; before and after a 500 mL colloid fluid challenge; and five minutes thereafter. In the 25 patients with successful blocks, the difference between the PI values in the blocked arm and the PI values in the contralateral arm increased within five minutes of the application of the local anesthetics (P < 0.05) and increased progressively until 15 min. After induction of general anesthesia, the PI increased in the unblocked arm while it remained relatively constant in the blocked arm, thus reducing the difference in the PI. A fluid challenge resulted in a decrease in PVI values in both arms. The perfusion index increases after successful interscalene nerve blockade and may be used as an indicator for successful block placement in awake patients. The PVI values before and after a fluid challenge can be useful to detect changes in preload, and this can be performed in both blocked and unblocked arms.

Utilizing the AAVSO's Variable Star Index (VSX) In Undergraduate Research Projects

NASA Astrophysics Data System (ADS)

Larsen, Kristine

2016-01-01

Among the many important services that the American Association of Variable Star Observers (AAVSO) provides to the astronomical community is the Variable Star Index (VSX - https://www.aavso.org/vsx/). This online catalog of variable stars is the repository of data on over 334,000 variable stars, including information on spectral type, range of magnitude, period, and type of variable, among other properties. A number of these stars were identified as being variable through automated telescope surveys, such as ASAS (All Sky Automated Survey). The computer code of this survey classified newly discovered variables as best it could, but a significant number of false classifications have been noted. The reclassification of ASAS variables in the VSX data, as well as a closer look at variables identified as miscellaneous type in VSX, are two of many projects that can be undertaken by interested undergraduates. In doing so, students learn about the physical properties of various types of variable stars as well as statistical analysis and computer software, especially the VStar variable star data visualization and analysis tool that is available to the astronomical community free of charge on the AAVSO website (https://www.aavso.org/vstar-overview). Two such projects are described in this presentation, the first to identify BY Draconis variables erroneously classified as Cepheids in ASAS data, and the second to identify SRD semiregular variables misidentified as "miscellaneous" in VSX.

[Evaluation of thermal comfort in a student population: predictive value of an integrated index (Fanger's predicted mean value].

PubMed

Catenacci, G; Terzi, R; Marcaletti, G; Tringali, S

1989-01-01

Practical applications and predictive values of a thermal comfort index (Fanger's PRV) were verified on a sample school population (1236 subjects) by studying the relationships between thermal sensations (subjective analysis), determined by means of an individual questionnaire, and the values of thermal comfort index (objective analysis) obtained by calculating the PMV index individually in the subjects under study. In homogeneous conditions of metabolic expenditure rate and thermal impedence from clothing, significant differences were found between the two kinds of analyses. At 22 degrees C mean radiant and operative temperature, the PMV values averaged 0 and the percentage of subjects who experienced thermal comfort did not exceed 60%. The high level of subjects who were dissatisfied with their environmental thermal conditions confirms the doubts regarding the use of the PMV index as a predictive indicator of thermal comfort, especially considering that the negative answers were not homogeneous nor attributable to the small thermal fluctuations (less than 0.5 degree C) measured in the classrooms.

Cortical Response Variability as a Developmental Index of Selective Auditory Attention

ERIC Educational Resources Information Center

Strait, Dana L.; Slater, Jessica; Abecassis, Victor; Kraus, Nina

2014-01-01

Attention induces synchronicity in neuronal firing for the encoding of a given stimulus at the exclusion of others. Recently, we reported decreased variability in scalp-recorded cortical evoked potentials to attended compared with ignored speech in adults. Here we aimed to determine the developmental time course for this neural index of auditory…

Predictive value of European Scleroderma Group Activity Index in an early scleroderma cohort.

PubMed

Nevskaya, Tatiana; Baron, Murray; Pope, Janet E

2017-07-01

To estimate the effect of disease activity, as measured by the European Scleroderma Research Group Activity Index (EScSG-AI), on the risk of subsequent organ damage in a large systemic sclerosis (SSc) cohort. Of 421 SSc patients from the Canadian Scleroderma Research Group database with disease duration of ⩽ 3 years, 197 who had no evidence of end-stage organ damage initially and available 3 year follow-up were included. Disease activity was assessed by the EScSG-AI with two variability measures: the adjusted mean EScSG-AI (the area under the curve of the EScSG-AI over the observation period) and persistently active disease/flare. Outcomes were based on the Medsger severity scale and included accrual of a new severity score (Δ ⩾ 1) overall and within organ systems or reaching a significant level of deterioration in health status. After adjustment for covariates, the adjusted mean EScSG-AI was the most consistent predictor of risk across the study outcomes over 3 years in dcSSc: disease progression defined as Δ ⩾ 1 in any major internal organ, significant decline in forced vital capacity and diffusing capacity of carbon monoxide, severity of visceral disease and HAQ Disability Index worsening. In multivariate analysis, progression of lung disease was predicted solely by adjusted mean EScSG-AI, while the severity of lung disease was predicted the adjusted mean EScSG-AI, older age, modified Rodnan skin score (mRSS) and initial severity. The EScSG-AI was associated with patient- and physician-assessed measures of health status and overpowered the mRSS in predicting disease outcomes. Disease activity burden quantified with the adjusted mean EScSG-AI predicted the risk of deterioration in health status and severe organ involvement in dcSSc. The EScSG-AI is more responsive when done repeatedly and averaged. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email

Explaining the variability of Photochemical Reflectance Index (PRI): deconvolution of variability related to Light Use Efficiency and Canopy attributes.

NASA Astrophysics Data System (ADS)

Merlier, Elodie; Hmimina, Gabriel; Dufrêne, Eric; Soudani, Kamel

2014-05-01

The Photochemical Reflectance Index (PRI) was designed as a proxy of the state of xanthophyll cycle which is used as a response of plants to excess of light (Gamon et al., 1990; 1992). Strong relationships between PRI and LUE were shown at leaf and canopy scales and over a wide range of species (Garbulsky et al., 2011). However, its use at canopy scale was shown to be significantly hampered by effects of confounding factors such as the PRI sensitivity to leaf pigment content (Gamon et al. 2001; Nakaji et al. 2006) and to canopy structure (Hilker et al. 2008). Several approaches aimed at correcting such effects and recent works focused on the deconvolution of LUE related and LUE unrelated PRI variability (Rahimzadeh-Bajgiran et al. 2012).In this study, the PRI variability at canopy scale is investigated over two years on three species (Fagus sylvatica, Quercus robur and Pinus sylvestris) growing under two water regimes. At daily scale, PRI variability is mainly explained by radiation conditions. As already reported at leaf scale in Hmimina et al. (2014), analysis of PRI responses to incoming photosynthetically active radiation over seasonal scale allowed to separate two sources of variability : a constitutive variability mainly related to canopy structure and leaf chlorophyll content and a facultative variability mainly related to LUE and soil moisture content. These results highlight the composite nature of PRI signal measured at canopy scale and the importance of disentangling its sources of variability in order to accurately assess ecosystem light use efficiency. Gamon JA, Field CB, Bilger W, Björkman O, Fredeen AL, Peñuelas J. 1990. Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 85, 1-7. Gamon JA, Field CB, Fredeen A AL, Thayer S. 2001. Assessing photosynthetic downregulation in sunflower stands with an optically-based model. Photosynthesis Research 67, 113-125. Gamon JA, Peñuelas J, Field CB

Fluorescence microscopy point spread function model accounting for aberrations due to refractive index variability within a specimen.

PubMed

Ghosh, Sreya; Preza, Chrysanthe

2015-07-01

A three-dimensional (3-D) point spread function (PSF) model for wide-field fluorescence microscopy, suitable for imaging samples with variable refractive index (RI) in multilayered media, is presented. This PSF model is a key component for accurate 3-D image restoration of thick biological samples, such as lung tissue. Microscope- and specimen-derived parameters are combined with a rigorous vectorial formulation to obtain a new PSF model that accounts for additional aberrations due to specimen RI variability. Experimental evaluation and verification of the PSF model was accomplished using images from 175-nm fluorescent beads in a controlled test sample. Fundamental experimental validation of the advantage of using improved PSFs in depth-variant restoration was accomplished by restoring experimental data from beads (6  μm in diameter) mounted in a sample with RI variation. In the investigated study, improvement in restoration accuracy in the range of 18 to 35% was observed when PSFs from the proposed model were used over restoration using PSFs from an existing model. The new PSF model was further validated by showing that its prediction compares to an experimental PSF (determined from 175-nm beads located below a thick rat lung slice) with a 42% improved accuracy over the current PSF model prediction.

Increased QT interval variability index in acute alcohol withdrawal.

PubMed

Bär, Karl-Jürgen; Boettger, Michael Karl; Koschke, Mandy; Boettger, Silke; Grotelüschen, Marei; Voss, Andreas; Yeragani, Vikram K

2007-07-10

Acute alcohol withdrawal is associated with increased cardiovascular mortality, most likely due to cardiac arrhythmias. As the QT interval reflects the most critical phase for the generation of reentry and thus for arrhythmia, we examined QT variability in patients suffering from acute alcohol withdrawal. High resolution electrocardiographic recordings were performed in 18 male unmedicated patients suffering from acute alcohol withdrawal, 18 matched controls and 15 abstained alcoholics. From these, parameters of beat-to-beat heart rate and QT variability such as approximate entropy and QT variability index (QTvi) were calculated. Measures were correlated with the severity of withdrawal symptoms and with serum electrolyte concentrations. Heart rate and QTvi were significantly increased in acute alcohol withdrawal. Abstained alcoholics did not significantly differ from controls. While QTvi correlated with the severity of alcohol withdrawal symptoms, the mean QT interval duration showed an inverse relationship with serum potassium concentrations. Our data indicate increased QT variability and thus increased repolarization lability in acute alcohol withdrawal. This might add to the elevated risk for serious cardiac arrhythmias. In part, these changes might be related to increased cardiac sympathetic activity or low potassium, thus suggesting the latter as possible targets for adjuvant pharmacological therapy during withdrawal.

Predictive Mortality Index for Community-Dwelling Elderly Koreans

PubMed Central

Kim, Nan H.; Cho, Hyun J.; Kim, Soriul; Seo, Ji H.; Lee, Hyun J.; Yu, Ji H.; Chung, Hye S.; Yoo, Hye J.; Seo, Ji A.; Kim, Sin Gon; Baik, Sei Hyun; Choi, Dong Seop; Shin, Chol; Choi, Kyung Mook

2016-01-01

Abstract There are very few predictive indexes for long-term mortality among community-dwelling elderly Asian individuals, despite its importance, given the rapid and continuous increase in this population. We aimed to develop 10-year predictive mortality indexes for community-dwelling elderly Korean men and women based on routinely collected clinical data. We used data from 2244 elderly individuals (older than 60 years of age) from the southwest Seoul Study, a prospective cohort study, for the development of a prognostic index. An independent longitudinal cohort of 679 elderly participants was selected from the Korean Genome Epidemiology Study in Ansan City for validation. During a 10-year follow-up, 393 participants (17.5%) from the development cohort died. Nine risk factors were identified and weighed in the Cox proportional regression model to create a point scoring system: age, male sex, smoking, diabetes, systolic blood pressure, triglyceride, total cholesterol, white blood cell count, and hemoglobin. In the development cohort, the 10-year mortality risk was 6.6%, 14.8%, 18.2%, and 38.4% among subjects with 1 to 4, 5 to 7, 8 to 9, and ≥10 points, respectively. In the validation cohort, the 10-year mortality risk was 5.2%, 12.0%, 16.0%, and 16.0% according to these categories. The C-statistic for the point system was 0.73 and 0.67 in the development and validation cohorts, respectively. The present study provides valuable information for prognosis among elderly Koreans and may guide individualized approaches for appropriate care in a rapidly aging society. PMID:26844511

Variable Refractive Index Effects on Radiation in Semitransparent Scattering Multilayered Regions

NASA Technical Reports Server (NTRS)

Siegel, R.; Spuckler, C. M.

1993-01-01

A simple set of equations is derived for predicting the temperature distribution and radiative energy flow in a semitransparent layer consisting of an arbitrary number of laminated sublayers that absorb, emit, and scatter radiation. Each sublayer can have a different refractive index and optical thickness. The plane composite region is heated on each exterior side by a different amount of incident radiation. The results are for the limiting case where heat conduction within the layers is very small relative to radiative transfer, and is neglected. The interfaces are assumed diffuse, and all interface reflections are included in the analysis. The thermal behavior is readily calculated from the analytical expressions that are obtained. By using many sublayers, expressions provide the temperature distribution and heat flow for a diffusing medium with a continually varying refractive index, including internal reflection effects caused by refractive index gradients. Temperature and heat flux results are given to show the effect of variations in refractive index and optical thickness through the multilayer laminate.

Predicting sun protection behaviors using protection motivation variables.

PubMed

Ch'ng, Joanne W M; Glendon, A Ian

2014-04-01

Protection motivation theory components were used to predict sun protection behaviors (SPBs) using four outcome measures: typical reported behaviors, previous reported behaviors, current sunscreen use as determined by interview, and current observed behaviors (clothing worn) to control for common method bias. Sampled from two SE Queensland public beaches during summer, 199 participants aged 18-29 years completed a questionnaire measuring perceived severity, perceived vulnerability, response efficacy, response costs, and protection motivation (PM). Personal perceived risk (similar to threat appraisal) and response likelihood (similar to coping appraisal) were derived from their respective PM components. Protection motivation predicted all four SPB criterion variables. Personal perceived risk and response likelihood predicted protection motivation. Protection motivation completely mediated the effect of response likelihood on all four criterion variables. Alternative models are considered. Strengths and limitations of the study are outlined and suggestions made for future research.

Heart rate variability analysis as an index of emotion regulation processes: interest of the Analgesia Nociception Index (ANI).

PubMed

De Jonckheere, J; Rommel, D; Nandrino, J L; Jeanne, M; Logier, R

2012-01-01

Autonomic Nervous System (ANS) variations are strongly influence by emotion regulation processes. Indeed, emotional stimuli are at the origin of an activation of the ANS and the way an individual pass from a state of alert in the case of emotional situation to a state of calm is closely coupled with the ANS flexibility. We have previously described and developed an Analgesia Nociception Index (ANI) for real time pain measurement during surgical procedure under general anesthesia. This index, based on heart rate variability analysis, constitutes a measure of parasympathetic tone and can be used in several other environments. In this paper, we hypothesized that such an index could be used as a tool to investigate the processes of emotional regulation of a human subject. To test this hypothesis, we analyzed ANI's response to a negative emotional stimulus. This analysis showed that the index decreases during the emotion induction phase and returns to its baseline after 2 minutes. This result confirms that ANI could be a good indicator of parasympathetic changes in emotional situation.

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.

Prediction of Indian Summer-Monsoon Onset Variability: A Season in Advance.

PubMed

Pradhan, Maheswar; Rao, A Suryachandra; Srivastava, Ankur; Dakate, Ashish; Salunke, Kiran; Shameera, K S

2017-10-27

Monsoon onset is an inherent transient phenomenon of Indian Summer Monsoon and it was never envisaged that this transience can be predicted at long lead times. Though onset is precipitous, its variability exhibits strong teleconnections with large scale forcing such as ENSO and IOD and hence may be predictable. Despite of the tremendous skill achieved by the state-of-the-art models in predicting such large scale processes, the prediction of monsoon onset variability by the models is still limited to just 2-3 weeks in advance. Using an objective definition of onset in a global coupled ocean-atmosphere model, it is shown that the skillful prediction of onset variability is feasible under seasonal prediction framework. The better representations/simulations of not only the large scale processes but also the synoptic and intraseasonal features during the evolution of monsoon onset are the comprehensions behind skillful simulation of monsoon onset variability. The changes observed in convection, tropospheric circulation and moisture availability prior to and after the onset are evidenced in model simulations, which resulted in high hit rate of early/delay in monsoon onset in the high resolution model.

Crown fuel spatial variability and predictability of fire spread

Treesearch

Russell A. Parsons; Jeremy Sauer; Rodman R. Linn

2010-01-01

Fire behavior predictions, as well as measures of uncertainty in those predictions, are essential in operational and strategic fire management decisions. While it is becoming common practice to assess uncertainty in fire behavior predictions arising from variability in weather inputs, uncertainty arising from the fire models themselves is difficult to assess. This is...

Interpreting weightings of the peer assessment rating index and the discrepancy index across contexts on Chinese patients.

PubMed

Liu, Siqi; Oh, Heesoo; Chambers, David William; Xu, Tianmin; Baumrind, Sheldon

2018-04-06

Determine optimal weightings of Peer Assessment Rating (PAR) index and Discrepancy Index (DI) for malocclusion severity assessment in Chinese orthodontic patients. Sixty-nine Chinese orthodontists assessed a full set of pre-treatment records from a stratified random sample of 120 subjects gathered from six university orthodontic centres. Using professional judgment as the outcome variable, multiple regression analyses were performed to derive customized weighting systems for the PAR index and DI, for all subjects and each Angle classification subgroup. Professional judgment was consistent, with an Intraclass Correlation Coefficient (ICC) of 0.995. The PAR index or DI can be reliably measured, with ICC = 0.959 and 0.990, respectively. The predictive accuracy of PAR index was greatly improved by the Chinese weighting process (from r = 0.431 to r = 0.788) with almost equal distribution in each Angle classification subgroup. The Chinese-weighted DI showed a higher predictive accuracy, at P = 0.01, compared with the PAR index (r = 0.851 versus r = 0.788). A better performance was found in the Class II group (r = 0.890) when compared to Class I (r = 0.736) and III (r = 0.785) groups. The Chinese-weighted PAR index and DI were capable of predicting 62 per cent and 73 per cent of total variance in the professional judgment of malocclusion severity in Chinese patients. Differential prediction across Angle classifications merits attention since different weighting formulas were found.

Body mass index predicts risk for complications from transtemporal cerebellopontine angle surgery.

PubMed

Mantravadi, Avinash V; Leonetti, John P; Burgette, Ryan; Pontikis, George; Marzo, Sam J; Anderson, Douglas

2013-03-01

To determine the relationship between body mass index (BMI) and risk for specific complications from transtemporal cerebellopontine angle (CPA) surgery for nonmalignant disease. Case series with chart review. Tertiary-care academic hospital. Retrospective review of 134 consecutive patients undergoing transtemporal cerebellopontine angle surgery for nonmalignant disease from 2009 to 2011. Data were collected regarding demographics, body mass index, intraoperative details, hospital stay, and complications including cerebrospinal fluid leak, wound complications, and brachial plexopathy. One hundred thirty-four patients were analyzed with a mean preoperative body mass index of 28.58. Statistical analysis demonstrated a significant difference in body mass index between patients with a postoperative cerebrospinal fluid leak and those without (P = .04), as well as a similar significant difference between those experiencing postoperative brachial plexopathy and those with no such complication (P = .03). Logistical regression analysis confirmed that body mass index is significant in predicting both postoperative cerebrospinal fluid leak (P = .004; odds ratio, 1.10) and brachial plexopathy (P = .04; odds ratio, 1.07). Elevated body mass index was not significant in predicting wound complications or increased hospital stay beyond postoperative day 3. Risk of cerebrospinal fluid leak and brachial plexopathy is increased in patients with elevated body mass index undergoing surgery of the cerebellopontine angle. Consideration should be given to preoperative optimization via dietary and lifestyle modifications as well as intraoperative somatosensory evoked potential monitoring of the brachial plexus to decrease these risks.

[Developing a predictive model for the caregiver strain index].

PubMed

Álvarez-Tello, Margarita; Casado-Mejía, Rosa; Praena-Fernández, Juan Manuel; Ortega-Calvo, Manuel

Patient homecare with multiple morbidities is an increasingly common occurrence. The caregiver strain index is tool in the form of questionnaire that is designed to measure the perceived burden of those who care for their families. The aim of this study is to construct a diagnostic nomogram of informal caregiver burden using data from a predictive model. The model was drawn up using binary logistic regression and the questionnaire items as dichotomous factors. The dependent variable was the final score obtained with the questionnaire but categorised in accordance with that in the literature. Scores between 0 and 6 were labelled as "no" (no caregiver stress) and at or greater than 7 as "yes". The version 3.1.1R statistical software was used. To construct confidence intervals for the ROC curve 2000 boot strap replicates were used. A sample of 67 caregivers was obtained. A diagnosing nomogram was made up with its calibration graph (Brier scaled = 0.686, Nagelkerke R 2 =0.791), and the corresponding ROC curve (area under the curve=0.962). The predictive model generated using binary logistic regression and the nomogram contain four items (1, 4, 5 and 9) of the questionnaire. R plotting functions allow a very good solution for validating a model like this. The area under the ROC curve (0.96; 95% CI: 0.994-0.941) achieves a high discriminative value. Calibration also shows high goodness of fit values, suggesting that it may be clinically useful in community nursing and geriatric establishments. Copyright © 2015 SEGG. Publicado por Elsevier España, S.L.U. All rights reserved.

Skilful multi-year predictions of tropical trans-basin climate variability

PubMed Central

Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei

2015-01-01

Tropical Pacific sea surface temperature anomalies influence the atmospheric circulation, impacting climate far beyond the tropics. The predictability of the corresponding atmospheric signals is typically limited to less than 1 year lead time. Here we present observational and modelling evidence for multi-year predictability of coherent trans-basin climate variations that are characterized by a zonal seesaw in tropical sea surface temperature and sea-level pressure between the Pacific and the other two ocean basins. State-of-the-art climate model forecasts initialized from a realistic ocean state show that the low-frequency trans-basin climate variability, which explains part of the El Niño Southern Oscillation flavours, can be predicted up to 3 years ahead, thus exceeding the predictive skill of current tropical climate forecasts for natural variability. This low-frequency variability emerges from the synchronization of ocean anomalies in all basins via global reorganizations of the atmospheric Walker Circulation. PMID:25897996

Skilful multi-year predictions of tropical trans-basin climate variability.

PubMed

Chikamoto, Yoshimitsu; Timmermann, Axel; Luo, Jing-Jia; Mochizuki, Takashi; Kimoto, Masahide; Watanabe, Masahiro; Ishii, Masayoshi; Xie, Shang-Ping; Jin, Fei-Fei

2015-04-21

Tropical Pacific sea surface temperature anomalies influence the atmospheric circulation, impacting climate far beyond the tropics. The predictability of the corresponding atmospheric signals is typically limited to less than 1 year lead time. Here we present observational and modelling evidence for multi-year predictability of coherent trans-basin climate variations that are characterized by a zonal seesaw in tropical sea surface temperature and sea-level pressure between the Pacific and the other two ocean basins. State-of-the-art climate model forecasts initialized from a realistic ocean state show that the low-frequency trans-basin climate variability, which explains part of the El Niño Southern Oscillation flavours, can be predicted up to 3 years ahead, thus exceeding the predictive skill of current tropical climate forecasts for natural variability. This low-frequency variability emerges from the synchronization of ocean anomalies in all basins via global reorganizations of the atmospheric Walker Circulation.

Multifactorial risk index for prediction of intraoperative blood transfusion in endovascular aneurysm repair.

PubMed

Mahmood, Eitezaz; Matyal, Robina; Mueller, Ariel; Mahmood, Feroze; Tung, Avery; Montealegre-Gallegos, Mario; Schermerhorn, Marc; Shahul, Sajid

2018-03-01

In some institutions, the current blood ordering practice does not discriminate minimally invasive endovascular aneurysm repair (EVAR) from open procedures, with consequent increasing costs and likelihood of blood product wastage for EVARs. This limitation in practice can possibly be addressed with the development of a reliable prediction model for transfusion risk in EVAR patients. We used the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database to create a model for prediction of intraoperative blood transfusion occurrence in patients undergoing EVAR. Afterward, we tested our predictive model on the Vascular Study Group of New England (VSGNE) database. We used the ACS NSQIP database for patients who underwent EVAR from 2011 to 2013 (N = 4709) as our derivation set for identifying a risk index for predicting intraoperative blood transfusion. We then developed a clinical risk score and validated this model using patients who underwent EVAR from 2003 to 2014 in the VSGNE database (N = 4478). The transfusion rates were 8.4% and 6.1% for the ACS NSQIP (derivation set) and VSGNE (validation) databases, respectively. Hemoglobin concentration, American Society of Anesthesiologists class, age, and aneurysm diameter predicted blood transfusion in the derivation set. When it was applied on the validation set, our risk index demonstrated good discrimination in both the derivation and validation set (C statistic = 0.73 and 0.70, respectively) and calibration using the Hosmer-Lemeshow test (P = .27 and 0.31) for both data sets. We developed and validated a risk index for predicting the likelihood of intraoperative blood transfusion in EVAR patients. Implementation of this index may facilitate the blood management strategies specific for EVAR. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Variable Refractive Index Effects on Radiation in Semitransparent Scattering Multilayered Regions

NASA Technical Reports Server (NTRS)

Siegel, R.; Spuckler, C. M.

1993-01-01

A simple set of equations is derived for predicting the temperature distribution and radiative energy flow in a semitransparent layer consisting of an arbitrary number of laminated sublayers that absorb, emit, and scatter radiation. Each sublayer can have a different refractive index and optical thickness. The plane composite region is heated on each exterior side by a different amount of incident radiation. The results are for the limiting case where heat conduction within the layers is very small relative to radiative transfer, and is neglected. The interfaces are assumed diffuse, and all interface reflections are included in the analysis. The thermal behavior is readily calculated from the analytical expressions that are obtained. By using many sublayers, the analytical expressions provide the temperature distribution and heat flow for a diffusing medium with a continuously varying refractive index, including internal reflection effects caused by refractive index gradients. Temperature and heat flux results are given to show the effect of variations in refractive index and optical thickness through the multilayer laminate.

Logistic model analysis of neurological findings in Minamata disease and the predicting index.

PubMed

Nakagawa, Masanori; Kodama, Tomoko; Akiba, Suminori; Arimura, Kimiyoshi; Wakamiya, Junji; Futatsuka, Makoto; Kitano, Takao; Osame, Mitsuhiro

2002-01-01

To establish a statistical diagnostic method to identify patients with Minamata disease (MD) considering factors of aging and sex, we analyzed the neurological findings in MD patients, inhabitants in a methylmercury polluted (MP) area, and inhabitants in a non-MP area. We compared the neurological findings in MD patients and inhabitants aged more than 40 years in the non-MP area. Based on the different frequencies of the neurological signs in the two groups, we devised the following formula to calculate the predicting index for MD: predicting index = 1/(1+e(-x)) x 100 (The value of x was calculated using the regression coefficients of each neurological finding obtained from logistic analysis. The index 100 indicated MD, and 0, non-MD). Using this method, we found that 100% of male and 98% of female patients with MD (95 cases) gave predicting indices higher than 95. Five percent of the aged inhabitants in the MP area (598 inhabitants) and 0.2% of those in the non-MP area (558 inhabitants) gave predicting indices of 50 or higher. Our statistical diagnostic method for MD was useful in distinguishing MD patients from healthy elders based on their neurological findings.

The importance of histopathological and clinical variables in predicting the evolution of colon cancer.

PubMed

Diculescu, Mircea; Iacob, Răzvan; Iacob, Speranţa; Croitoru, Adina; Becheanu, Gabriel; Popeneciu, Valentin

2002-09-01

It has been a consensus that prognostic factors should always be taken into account before planning treatment in colorectal cancer. A 5 year prospective study was conducted, in order to assess the importance of several histopathological and clinical prognostic variables in the prediction of evolution in colon cancer. Some of the factors included in the analysis are still subject to dispute by different authors. 46 of 53 screened patients qualified to enter the study and underwent a potentially curative resection of the tumor, followed, when necessary, by adjuvant chemotherapy. Univariate and multivariate analyses were carried out in order to identify independent prognostic indicators. The endpoint of the study was considered the recurrence of the tumor or the detection of metastases. 65.2% of the patients had a good evolution during the follow up period. Multivariate survival analysis performed by Cox proportional hazard model identified 3 independent prognostic factors: Dukes stage (p = 0.00002), the grade of differentiation (p = 0.0009) and the weight loss index, representing the weight loss of the patient divided by the number of months when it was actually lost (p = 0.02). Age under 40 years, sex, microscopic aspect of the tumor, tumor location, anemia degree were not identified by our analysis as having prognostic importance. Histopathological factors continue to be the most valuable source of information regarding the possible evolution of patients with colorectal cancer. Individual clinical symptoms or biological parameters such as erytrocyte sedimentation rate or hemoglobin level are of little or no prognostic value. More research is required relating to the impact of a performance status index (which could include also weight loss index) as another reliable prognostic variable.

Predicting long-term streamflow variability in moist eucalypt forests using forest growth models and a sapwood area index

NASA Astrophysics Data System (ADS)

Jaskierniak, D.; Kuczera, G.; Benyon, R.

2016-04-01

A major challenge in surface hydrology involves predicting streamflow in ungauged catchments with heterogeneous vegetation and spatiotemporally varying evapotranspiration (ET) rates. We present a top-down approach for quantifying the influence of broad-scale changes in forest structure on ET and hence streamflow. Across three catchments between 18 and 100 km2 in size and with regenerating Eucalyptus regnans and E. delegatensis forest, we demonstrate how variation in ET can be mapped in space and over time using LiDAR data and commonly available forest inventory data. The model scales plot-level sapwood area (SA) to the catchment-level using basal area (BA) and tree stocking density (N) estimates in forest growth models. The SA estimates over a 69 year regeneration period are used in a relationship between SA and vegetation induced streamflow loss (L) to predict annual streamflow (Q) with annual rainfall (P) estimates. Without calibrating P, BA, N, SA, and L to Q data, we predict annual Q with R2 between 0.68 and 0.75 and Nash Sutcliffe efficiency (NSE) between 0.44 and 0.48. To remove bias, the model was extended to allow for runoff carry-over into the following year as well as minor correction to rainfall bias, which produced R2 values between 0.72 and 0.79, and NSE between 0.70 and 0.79. The model under-predicts streamflow during drought periods as it lacks representation of ecohydrological processes that reduce L with either reduced growth rates or rainfall interception during drought. Refining the relationship between sapwood thickness and forest inventory variables is likely to further improve results.

VizieR Online Data Catalog: AAVSO International Variable Star Index VSX (Watson+, 2006-2014)

NASA Astrophysics Data System (ADS)

Watson, C.; Henden, A. A.; Price, A.

2017-05-01

This file contains Galactic stars known or suspected to be variable. It lists all stars that have an entry in the AAVSO International Variable Star Index (VSX; http://www.aavso.org/vsx). The database consisted initially of the General Catalogue of Variable Stars (GCVS) and the New Catalogue of Suspected Variables (NSV) and was then supplemented with a large number of variable star catalogues, as well as individual variable star discoveries or variables found in the literature. Effort has also been invested to update the entries with the latest information regarding position, type and period and to remove duplicates. The VSX database is being continually updated and maintained. For historical reasons some objects outside of the Galaxy have been included. (3 data files).

VizieR Online Data Catalog: AAVSO International Variable Star Index VSX (Watson+, 2006-2014)

NASA Astrophysics Data System (ADS)

Watson, C.; Henden, A. A.; Price, A.

2018-05-01

This file contains Galactic stars known or suspected to be variable. It lists all stars that have an entry in the AAVSO International Variable Star Index (VSX; http://www.aavso.org/vsx). The database consisted initially of the General Catalogue of Variable Stars (GCVS) and the New Catalogue of Suspected Variables (NSV) and was then supplemented with a large number of variable star catalogues, as well as individual variable star discoveries or variables found in the literature. Effort has also been invested to update the entries with the latest information regarding position, type and period and to remove duplicates. The VSX database is being continually updated and maintained. For historical reasons some objects outside of the Galaxy have been included. (3 data files).

Predictive modeling and reducing cyclic variability in autoignition engines

DOEpatents

Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob

2016-08-30

Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.

Yield variability prediction by remote sensing sensors with different spatial resolution

NASA Astrophysics Data System (ADS)

Kumhálová, Jitka; Matějková, Štěpánka

2017-04-01

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

Longitudinal change in the BODE index predicts mortality in severe emphysema.

PubMed

Martinez, Fernando J; Han, Meilan K; Andrei, Adin-Cristian; Wise, Robert; Murray, Susan; Curtis, Jeffrey L; Sternberg, Alice; Criner, Gerard; Gay, Steven E; Reilly, John; Make, Barry; Ries, Andrew L; Sciurba, Frank; Weinmann, Gail; Mosenifar, Zab; DeCamp, Malcolm; Fishman, Alfred P; Celli, Bartolome R

2008-09-01

The predictive value of longitudinal change in BODE (Body mass index, airflow Obstruction, Dyspnea, and Exercise capacity) index has received limited attention. We hypothesized that decrease in a modified BODE (mBODE) would predict survival in National Emphysema Treatment Trial (NETT) patients. To determine how the mBODE score changes in patients with lung volume reduction surgery versus medical therapy and correlations with survival. Clinical data were recorded using standardized instruments. The mBODE was calculated and patient-specific mBODE trajectories during 6, 12, and 24 months of follow-up were estimated using separate regressions for each patient. Patients were classified as having decreasing, stable, increasing, or missing mBODE based on their absolute change from baseline. The predictive ability of mBODE change on survival was assessed using multivariate Cox regression models. The index of concordance was used to directly compare the predictive ability of mBODE and its separate components. The entire cohort (610 treated medically and 608 treated surgically) was characterized by severe airflow obstruction, moderate breathlessness, and increased mBODE at baseline. A wide distribution of change in mBODE was seen at follow-up. An increase in mBODE of more than 1 point was associated with increased mortality in surgically and medically treated patients. Surgically treated patients were less likely to experience death or an increase greater than 1 in mBODE. Indices of concordance showed that mBODE change predicted survival better than its separate components. The mBODE demonstrates short- and intermediate-term responsiveness to intervention in severe chronic obstructive pulmonary disease. Increase in mBODE of more than 1 point from baseline to 6, 12, and 24 months of follow-up was predictive of subsequent mortality. Change in mBODE may prove a good surrogate measure of survival in therapeutic trials in severe chronic obstructive pulmonary disease. Clinical

Do heart and respiratory rate variability improve prediction of extubation outcomes in critically ill patients?

PubMed Central

2014-01-01

Introduction Prolonged ventilation and failed extubation are associated with increased harm and cost. The added value of heart and respiratory rate variability (HRV and RRV) during spontaneous breathing trials (SBTs) to predict extubation failure remains unknown. Methods We enrolled 721 patients in a multicenter (12 sites), prospective, observational study, evaluating clinical estimates of risk of extubation failure, physiologic measures recorded during SBTs, HRV and RRV recorded before and during the last SBT prior to extubation, and extubation outcomes. We excluded 287 patients because of protocol or technical violations, or poor data quality. Measures of variability (97 HRV, 82 RRV) were calculated from electrocardiogram and capnography waveforms followed by automated cleaning and variability analysis using Continuous Individualized Multiorgan Variability Analysis (CIMVA™) software. Repeated randomized subsampling with training, validation, and testing were used to derive and compare predictive models. Results Of 434 patients with high-quality data, 51 (12%) failed extubation. Two HRV and eight RRV measures showed statistically significant association with extubation failure (P <0.0041, 5% false discovery rate). An ensemble average of five univariate logistic regression models using RRV during SBT, yielding a probability of extubation failure (called WAVE score), demonstrated optimal predictive capacity. With repeated random subsampling and testing, the model showed mean receiver operating characteristic area under the curve (ROC AUC) of 0.69, higher than heart rate (0.51), rapid shallow breathing index (RBSI; 0.61) and respiratory rate (0.63). After deriving a WAVE model based on all data, training-set performance demonstrated that the model increased its predictive power when applied to patients conventionally considered high risk: a WAVE score >0.5 in patients with RSBI >105 and perceived high risk of failure yielded a fold increase in risk of extubation

Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

NASA Astrophysics Data System (ADS)

Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

2017-12-01

Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

Including the dynamic relationship between climatic variables and leaf area index in a hydrological model to improve streamflow prediction under a changing climate

NASA Astrophysics Data System (ADS)

Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

2015-06-01

Anthropogenic climate change is projected to enrich the atmosphere with carbon dioxide, change vegetation dynamics and influence the availability of water at the catchment scale. This study combines a nonlinear model for estimating changes in leaf area index (LAI) due to climatic fluctuations with the variable infiltration capacity (VIC) hydrological model to improve catchment streamflow prediction under a changing climate. The combined model was applied to 13 gauged sub-catchments with different land cover types (crop, pasture and tree) in the Goulburn-Broken catchment, Australia, for the "Millennium Drought" (1997-2009) relative to the period 1983-1995, and for two future periods (2021-2050 and 2071-2100) and two emission scenarios (Representative Concentration Pathway (RCP) 4.5 and RCP8.5) which were compared with the baseline historical period of 1981-2010. This region was projected to be warmer and mostly drier in the future as predicted by 38 Coupled Model Intercomparison Project Phase 5 (CMIP5) runs from 15 global climate models (GCMs) and for two emission scenarios. The results showed that during the Millennium Drought there was about a 29.7-66.3 % reduction in mean annual runoff due to reduced precipitation and increased temperature. When drought-induced changes in LAI were included, smaller reductions in mean annual runoff of between 29.3 and 61.4 % were predicted. The proportional increase in runoff due to modeling LAI was 1.3-10.2 % relative to not including LAI. For projected climate change under the RCP4.5 emission scenario, ignoring the LAI response to changing climate could lead to a further reduction in mean annual runoff of between 2.3 and 27.7 % in the near-term (2021-2050) and 2.3 to 23.1 % later in the century (2071-2100) relative to modeling the dynamic response of LAI to precipitation and temperature changes. Similar results (near-term 2.5-25.9 % and end of century 2.6-24.2 %) were found for climate change under the RCP8.5 emission scenario

Optimal no-go theorem on hidden-variable predictions of effect expectations

NASA Astrophysics Data System (ADS)

Blass, Andreas; Gurevich, Yuri

2018-03-01

No-go theorems prove that, under reasonable assumptions, classical hidden-variable theories cannot reproduce the predictions of quantum mechanics. Traditional no-go theorems proved that hidden-variable theories cannot predict correctly the values of observables. Recent expectation no-go theorems prove that hidden-variable theories cannot predict the expectations of observables. We prove the strongest expectation-focused no-go theorem to date. It is optimal in the sense that the natural weakenings of the assumptions and the natural strengthenings of the conclusion make the theorem fail. The literature on expectation no-go theorems strongly suggests that the expectation-focused approach is more general than the value-focused one. We establish that the expectation approach is not more general.

A predictive model for spotted wilt epidemics in peanut based on local weather conditions and the tomato spotted wilt virus risk index.

PubMed

Olatinwo, R O; Paz, J O; Brown, S L; Kemerait, R C; Culbreath, A K; Beasley, J P; Hoogenboom, G

2008-10-01

Tomato spotted wilt virus (TSWV), a member of the genus Tospovirus (family Bunyaviridae), is an important plant virus that causes severe damage to peanut (Arachis hypogaea) in the southeastern United States. Disease severity has been extremely variable in individual fields in Georgia, due to several factors including variability in weather patterns. A TSWV risk index has been developed by the University of Georgia to aid peanut growers with the assessment and avoidance of high risk situations. This study was conducted to examine the relationship between weather parameters and spotted wilt severity in peanut, and to develop a predictive model that integrates localized weather information into the risk index. On-farm survey data collected during 1999, 2002, 2004, and 2005 growing seasons, and derived weather variables during the same years were analyzed using nonlinear and multiple regression analyses. Meteorological data were obtained from the Georgia Automated Environmental Monitoring Network. The best model explained 61% of the variation in spotted wilt severity (square root transformed) as a function of the interactions between the TSWV risk index, the average daily temperature in April (TavA), the average daily minimum temperature between March and April (TminMA), the accumulated rainfall in March (RainfallM), the accumulated rainfall in April (RainfallA), the number of rain days in April (RainDayA), evapotranspiration in April (EVTA), and the number of days from 1 January to the planting date (JulianDay). Integrating this weather-based model with the TSWV risk index may help peanut growers more effectively manage tomato spotted wilt disease.

Novel Radiobiological Gamma Index for Evaluation of 3-Dimensional Predicted Dose Distribution

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

Sumida, Iori, E-mail: sumida@radonc.med.osaka-u.ac.jp; Yamaguchi, Hajime; Kizaki, Hisao

2015-07-15

Purpose: To propose a gamma index-based dose evaluation index that integrates the radiobiological parameters of tumor control (TCP) and normal tissue complication probabilities (NTCP). Methods and Materials: Fifteen prostate and head and neck (H&N) cancer patients received intensity modulated radiation therapy. Before treatment, patient-specific quality assurance was conducted via beam-by-beam analysis, and beam-specific dose error distributions were generated. The predicted 3-dimensional (3D) dose distribution was calculated by back-projection of relative dose error distribution per beam. A 3D gamma analysis of different organs (prostate: clinical [CTV] and planned target volumes [PTV], rectum, bladder, femoral heads; H&N: gross tumor volume [GTV], CTV,more » spinal cord, brain stem, both parotids) was performed using predicted and planned dose distributions under 2%/2 mm tolerance and physical gamma passing rate was calculated. TCP and NTCP values were calculated for voxels with physical gamma indices (PGI) >1. We propose a new radiobiological gamma index (RGI) to quantify the radiobiological effects of TCP and NTCP and calculate radiobiological gamma passing rates. Results: The mean RGI gamma passing rates for prostate cases were significantly different compared with those of PGI (P<.03–.001). The mean RGI gamma passing rates for H&N cases (except for GTV) were significantly different compared with those of PGI (P<.001). Differences in gamma passing rates between PGI and RGI were due to dose differences between the planned and predicted dose distributions. Radiobiological gamma distribution was visualized to identify areas where the dose was radiobiologically important. Conclusions: RGI was proposed to integrate radiobiological effects into PGI. This index would assist physicians and medical physicists not only in physical evaluations of treatment delivery accuracy, but also in clinical evaluations of predicted dose distribution.« less

The prostate health index PHI predicts oncological outcome and biochemical recurrence after radical prostatectomy - analysis in 437 patients

PubMed Central

Maxeiner, Andreas; Kilic, Ergin; Matalon, Julia; Friedersdorff, Frank; Miller, Kurt; Jung, Klaus; Stephan, Carsten; Busch, Jonas

2017-01-01

The purpose of this study was to investigate the Prostate-Health-Index (PHI) for pathological outcome prediction following radical prostatectomy and also for biochemical recurrence prediction in comparison to established parameters such as Gleason-score, pathological tumor stage, resection status (R0/1) and prostate-specific antigen (PSA). Out of a cohort of 460 cases with preoperative PHI-measurements (World Health Organization calibration: Beckman Coulter Access-2-Immunoassay) between 2001 and 2014, 437 patients with complete follow up data were included. From these 437 patients, 87 (19.9%) developed a biochemical recurrence. Patient characteristics were compared by using chi-square test. Predictors were analyzed by multivariate adjusted logistic and Cox regression. The median follow up for a biochemical recurrence was 65 (range 3-161) months. PHI, PSA, [-2]proPSA, PHI- and PSA-density performed as significant variables (p < 0.05) for cancer aggressiveness: Gleason-score <7 or ≥7 (ISUP grade 1 or ≥2) . Concerning pathological tumor stage discrimination and prediction, variables as PHI, PSA, %fPSA, [-2]proPSA, PHI- and PSA-density significantly discriminated between stages prediction PHI, PSA, [-2]proPSA, PHI- and PSA-density were the strongest predictors. In conclusion, due to heterogeneity of time spans to biochemical recurrence, longer follow up periods are crucial. This study with a median follow up of more than 5 years, confirmed a clinical value for PHI as an independent biomarker essential for biochemical recurrence prediction. PMID:29108306

The prostate health index PHI predicts oncological outcome and biochemical recurrence after radical prostatectomy - analysis in 437 patients.

PubMed

Maxeiner, Andreas; Kilic, Ergin; Matalon, Julia; Friedersdorff, Frank; Miller, Kurt; Jung, Klaus; Stephan, Carsten; Busch, Jonas

2017-10-03

The purpose of this study was to investigate the Prostate-Health-Index (PHI) for pathological outcome prediction following radical prostatectomy and also for biochemical recurrence prediction in comparison to established parameters such as Gleason-score, pathological tumor stage, resection status (R0/1) and prostate-specific antigen (PSA). Out of a cohort of 460 cases with preoperative PHI-measurements (World Health Organization calibration: Beckman Coulter Access-2-Immunoassay) between 2001 and 2014, 437 patients with complete follow up data were included. From these 437 patients, 87 (19.9%) developed a biochemical recurrence. Patient characteristics were compared by using chi-square test. Predictors were analyzed by multivariate adjusted logistic and Cox regression. The median follow up for a biochemical recurrence was 65 (range 3-161) months. PHI, PSA, [-2]proPSA, PHI- and PSA-density performed as significant variables (p < 0.05) for cancer aggressiveness: Gleason-score <7 or ≥7 (ISUP grade 1 or ≥2) . Concerning pathological tumor stage discrimination and prediction, variables as PHI, PSA, %fPSA, [-2]proPSA, PHI- and PSA-density significantly discriminated between stages prediction PHI, PSA, [-2]proPSA, PHI- and PSA-density were the strongest predictors. In conclusion, due to heterogeneity of time spans to biochemical recurrence, longer follow up periods are crucial. This study with a median follow up of more than 5 years, confirmed a clinical value for PHI as an independent biomarker essential for biochemical recurrence prediction.

Spatial Models for Prediction and Early Warning of Aedes aegypti Proliferation from Data on Climate Change and Variability in Cuba.

PubMed

Ortiz, Paulo L; Rivero, Alina; Linares, Yzenia; Pérez, Alina; Vázquez, Juan R

2015-04-01

Climate variability, the primary expression of climate change, is one of the most important environmental problems affecting human health, particularly vector-borne diseases. Despite research efforts worldwide, there are few studies addressing the use of information on climate variability for prevention and early warning of vector-borne infectious diseases. Show the utility of climate information for vector surveillance by developing spatial models using an entomological indicator and information on predicted climate variability in Cuba to provide early warning of danger of increased risk of dengue transmission. An ecological study was carried out using retrospective and prospective analyses of time series combined with spatial statistics. Several entomological and climatic indicators were considered using complex Bultó indices -1 and -2. Moran's I spatial autocorrelation coefficient specified for a matrix of neighbors with a radius of 20 km, was used to identify the spatial structure. Spatial structure simulation was based on simultaneous autoregressive and conditional autoregressive models; agreement between predicted and observed values for number of Aedes aegypti foci was determined by the concordance index Di and skill factor Bi. Spatial and temporal distributions of populations of Aedes aegypti were obtained. Models for describing, simulating and predicting spatial patterns of Aedes aegypti populations associated with climate variability patterns were put forward. The ranges of climate variability affecting Aedes aegypti populations were identified. Forecast maps were generated for the municipal level. Using the Bultó indices of climate variability, it is possible to construct spatial models for predicting increased Aedes aegypti populations in Cuba. At 20 x 20 km resolution, the models are able to provide warning of potential changes in vector populations in rainy and dry seasons and by month, thus demonstrating the usefulness of climate information for

Development and validation of a prognostic index for 4-year mortality in older adults.

PubMed

Lee, Sei J; Lindquist, Karla; Segal, Mark R; Covinsky, Kenneth E

2006-02-15

Both comorbid conditions and functional measures predict mortality in older adults, but few prognostic indexes combine both classes of predictors. Combining easily obtained measures into an accurate predictive model could be useful to clinicians advising patients, as well as policy makers and epidemiologists interested in risk adjustment. To develop and validate a prognostic index for 4-year mortality using information that can be obtained from patient report. Using the 1998 wave of the Health and Retirement Study (HRS), a population-based study of community-dwelling US adults older than 50 years, we developed the prognostic index from 11,701 individuals and validated the index with 8009. Individuals were asked about their demographic characteristics, whether they had specific diseases, and whether they had difficulty with a series of functional measures. We identified variables independently associated with mortality and weighted the variables to create a risk index. Death by December 31, 2002. The overall response rate was 81%. During the 4-year follow-up, there were 1361 deaths (12%) in the development cohort and 1072 deaths (13%) in the validation cohort. Twelve independent predictors of mortality were identified: 2 demographic variables (age: 60-64 years, 1 point; 65-69 years, 2 points; 70-74 years, 3 points; 75-79 years, 4 points; 80-84 years, 5 points, >85 years, 7 points and male sex, 2 points), 6 comorbid conditions (diabetes, 1 point; cancer, 2 points; lung disease, 2 points; heart failure, 2 points; current tobacco use, 2 points; and body mass index <25, 1 point), and difficulty with 4 functional variables (bathing, 2 points; walking several blocks, 2 points; managing money, 2 points, and pushing large objects, 1 point. Scores on the risk index were strongly associated with 4-year mortality in the validation cohort, with 0 to 5 points predicting a less than 4% risk, 6 to 9 points predicting a 15% risk, 10 to 13 points predicting a 42% risk, and 14 or

Using neural networks for prediction of air pollution index in industrial city

NASA Astrophysics Data System (ADS)

Rahman, P. A.; Panchenko, A. A.; Safarov, A. M.

2017-10-01

This scientific paper is dedicated to the use of artificial neural networks for the ecological prediction of state of the atmospheric air of an industrial city for capability of the operative environmental decisions. In the paper, there is also the described development of two types of prediction models for determining of the air pollution index on the basis of neural networks: a temporal (short-term forecast of the pollutants content in the air for the nearest days) and a spatial (forecast of atmospheric pollution index in any point of city). The stages of development of the neural network models are briefly overviewed and description of their parameters is also given. The assessment of the adequacy of the prediction models, based on the calculation of the correlation coefficient between the output and reference data, is also provided. Moreover, due to the complexity of perception of the «neural network code» of the offered models by the ordinary users, the software implementations allowing practical usage of neural network models are also offered. It is established that the obtained neural network models provide sufficient reliable forecast, which means that they are an effective tool for analyzing and predicting the behavior of dynamics of the air pollution in an industrial city. Thus, this scientific work successfully develops the urgent matter of forecasting of the atmospheric air pollution index in industrial cities based on the use of neural network models.

Formulas of Site Index Prediction Tables for Oak in Missouri

Treesearch

Harry V. Jr. Wiant; Robert A. McQuilkin

1976-01-01

Recently published site index prediction tables for oak in Missouri were fomulized using the "matchacurve" system. The average absolute differences between formula and table values were .8 feet for white oak and 1.4 feet for black and scarlet oaks; maximum differences were 3.0 and 4.2 feet, respectively.

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

NASA Astrophysics Data System (ADS)

Deo, Ravinesh C.; Şahin, Mehmet

2015-02-01

The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed data-driven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning

Droughts in Amazonia: Spatiotemporal Variability, Teleconnections, and Seasonal Predictions

NASA Astrophysics Data System (ADS)

Lima, Carlos H. R.; AghaKouchak, Amir

2017-12-01

Most Amazonia drought studies have focused on rainfall deficits and their impact on river discharges, while the analysis of other important driver variables, such as temperature and soil moisture, has attracted less attention. Here we try to better understand the spatiotemporal dynamics of Amazonia droughts and associated climate teleconnections as characterized by the Palmer Drought Severity Index (PDSI), which integrates information from rainfall deficit, temperature anomalies, and soil moisture capacity. The results reveal that Amazonia droughts are most related to one dominant pattern across the entire region, followed by two seesaw kind of patterns: north-south and east-west. The main two modes are correlated with sea surface temperature (SST) anomalies in the tropical Pacific and Atlantic oceans. The teleconnections associated with global SST are then used to build a seasonal forecast model for PDSI over Amazonia based on predictors obtained from a sparse canonical correlation analysis approach. A unique feature of the presented drought prediction method is using only a few number of predictors to avoid excessive noise in the predictor space. Cross-validated results show correlations between observed and predicted spatial average PDSI up to 0.60 and 0.45 for lead times of 5 and 9 months, respectively. To the best of our knowledge, this is the first study in the region that, based on cross-validation results, leads to appreciable forecast skills for lead times beyond 4 months. This is a step forward in better understanding the dynamics of Amazonia droughts and improving risk assessment and management, through improved drought forecasting.

Development and validation of an ICD-10-based disability predictive index for patients admitted to hospitals with trauma.

PubMed

Wada, Tomoki; Yasunaga, Hideo; Yamana, Hayato; Matsui, Hiroki; Fushimi, Kiyohide; Morimura, Naoto

2018-03-01

There was no established disability predictive measurement for patients with trauma that could be used in administrative claims databases. The aim of the present study was to develop and validate a diagnosis-based disability predictive index for severe physical disability at discharge using the International Classification of Diseases, 10th revision (ICD-10) coding. This retrospective observational study used the Diagnosis Procedure Combination database in Japan. Patients who were admitted to hospitals with trauma and discharged alive from 01 April 2010 to 31 March 2015 were included. Pediatric patients under 15 years old were excluded. Data for patients admitted to hospitals from 01 April 2010 to 31 March 2013 was used for development of a disability predictive index (derivation cohort), while data for patients admitted to hospitals from 01 April 2013 to 31 March 2015 was used for the internal validation (validation cohort). The outcome of interest was severe physical disability defined as the Barthel Index score of <60 at discharge. Trauma-related ICD-10 codes were categorized into 36 injury groups with reference to the categorization used in the Global Burden of Diseases study 2013. A multivariable logistic regression analysis was performed for the outcome using the injury groups and patient baseline characteristics including patient age, sex, and Charlson Comorbidity Index (CCI) score in the derivation cohort. A score corresponding to a regression coefficient was assigned to each injury group. The disability predictive index for each patient was defined as the sum of the scores. The predictive performance of the index was validated using the receiver operating characteristic curve analysis in the validation cohort. The derivation cohort included 1,475,158 patients, while the validation cohort included 939,659 patients. Of the 939,659 patients, 235,382 (25.0%) were discharged with severe physical disability. The c-statistics of the disability predictive index

Association of Body Mass Index with Depression, Anxiety and Suicide—An Instrumental Variable Analysis of the HUNT Study

PubMed Central

Bjørngaard, Johan Håkon; Carslake, David; Lund Nilsen, Tom Ivar; Linthorst, Astrid C. E.; Davey Smith, George; Gunnell, David; Romundstad, Pål Richard

2015-01-01

Objective While high body mass index is associated with an increased risk of depression and anxiety, cumulative evidence indicates that it is a protective factor for suicide. The associations from conventional observational studies of body mass index with mental health outcomes are likely to be influenced by reverse causality or confounding by ill-health. In the present study, we investigated the associations between offspring body mass index and parental anxiety, depression and suicide in order to avoid problems with reverse causality and confounding by ill-health. Methods We used data from 32,457 mother-offspring and 27,753 father-offspring pairs from the Norwegian HUNT-study. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale and suicide death from national registers. Associations between offspring and own body mass index and symptoms of anxiety and depression and suicide mortality were estimated using logistic and Cox regression. Causal effect estimates were estimated with a two sample instrument variable approach using offspring body mass index as an instrument for parental body mass index. Results Both own and offspring body mass index were positively associated with depression, while the results did not indicate any substantial association between body mass index and anxiety. Although precision was low, suicide mortality was inversely associated with own body mass index and the results from the analysis using offspring body mass index supported these results. Adjusted odds ratios per standard deviation body mass index from the instrumental variable analysis were 1.22 (95% CI: 1.05, 1.43) for depression, 1.10 (95% CI: 0.95, 1.27) for anxiety, and the instrumental variable estimated hazard ratios for suicide was 0.69 (95% CI: 0.30, 1.63). Conclusion The present study’s results indicate that suicide mortality is inversely associated with body mass index. We also found support for a positive association between body mass index

Association of Body Mass Index with Depression, Anxiety and Suicide-An Instrumental Variable Analysis of the HUNT Study.

PubMed

Bjørngaard, Johan Håkon; Carslake, David; Lund Nilsen, Tom Ivar; Linthorst, Astrid C E; Davey Smith, George; Gunnell, David; Romundstad, Pål Richard

2015-01-01

While high body mass index is associated with an increased risk of depression and anxiety, cumulative evidence indicates that it is a protective factor for suicide. The associations from conventional observational studies of body mass index with mental health outcomes are likely to be influenced by reverse causality or confounding by ill-health. In the present study, we investigated the associations between offspring body mass index and parental anxiety, depression and suicide in order to avoid problems with reverse causality and confounding by ill-health. We used data from 32,457 mother-offspring and 27,753 father-offspring pairs from the Norwegian HUNT-study. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale and suicide death from national registers. Associations between offspring and own body mass index and symptoms of anxiety and depression and suicide mortality were estimated using logistic and Cox regression. Causal effect estimates were estimated with a two sample instrument variable approach using offspring body mass index as an instrument for parental body mass index. Both own and offspring body mass index were positively associated with depression, while the results did not indicate any substantial association between body mass index and anxiety. Although precision was low, suicide mortality was inversely associated with own body mass index and the results from the analysis using offspring body mass index supported these results. Adjusted odds ratios per standard deviation body mass index from the instrumental variable analysis were 1.22 (95% CI: 1.05, 1.43) for depression, 1.10 (95% CI: 0.95, 1.27) for anxiety, and the instrumental variable estimated hazard ratios for suicide was 0.69 (95% CI: 0.30, 1.63). The present study's results indicate that suicide mortality is inversely associated with body mass index. We also found support for a positive association between body mass index and depression, but not for anxiety.

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

PubMed

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

2011-10-01

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

Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network

NASA Astrophysics Data System (ADS)

Yakubu, A.; Oluremi, O. I. A.; Ekpo, E. I.

2018-03-01

There is an increasing use of robust analytical algorithms in the prediction of heat stress. The present investigation therefore, was carried out to forecast heat stress index (HSI) in Sasso laying hens. One hundred and sixty seven records on the thermo-physiological parameters of the birds were utilized. They were reared on deep litter and battery cage systems. Data were collected when the birds were 42- and 52-week of age. The independent variables fitted were housing system, age of birds, rectal temperature (RT), pulse rate (PR), and respiratory rate (RR). The response variable was HSI. Data were analyzed using automatic linear modeling (ALM) and artificial neural network (ANN) procedures. The ALM model building method involved Forward Stepwise using the F Statistic criterion. As regards ANN, multilayer perceptron (MLP) with back-propagation network was used. The ANN network was trained with 90% of the data set while 10% were dedicated to testing for model validation. RR and PR were the two parameters of utmost importance in the prediction of HSI. However, the fractional importance of RR was higher than that of PR in both ALM (0.947 versus 0.053) and ANN (0.677 versus 0.274) models. The two models also predicted HSI effectively with high degree of accuracy [r = 0.980, R 2 = 0.961, adjusted R 2 = 0.961, and RMSE = 0.05168 (ALM); r = 0.983, R 2 = 0.966; adjusted R 2 = 0.966, and RMSE = 0.04806 (ANN)]. The present information may be exploited in the development of a heat stress chart based largely on RR. This may aid detection of thermal discomfort in a poultry house under tropical and subtropical conditions.

Options for refractive index and viscosity matching to study variable density flows

NASA Astrophysics Data System (ADS)

Clément, Simon A.; Guillemain, Anaïs; McCleney, Amy B.; Bardet, Philippe M.

2018-02-01

Variable density flows are often studied by mixing two miscible aqueous solutions of different densities. To perform optical diagnostics in such environments, the refractive index of the fluids must be matched, which can be achieved by carefully choosing the two solutes and the concentration of the solutions. To separate the effects of buoyancy forces and viscosity variations, it is desirable to match the viscosity of the two solutions in addition to their refractive index. In this manuscript, several pairs of index matched fluids are compared in terms of viscosity matching, monetary cost, and practical use. Two fluid pairs are studied in detail, with two aqueous solutions (binary solutions of water and a salt or alcohol) mixed into a ternary solution. In each case: an aqueous solution of isopropanol mixed with an aqueous solution of sodium chloride (NaCl) and an aqueous solution of glycerol mixed with an aqueous solution of sodium sulfate (Na_2SO_4). The first fluid pair allows reaching high-density differences at low cost, but brings a large difference in dynamic viscosity. The second allows matching dynamic viscosity and refractive index simultaneously, at reasonable cost. For each of these four solutes, the density, kinematic viscosity, and refractive index are measured versus concentration and temperature, as well as wavelength for the refractive index. To investigate non-linear effects when two index-matched, binary solutions are mixed, the ternary solutions formed are also analyzed. Results show that density and refractive index follow a linear variation with concentration. However, the viscosity of the isopropanol and NaCl pair deviates from the linear law and has to be considered. Empirical correlations and their coefficients are given to create index-matched fluids at a chosen temperature and wavelength. Finally, the effectiveness of the refractive index matching is illustrated with particle image velocimetry measurements performed for a buoyant jet in a

Prediction of Sym-H index by NARX neural network from IMF and solar wind data

NASA Astrophysics Data System (ADS)

Cai, L.; Ma, S.-Y.; Liu, R.-S.; Schlegel, K.; Zhou, Y.-L.; Luehr, H.

2009-04-01

Similar to Dst, the Sym-H index is also an indicator of magnetic storm intensity, but having distinct advantage of higher time-resolution. In this study an artificial neural network (ANN) of Nonlinear Auto Regressive with eXogenous inputs (NARX) has been developed to predict for the first time Sym-H index from solar wind and IMF parameters. In total 73 great storm events during 1998 to 2006 are used, out of which 67 are selected to train the network and the other 6 samples including 2 super-storms for test. The newly developed NARX model shows much better capability than usual BP and Elman network in Sym-H prediction. When using IMF Bz, By and total B with a history length of 90 minutes along with solar wind proton density Np and velocity Vsw as the original external inputs of the ANN to predict Sym-H index one hour later, the cross-correlation between NARX network predicted and Kyoto observed Sym-H is 0.95 for the 6 test storms as a whole, even as high as 0.95 and 0.98 respectively for the two super-storms. This excellent performance of the NARX model can mainly be attributed to a feedback from the output neuron with a suitable length of about 120 min. to the external input. It is such a feedback that makes the ring current status properly brought into effect in the prediction of storm-time Sym-H index by our NARX network. Furthermore, different parameter combinations with different history length (70 to 120 min.) for IMF and solar wind data as external inputs are examined along with different hidden neuron number. It is found that the NARX network with 10 hidden units and with 100 min. length of Bz, Np and Vsw as external inputs provides the best results in Sym-H prediction. Besides, efforts have also been made to predict Sym-H longer time ahead, showing that the NARX network can predict Sym-H index 180 min. ahead with correlation coefficient of 0.94 between predicted and observed Sym-H and RMSE less than 19 nT for the 6 test samples.

Predictive Accuracy of the Veterans Aging Cohort Study (VACS) Index for Mortality with HIV Infection: A North American Cross Cohort Analysis

PubMed Central

Justice, AC; Modur, S; Tate, JP; Althoff, KN; Jacobson, LP; Gebo, K; Kitahata, M; Horberg, M; Brooks, J; Buchacz, K; Rourke, SB; Rachlis, A; Napravnik, S; Eron, J; Willig, H; Moore, R; Kirk, GD; Bosch, R; Rodriguez, B; Hogg, RS; Thorne, J; Goedert, JJ; Klein, M; Gill, MJ; Deeks, S; Sterling, TR; Anastos, K; Gange, SJ

2013-01-01

Background By supplementing an index composed of HIV biomarkers and age (Restricted Index) with measures of organ injury, the Veterans Aging Cohort Study (VACS) Index more completely reflects risk of mortality. We compare the accuracy of the VACS and Restricted Indices 1) among subjects outside the Veterans Healthcare System (VA), 2) over 1–5 years of prior exposure to antiretroviral therapy (ART), and 3) within important patient subgroups. Methods We used data from 13 cohorts in the North American AIDS Cohort Collaboration (NA-ACCORD, n=10, 835) limiting analyses to HIV-infected subjects with at least 12 months exposure to ART. Variables included demographic, laboratory (CD4 count, HIV-1 RNA, hemoglobin, platelets, aspartate and alanine transaminase, creatinine and hepatitis C status), and survival. We used C statistic and net reclassification improvement (NRI) to test discrimination varying prior ART exposure from 1–5 years. We then combined VA (n=5,066) and NA-ACCORD data, fit a parametric survival model, and compared predicted to observed mortality by cohort, gender, age, race, and HIV-1 RNA level. Results Mean follow-up was 3.3 years (655 deaths). Compared with the Restricted Index, the VACS Index showed greater discrimination (C statistic: 0.77 vs. 0.74; NRI 12%; p<0.0001). NRI was highest among those with HIV-1 RNA<500 copies/ml (25%) and age ≥50 years (20%). Predictions were similar to observed mortality among all subgroups. Conclusion VACS Index scores discriminate risk and translate into accurate mortality estimates over 1–5 years of exposures to ART and for diverse patient subgroups from North American PMID:23187941

The NAFLD Index: A Simple and Accurate Screening Tool for the Prediction of Non-Alcoholic Fatty Liver Disease.

PubMed

Ichino, Naohiro; Osakabe, Keisuke; Sugimoto, Keiko; Suzuki, Koji; Yamada, Hiroya; Takai, Hiroji; Sugiyama, Hiroko; Yukitake, Jun; Inoue, Takashi; Ohashi, Koji; Hata, Tadayoshi; Hamajima, Nobuyuki; Nishikawa, Toru; Hashimoto, Senju; Kawabe, Naoto; Yoshioka, Kentaro

2015-01-01

Non-alcoholic fatty liver disease (NAFLD) is a common debilitating condition in many industrialized countries that increases the risk of cardiovascular disease. The aim of this study was to derive a simple and accurate screening tool for the prediction of NAFLD in the Japanese population. A total of 945 participants, 279 men and 666 women living in Hokkaido, Japan, were enrolled among residents who attended a health check-up program from 2010 to 2014. Participants with an alcohol consumption > 20 g/day and/or a chronic liver disease, such as chronic hepatitis B, chronic hepatitis C or autoimmune hepatitis, were excluded from this study. Clinical and laboratory data were examined to identify predictive markers of NAFLD. A new predictive index for NAFLD, the NAFLD index, was constructed for men and for women. The NAFLD index for men = -15.5693+0.3264 [BMI] +0.0134 [triglycerides (mg/dl)], and for women = -31.4686+0.3683 [BMI] +2.5699 [albumin (g/dl)] +4.6740[ALT/AST] -0.0379 [HDL cholesterol (mg/dl)]. The AUROC of the NAFLD index for men and for women was 0.87(95% CI 0.88-1.60) and 0.90 (95% CI 0.66-1.02), respectively. The cut-off point of -5.28 for men predicted NAFLD with an accuracy of 82.8%. For women, the cut-off point of -7.65 predicted NAFLD with an accuracy of 87.7%. A new index for the non-invasive prediction of NAFLD, the NAFLD index, was constructed using available clinical and laboratory data. This index is a simple screening tool to predict the presence of NAFLD.

Bispectral index to predict neurological outcome early after cardiac arrest.

PubMed

Stammet, Pascal; Collignon, Olivier; Werer, Christophe; Sertznig, Claude; Devaux, Yvan

2014-12-01

To address the value of continuous monitoring of bispectral index (BIS) to predict neurological outcome after cardiac arrest. In this prospective observational study in adult comatose patients treated by therapeutic hypothermia after cardiac arrest we measured bispectral index (BIS) during the first 24 hours of intensive care unit stay. A blinded neurological outcome assessment by cerebral performance category (CPC) was done 6 months after cardiac arrest. Forty-six patients (48%) had a good neurological outcome at 6-month, as defined by a cerebral performance category (CPC) 1-2, and 50 patients (52%) had a poor neurological outcome (CPC 3-5). Over the 24h of monitoring, mean BIS values over time were higher in the good outcome group (38 ± 9) compared to the poor outcome group (17 ± 12) (p<0.001). Analysis of BIS recorded every 30 minutes provided an optimal prediction after 12.5h, with an area under the receiver operating characteristic curve (AUC) of 0.89, a specificity of 89% and a sensitivity of 86% using a cut-off value of 23. With a specificity fixed at 100% (sensitivity 26%) the cut-off BIS value was 2.4 over the first 271 minutes. In multivariable analyses including clinical characteristics, mean BIS value over the first 12.5h was a predictor of neurological outcome (p = 6E-6) and provided a continuous net reclassification index of 1.28% (p = 4E-10) and an integrated discrimination improvement of 0.31 (p=1E-10). Mean BIS value calculated over the first 12.5h after ICU admission potentially predicts 6-months neurological outcome after cardiac arrest. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Hydroclimatic variability and predictability: a survey of recent research

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

Koster, Randal D.; Betts, Alan K.; Dirmeyer, Paul A.

Recent research in large-scale hydroclimatic variability is surveyed, focusing on five topics: (i) variability in general, (ii) droughts, (iii) floods, (iv) land–atmosphere coupling, and (v) hydroclimatic prediction. Moreover, each surveyed topic is supplemented by illustrative examples of recent research, as presented at a 2016 symposium honoring the career of Professor Eric Wood. Altogether, the recent literature and the illustrative examples clearly show that current research into hydroclimatic variability is strong, vibrant, and multifaceted.

Hydroclimatic variability and predictability: a survey of recent research

DOE PAGES

Koster, Randal D.; Betts, Alan K.; Dirmeyer, Paul A.; ...

2017-07-25

Recent research in large-scale hydroclimatic variability is surveyed, focusing on five topics: (i) variability in general, (ii) droughts, (iii) floods, (iv) land–atmosphere coupling, and (v) hydroclimatic prediction. Moreover, each surveyed topic is supplemented by illustrative examples of recent research, as presented at a 2016 symposium honoring the career of Professor Eric Wood. Altogether, the recent literature and the illustrative examples clearly show that current research into hydroclimatic variability is strong, vibrant, and multifaceted.

The taxonomic distinctness of macroinvertebrate communities of Atlantic Forest streams cannot be predicted by landscape and climate variables, but traditional biodiversity indices can.

PubMed

Roque, F O; Guimarães, E A; Ribeiro, M C; Escarpinati, S C; Suriano, M T; Siqueira, T

2014-11-01

Predicting how anthropogenic activities may influence the various components of biodiversity is essential for finding ways to reduce diversity loss. This challenge involves: a) understanding how environmental factors influence diversity across different spatial scales, and b) developing ways to measure these relationships in a way that is fast, economical, and easy to communicate. In this study, we investigate whether landscape and bioclimatic variables could explain variation in biodiversity indices in macroinvertebrate communities from 39 Atlantic Forest streams. In addition to traditional diversity measures, i.e., species richness, abundance and Shannon index, we used a taxonomic distinctness index that measures the degree of phylogenetic relationship among taxa. The amount of variation in the diversity measures that was explained by environmental and spatial variables was estimated using variation partitioning based on multiple regression. Our study demonstrates that taxonomic distinctness does not respond in the same way as the traditional used in biodiversity studies. We found no evidence that taxonomic distinctness responds predictably to variation in landscape metrics, indicating the need for the incorporation of predictors at multiple scales in this type of study. The lack of congruence between taxonomic distinctness and other indices and its low predictability may be related to the fact that this measure expresses long-term evolutionary adaptation to ecosystem conditions, while the other traditional biodiversity metrics respond to short-term environmental changes.

Utility of the MMPI Pain Assessment Index in Predicting Outcome After Lumbar Surgery.

ERIC Educational Resources Information Center

Turner, Judith; And Others

1986-01-01

Examined the ability of the Pain Assesment Index, determined from presurgery Minnesota Multiphasic Personality Inventory scores, to predict outcome subsequent to lumbar laminectomy and discectomy. The PAI was found to have good ability to identify patients who were doing well after surgery, but low power in predicting which patients would have…

Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach.

PubMed

Oyehan, Tajudeen A; Alade, Ibrahim O; Bagudu, Aliyu; Sulaiman, Kazeem O; Olatunji, Sunday O; Saleh, Tawfik A

2018-04-30

The optical properties of blood play crucial roles in medical diagnostics and treatment, and in the design of new medical devices. Haemoglobin is a vital constituent of the blood whose optical properties affect all of the optical properties of human blood. The refractive index of haemoglobin has been reported to strongly depend on its concentration which is a function of the physiology of biological cells. This makes the refractive index of haemoglobin an essential non-invasive bio-marker of diseases. Unfortunately, the complexity of blood tissue makes it challenging to experimentally measure the refractive index of haemoglobin. While a few studies have reported on the refractive index of haemoglobin, there is no solid consensus with the data obtained due to different measuring instruments and the conditions of the experiments. Moreover, obtaining the refractive index via an experimental approach is quite laborious. In this work, an accurate, fast and relatively convenient strategy to estimate the refractive index of haemoglobin is reported. Thus, the GA-SVR model is presented for the prediction of the refractive index of haemoglobin using wavelength, temperature, and the concentration of haemoglobin as descriptors. The model developed is characterised by an excellent accuracy and very low error estimates. The correlation coefficients obtained in these studies are 99.94% and 99.91% for the training and testing results, respectively. In addition, the result shows an almost perfect match with the experimental data and also demonstrates significant improvement over a recent mathematical model available in the literature. The GA-SVR model predictions also give insights into the influence of concentration, wavelength, and temperature on the RI measurement values. The model outcome can be used not only to accurately estimate the refractive index of haemoglobin but also could provide a reliable common ground to benchmark the experimental refractive index results

Underestimated AMOC Variability and Implications for AMV and Predictability in CMIP Models

NASA Astrophysics Data System (ADS)

Yan, Xiaoqin; Zhang, Rong; Knutson, Thomas R.

2018-05-01

The Atlantic Meridional Overturning Circulation (AMOC) has profound impacts on various climate phenomena. Using both observations and simulations from the Coupled Model Intercomparison Project Phase 3 and 5, here we show that most models underestimate the amplitude of low-frequency AMOC variability. We further show that stronger low-frequency AMOC variability leads to stronger linkages between the AMOC and key variables associated with the Atlantic multidecadal variability (AMV), and between the subpolar AMV signal and northern hemisphere surface air temperature. Low-frequency extratropical northern hemisphere surface air temperature variability might increase with the amplitude of low-frequency AMOC variability. Atlantic decadal predictability is much higher in models with stronger low-frequency AMOC variability and much lower in models with weaker or without AMOC variability. Our results suggest that simulating realistic low-frequency AMOC variability is very important, both for simulating realistic linkages between AMOC and AMV-related variables and for achieving substantially higher Atlantic decadal predictability.

The validity of the Gait Variability Index for individuals with mild to moderate Parkinson's disease.

PubMed

Rennie, Linda; Dietrichs, Espen; Moe-Nilssen, Rolf; Opheim, Arve; Franzén, Erika

2017-05-01

Increased step-to-step variability is a feature of gait in individuals with Parkinson's disease (PD) and is associated with increased disease severity and reductions in balance and mobility. The Gait Variability Index (GVI) quantifies gait variability in spatiotemporal variables where a score ≥100 indicates a similar level of gait variability as the control group, and lower scores denote increased gait variability. The study aim was to explore mean GVI score and investigate construct validity of the index for individuals with mild to moderate PD. 100 (57 males) subjects with idiopathic PD, Hoehn & Yahr 2 (n=44) and 3, and ≥60 years were included. Data on disease severity, dynamic balance, mobility and spatiotemporal gait parameters at self-selected speed (GAITRite) was collected. The results showed a mean overall GVI: 97.5 (SD 11.7) and mean GVI for the most affected side: 94.5 (SD 10.6). The associations between the GVI and Mini- BESTest and TUG were low (r=0.33 and 0.42) and the GVI could not distinguish between Hoehn & Yahr 2 and 3 (AUC=0.529, SE=0.058, p=0.622). The mean GVI was similar to previously reported values for older adults, contrary to consistent reports of increased gait variability in PD compared to healthy peers. Therefore, the validity of the GVI could not be confirmed for individuals with mild to moderate PD in its current form due to low associations with validated tests for functional balance and mobility and poor discriminatory ability. Future work should aim to establish which spatiotemporal variables are most informative regarding gait variability in individuals with PD. Copyright © 2017 Elsevier B.V. All rights reserved.

A behavioral economic reward index predicts drinking resolutions: moderation revisited and compared with other outcomes.

PubMed

Tucker, Jalie A; Roth, David L; Vignolo, Mary J; Westfall, Andrew O

2009-04-01

Data were pooled from 3 studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1- to 2-year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes than abstinence or relapse and to be predicted by more balanced preresolution monetary allocations between short-term and longer term objectives (i.e., drinking and saving for the future). Standardized odds ratios (ORs) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this "Alcohol-Savings Discretionary Expenditure" index predicted higher rates of abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p < .0001) compared with moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of preresolution spending patterns aids in predicting moderation.

Variable context Markov chains for HIV protease cleavage site prediction.

PubMed

Oğul, Hasan

2009-06-01

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids. It was applied for HIV-1 protease cleavage site prediction problem and shown to outperform existing methods in terms of prediction accuracy on a common dataset. In general, the method is a promising tool for prediction of cleavage sites of all proteases and encouraged to be used for any kind of peptide classification problem as well.

Predicting waist circumference from body mass index.

PubMed

Bozeman, Samuel R; Hoaglin, David C; Burton, Tanya M; Pashos, Chris L; Ben-Joseph, Rami H; Hollenbeak, Christopher S

2012-08-03

Being overweight or obese increases risk for cardiometabolic disorders. Although both body mass index (BMI) and waist circumference (WC) measure the level of overweight and obesity, WC may be more important because of its closer relationship to total body fat. Because WC is typically not assessed in clinical practice, this study sought to develop and verify a model to predict WC from BMI and demographic data, and to use the predicted WC to assess cardiometabolic risk. Data were obtained from the Third National Health and Nutrition Examination Survey (NHANES) and the Atherosclerosis Risk in Communities Study (ARIC). We developed linear regression models for men and women using NHANES data, fitting waist circumference as a function of BMI. For validation, those regressions were applied to ARIC data, assigning a predicted WC to each individual. We used the predicted WC to assess abdominal obesity and cardiometabolic risk. The model correctly classified 88.4% of NHANES subjects with respect to abdominal obesity. Median differences between actual and predicted WC were -0.07 cm for men and 0.11 cm for women. In ARIC, the model closely estimated the observed WC (median difference: -0.34 cm for men, +3.94 cm for women), correctly classifying 86.1% of ARIC subjects with respect to abdominal obesity and 91.5% to 99.5% as to cardiometabolic risk.The model is generalizable to Caucasian and African-American adult populations because it was constructed from data on a large, population-based sample of men and women in the United States, and then validated in a population with a larger representation of African-Americans. The model accurately estimates WC and identifies cardiometabolic risk. It should be useful for health care practitioners and public health officials who wish to identify individuals and populations at risk for cardiometabolic disease when WC data are unavailable.

Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach.

PubMed

Lee, Hyung-Chul; Ryu, Ho-Geol; Chung, Eun-Jin; Jung, Chul-Woo

2018-03-01

The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P < 0.001). The deep learning model-predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

Improved predictability of droughts over southern Africa using the standardized precipitation evapotranspiration index and ENSO

NASA Astrophysics Data System (ADS)

Manatsa, Desmond; Mushore, Terrence; Lenouo, Andre

2017-01-01

The provision of timely and reliable climate information on which to base management decisions remains a critical component in drought planning for southern Africa. In this observational study, we have not only proposed a forecasting scheme which caters for timeliness and reliability but improved relevance of the climate information by using a novel drought index called the standardised precipitation evapotranspiration index (SPEI), instead of the traditional precipitation only based index, the standardised precipitation index (SPI). The SPEI which includes temperature and other climatic factors in its construction has a more robust connection to ENSO than the SPI. Consequently, the developed ENSO-SPEI prediction scheme can provide quantitative information about the spatial extent and severity of predicted drought conditions in a way that reflects more closely the level of risk in the global warming context of the sub region. However, it is established that the ENSO significant regional impact is restricted only to the period December-March, implying a revisit to the traditional ENSO-based forecast scheme which essentially divides the rainfall season into the two periods, October to December and January to March. Although the prediction of ENSO events has increased with the refinement of numerical models, this work has demonstrated that the prediction of drought impacts related to ENSO is also a reality based only on observations. A large temporal lag is observed between the development of ENSO phenomena (typically in May of the previous year) and the identification of regional SPEI defined drought conditions. It has been shown that using the Southern Africa Regional Climate Outlook Forum's (SARCOF) traditional 3-month averaged Nino 3.4 SST index (June to August) as a predictor does not have an added advantage over using only the May SST index values. In this regard, the extended lead time and improved skill demonstrated in this study could immensely benefit

Empirical modelling to predict the refractive index of human blood.

PubMed

Yahya, M; Saghir, M Z

2016-02-21

Optical techniques used for the measurement of the optical properties of blood are of great interest in clinical diagnostics. Blood analysis is a routine procedure used in medical diagnostics to confirm a patient's condition. Measuring the optical properties of blood is difficult due to the non-homogenous nature of the blood itself. In addition, there is a lot of variation in the refractive indices reported in the literature. These are the reasons that motivated the researchers to develop a mathematical model that can be used to predict the refractive index of human blood as a function of concentration, temperature and wavelength. The experimental measurements were conducted on mimicking phantom hemoglobin samples using the Abbemat Refractometer. The results analysis revealed a linear relationship between the refractive index and concentration as well as temperature, and a non-linear relationship between refractive index and wavelength. These results are in agreement with those found in the literature. In addition, a new formula was developed based on empirical modelling which suggests that temperature and wavelength coefficients be added to the Barer formula. The verification of this correlation confirmed its ability to determine refractive index and/or blood hematocrit values with appropriate clinical accuracy.

Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network.

PubMed

Yakubu, A; Oluremi, O I A; Ekpo, E I

2018-03-17

There is an increasing use of robust analytical algorithms in the prediction of heat stress. The present investigation therefore, was carried out to forecast heat stress index (HSI) in Sasso laying hens. One hundred and sixty seven records on the thermo-physiological parameters of the birds were utilized. They were reared on deep litter and battery cage systems. Data were collected when the birds were 42- and 52-week of age. The independent variables fitted were housing system, age of birds, rectal temperature (RT), pulse rate (PR), and respiratory rate (RR). The response variable was HSI. Data were analyzed using automatic linear modeling (ALM) and artificial neural network (ANN) procedures. The ALM model building method involved Forward Stepwise using the F Statistic criterion. As regards ANN, multilayer perceptron (MLP) with back-propagation network was used. The ANN network was trained with 90% of the data set while 10% were dedicated to testing for model validation. RR and PR were the two parameters of utmost importance in the prediction of HSI. However, the fractional importance of RR was higher than that of PR in both ALM (0.947 versus 0.053) and ANN (0.677 versus 0.274) models. The two models also predicted HSI effectively with high degree of accuracy [r = 0.980, R 2  = 0.961, adjusted R 2  = 0.961, and RMSE = 0.05168 (ALM); r = 0.983, R 2  = 0.966; adjusted R 2  = 0.966, and RMSE = 0.04806 (ANN)]. The present information may be exploited in the development of a heat stress chart based largely on RR. This may aid detection of thermal discomfort in a poultry house under tropical and subtropical conditions.

Heart rate variability as predictive factor for sudden cardiac death.

PubMed

Sessa, Francesco; Anna, Valenzano; Messina, Giovanni; Cibelli, Giuseppe; Monda, Vincenzo; Marsala, Gabriella; Ruberto, Maria; Biondi, Antonio; Cascio, Orazio; Bertozzi, Giuseppe; Pisanelli, Daniela; Maglietta, Francesca; Messina, Antonietta; Mollica, Maria P; Salerno, Monica

2018-02-23

Sudden cardiac death (SCD) represents about 25% of deaths in clinical cardiology. The identification of risk factors for SCD is the philosopher's stone of cardiology and the identification of non-invasive markers of risk of SCD remains one of the most important goals for the scientific community.The aim of this review is to analyze the state of the art around the heart rate variability (HRV) as a predictor factor for SCD.HRV is probably the most analyzed index in cardiovascular risk stratification technical literature, therefore an important number of models and methods have been developed.Nowadays, low HRV has been shown to be independently predictive of increased mortality in post- myocardial infarction patients, heart failure patients, in contrast with the data of the general population.Contrariwise, the relationship between HRV and SCD has received scarce attention in low-risk cohorts. Furthermore, in general population the attributable risk is modest and the cost/benefit ratio is not always convenient.The HRV evaluation could become an important tool for health status in risks population, even though the use of HRV alone for risk stratification of SCD is limited and further studies are needed.

An index of ecological integrity for the Mississippi alluvial plain ecoregion: index development and relations to selected landscape variables

USGS Publications Warehouse

Justus, B.G.

2003-01-01

Macroinvertebrate community, fish community, water-quality, and habitat data collected from 36 sites in the Mississippi Alluvial Plain Ecoregion during 1996-98 by the U.S. Geological Survey were considered for a multimetric test of ecological integrity. Test metrics were correlated to site scores of a Detrended Correspondence Analysis of the fish community (the biological community that was the most statistically significant for indicating ecological conditions in the ecoregion) and six metrics--four fish metrics, one chemical metric (total ammonia plus organic nitrogen) and one physical metric (turbidity)--having the highest correlations were selected for the index. Index results indicate that sites in the northern half of the study unit (in Arkansas and Missouri) were less degraded than sites in the southern half of the study unit (in Louisiana and Mississippi). Of 148 landscape variables evaluated, the percentage of Holocene deposits and cotton insecticide rates had the highest correlations to index of ecological integrity results. sites having the highest (best) index scores had the lowest percentages of Holocene deposits and the lowest cotton insecticide use rates, indicating that factors relating to the amount of Holocene deposits and cotton insecticide use rates partially explain differences in ecological conditions throughout the Mississippi Alluvial Plain Ecoregion.

Utilizing the AAVSO's Variable Star Index (VSX) in Undergraduate Research Projects (Poster abstract)

NASA Astrophysics Data System (ADS)

Larsen, K.

2016-12-01

(Abstract only) Among the many important services that the American Association of Variable Star Observers (AAVSO) provides to the astronomical community is the Variable Star Index (VSX; https://www.aavso.org/vsx/). This online catalog of variable stars is the repository of data on over 334,000 variable stars, including information on spectral type, range of magnitude, period, and type of variable, among other properties. A number of these stars were identified as being variable through automated telescope surveys, such as ASAS (All Sky Automated Survey). The computer code of this survey classified newly discovered variables as best it could, but a significant number of false classifications have been noted. The reclassification of ASAS variables in the VSX data, as well as a closer look at variables identified as miscellaneous type in VSX, are two of many projects that can be undertaken by interested undergraduates. In doing so, students learn about the physical properties of various types of variable stars as well as statistical analysis and computer software, especially the vstar variable star data visualization and analysis tool that is available to the astronomical community free of charge on the AAVSO website (https://www.aavso.org/vstar-overview). Three such projects are described in this presentation, to identify BY Draconis variables misidentified as Cepheids or "miscellaneous", and SRD semiregular variables and ELL (rotating ellipsoidal) variables misidentified as "miscellaneous", in ASAS data and VSX.

Variability and Predictability of Land-Atmosphere Interactions: Observational and Modeling Studies

NASA Technical Reports Server (NTRS)

Roads, John; Oglesby, Robert; Marshall, Susan; Robertson, Franklin R.

2002-01-01

The overall goal of this project is to increase our understanding of seasonal to interannual variability and predictability of atmosphere-land interactions. The project objectives are to: 1. Document the low frequency variability in land surface features and associated water and energy cycles from general circulation models (GCMs), observations and reanalysis products. 2. Determine what relatively wet and dry years have in common on a region-by-region basis and then examine the physical mechanisms that may account for a significant portion of the variability. 3. Develop GCM experiments to examine the hypothesis that better knowledge of the land surface enhances long range predictability. This investigation is aimed at evaluating and predicting seasonal to interannual variability for selected regions emphasizing the role of land-atmosphere interactions. Of particular interest are the relationships between large, regional and local scales and how they interact to account for seasonal and interannual variability, including extreme events such as droughts and floods. North and South America, including the Global Energy and Water Cycle Experiment Continental International Project (GEWEX GCIP), MacKenzie, and LBA basins, are currently being emphasized. We plan to ultimately generalize and synthesize to other land regions across the globe, especially those pertinent to other GEWEX projects.

Resting cardiac vagal tone predicts intraindividual reaction time variability during an attention task in a sample of young and healthy adults.

PubMed

Williams, DeWayne P; Thayer, Julian F; Koenig, Julian

2016-12-01

Intraindividual reaction time variability (IIV), defined as the variability in trial-to-trial response times, is thought to serve as an index of central nervous system function. As such, greater IIV reflects both poorer executive brain function and cognitive control, in addition to lapses in attention. Resting-state vagally mediated heart rate variability (vmHRV), a psychophysiological index of self-regulatory abilities, has been linked with executive brain function and cognitive control such that those with greater resting-state vmHRV often perform better on cognitive tasks. However, research has yet to investigate the direct relationship between resting vmHRV and task IIV. The present study sought to examine this relationship in a sample of 104 young and healthy participants who first completed a 5-min resting-baseline period during which resting-state vmHRV was assessed. Participants then completed an attentional (target detection) task, where reaction time, accuracy, and trial-to-trial IIV were obtained. Results showed resting vmHRV to be significantly related to IIV, such that lower resting vmHRV predicted higher IIV on the task, even when controlling for several covariates (including mean reaction time and accuracy). Overall, our results provide further evidence for the link between resting vmHRV and cognitive control, and extend these notions to the domain of lapses in attention, as indexed by IIV. Implications and recommendations for future research on resting vmHRV and cognition are discussed. © 2016 Society for Psychophysiological Research.

Accuracy of Body Mass Index Versus Lean Mass Index for Prediction of Sarcopenia in Older Women.

PubMed

Benton, M J; Silva-Smith, A L

2018-01-01

We compared accuracy of body mass index (BMI) versus lean mass index (LMI) to predict sarcopenia in 58 community-dwelling women (74.1±0.9 years). Lean mass was measured with multi-frequency bioelectrical impedance analysis, and strength was measured with Arm Curl test, Chair Stand test, and handgrip dynamometry. Sarcopenia was defined as low LMI. When categorized by BMI, normal women had less absolute lean mass (37.6±1.0 vs. 42.6±0.9 kg; P<0.001) and less relative lean mass (14.1±0.2 vs. 16.1±0.2 kg/m2; P<0.001) compared to overweight/obese women, but no differences in strength. When categorized by LMI, normal women had more absolute lean mass (44.0±0.7 vs. 35.7±0.7 kg; P<0.001), more relative lean mass (16.2±0.2 vs. 13.8±0.2 kg/m2; P<0.001), and greater upper body strength (16.7±0.9 vs. 14.2±0.6 arm curls; P<0.05) compared to women with low LMI. BMI failed to accurately predict low values of lean mass and strength. For clinical assessment, calculation of LMI rather than BMI is appropriate.

Quantifying Variability of Avian Colours: Are Signalling Traits More Variable?

PubMed Central

Delhey, Kaspar; Peters, Anne

2008-01-01

Background Increased variability in sexually selected ornaments, a key assumption of evolutionary theory, is thought to be maintained through condition-dependence. Condition-dependent handicap models of sexual selection predict that (a) sexually selected traits show amplified variability compared to equivalent non-sexually selected traits, and since males are usually the sexually selected sex, that (b) males are more variable than females, and (c) sexually dimorphic traits more variable than monomorphic ones. So far these predictions have only been tested for metric traits. Surprisingly, they have not been examined for bright coloration, one of the most prominent sexual traits. This omission stems from computational difficulties: different types of colours are quantified on different scales precluding the use of coefficients of variation. Methodology/Principal Findings Based on physiological models of avian colour vision we develop an index to quantify the degree of discriminable colour variation as it can be perceived by conspecifics. A comparison of variability in ornamental and non-ornamental colours in six bird species confirmed (a) that those coloured patches that are sexually selected or act as indicators of quality show increased chromatic variability. However, we found no support for (b) that males generally show higher levels of variability than females, or (c) that sexual dichromatism per se is associated with increased variability. Conclusions/Significance We show that it is currently possible to realistically estimate variability of animal colours as perceived by them, something difficult to achieve with other traits. Increased variability of known sexually-selected/quality-indicating colours in the studied species, provides support to the predictions borne from sexual selection theory but the lack of increased overall variability in males or dimorphic colours in general indicates that sexual differences might not always be shaped by similar selective

The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements

NASA Astrophysics Data System (ADS)

Lucas, S. E.; Todd, J. F.

2015-12-01

The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). The CVP Program currently supports multiple projects in areas that are aimed at improved representation of physical processes in global models. Some of the topics that are currently funded include: i) Improved Understanding of Intraseasonal Tropical Variability - DYNAMO field campaign and post -field projects, and the new climate model improvement teams focused on MJO processes; ii) Climate Process Teams (CPTs, co-funded with NSF) with projects focused on Cloud macrophysical parameterization and its application to aerosol indirect effects, and Internal-Wave Driven Mixing in Global Ocean Models; iii) Improved Understanding of Tropical Pacific Processes, Biases, and Climatology; iv) Understanding Arctic Sea Ice Mechanism and Predictability;v) AMOC Mechanisms and Decadal Predictability Recent results from CVP-funded projects will be summarized. Additional information can be found at http://cpo.noaa.gov/CVP.

Analyst-to-Analyst Variability in Simulation-Based Prediction

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

Glickman, Matthew R.; Romero, Vicente J.

This report describes findings from the culminating experiment of the LDRD project entitled, "Analyst-to-Analyst Variability in Simulation-Based Prediction". For this experiment, volunteer participants solving a given test problem in engineering and statistics were interviewed at different points in their solution process. These interviews are used to trace differing solutions to differing solution processes, and differing processes to differences in reasoning, assumptions, and judgments. The issue that the experiment was designed to illuminate -- our paucity of understanding of the ways in which humans themselves have an impact on predictions derived from complex computational simulations -- is a challenging and openmore » one. Although solution of the test problem by analyst participants in this experiment has taken much more time than originally anticipated, and is continuing past the end of this LDRD, this project has provided a rare opportunity to explore analyst-to-analyst variability in significant depth, from which we derive evidence-based insights to guide further explorations in this important area.« less

Predicting preference-based SF-6D index scores from the SF-8 health survey.

PubMed

Wang, P; Fu, A Z; Wee, H L; Lee, J; Tai, E S; Thumboo, J; Luo, N

2013-09-01

To develop and test functions for predicting the preference-based SF-6D index scores from the SF-8 health survey. This study was a secondary analysis of data collected in a population health survey in which respondents (n = 7,529) completed both the SF-36 and the SF-8 questionnaires. We examined seven ordinary least-square estimators for their performance in predicting SF-6D scores from the SF-8 at both the individual and the group levels. In general, all functions performed similarly well in predicting SF-6D scores, and the predictions at the group level were better than predictions at the individual level. At the individual level, 42.5-51.5% of prediction errors were smaller than the minimally important difference (MID) of the SF-6D scores, depending on the function specifications, while almost all prediction errors of the tested functions were smaller than the MID of SF-6D at the group level. At both individual and group levels, the tested functions predicted lower than actual scores at the higher end of the SF-6D scale. Our study developed functions to generate preference-based SF-6D index scores from the SF-8 health survey, the first of its kind. Further research is needed to evaluate the performance and validity of the prediction functions.

Estimates of solar variability using the solar backscatter ultraviolet (SBUV) 2 Mg II index from the NOAA 9 satellite

NASA Technical Reports Server (NTRS)

Cebula, Richard P.; Deland, Matthew T.; Schlesinger, Barry M.

1992-01-01

The Mg II core to wing index was first developed for the Nimbus 7 solar backscatter ultraviolet (SBUV) instrument as an indicator of solar variability on both solar 27-day rotational and solar cycle time scales. This work extends the Mg II index to the NOAA 9 SBUV 2 instrument and shows that the variations in absolute value between Mg II index data sets caused by interinstrument differences do not affect the ability to track temporal variations. The NOAA 9 Mg II index accurately represents solar rotational modulation but contains more day-to-day noise than the Nimbus 7 Mg II index. Solar variability at other UV wavelengths is estimated by deriving scale factors between the Mg II index rotational variations and at those selected wavelengths. Based on the 27-day average of the NOAA 9 Mg II index and the NOAA 9 scale factors, the solar irradiance change from solar minimum in September 1986 to the beginning of the maximum of solar cycle 22 in 1989 is estimated to be 8.6 percent at 205 nm, 3.5 percent at 250 nm, and less than 1 percent beyond 300 nm.

[Behavior of predictive variables of exacerbations of the COPD in the neumological hospital of Cuba.

PubMed

León Valdivies, Yusbiel José; Sánchez de la Osa, Reinaldo B; Garcia Silvera, Eberto; Machado Molina, Delfina; Oses Herrera, Liliana

2017-01-01

The use of predictive variables of exacerbations of the COPD is not a practice generalized in our environment, for what we cannot characterize the exacerbating patient neither to design strategies for its integral handling. There was carried out a prospective descriptive study to correlate in patient with diagnosis of COPD from the Neumologic Hospital of Cuba, with the objective of determining the association between clinical, functional variables and imagenological and the exacerbations frequency a year. The population was constituted for patients with clinical diagnosis of COPD and the sample for those patients with confirmed diagnosis that they completed the inclusion approaches. The correlation among the variables was carried out by means of the Coefficient of Correlation of Pearson with an interval of Trust of 95% and the test t student with a significance level (p) smaller than 0.05. 81.82% of the very serious patients are exacerbating with emphysema. 75% of the patients with index of the lung artery / aorta have more than two exacerbations a year. 84.61% of the patient exacerbating presented degree four of the dyspnea. The half pressure of the lung artery next to the VEF1 constituted the best exacerbations predictors in the group of studied patients.

Prediction of remission of depression with clinical variables, neuropsychological performance, and serotonergic/dopaminergic gene polymorphisms.

PubMed

Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Asbun-Bojalil, Juan; Lira-Islas, Yuridia; Reyes-Ponce, Celia; Guàrdia-Olmos, Joan

2012-11-01

The aim of our work is to study the possible role of clinical variables, neuropsychological performance, and the 5HTTLPR, rs25531, and val108/58Met COMT polymorphisms on the prediction of depression remission after 12 weeks' treatment with fluoxetine. These variables have been studied as potential predictors of depression remission, but they present poor prognostic sensitivity and specificity by themselves. Seventy-two depressed patients were genotyped according to the aforementioned polymorphisms and were clinically and neuropsychologically assessed before a 12-week fluxetine treatment. Only the La allele of rs25531 polymorphism and the GG and AA forms of the val 108/158 Met polymorphism predict major depressive disorder remission after 12 weeks' treatment with fluoxetine. None of the clinical and neuropsychological variables studied predicted remission. Our results suggest that clinical and neuropsychological variables can initially predict early response to fluoxetine and mask the predictive role of genetic variables; but in remission, where clinical and neuropsychological symptoms associated with depression tend to disappear thanks to the treatment administered, the polymorphisms studied are the only variables in our model capable of predicting remission. However, placebo effects that are difficult to control require cautious interpretation of the results.

Real Time Monitoring and Prediction of the Monsoon Intraseasonal Oscillations: An index based on Nonlinear Laplacian Spectral Analysis Technique

NASA Astrophysics Data System (ADS)

Cherumadanakadan Thelliyil, S.; Ravindran, A. M.; Giannakis, D.; Majda, A.

2016-12-01

An improved index for real time monitoring and forecast verification of monsoon intraseasonal oscillations (MISO) is introduced using the recently developed Nonlinear Laplacian Spectral Analysis (NLSA) algorithm. Previous studies has demonstrated the proficiency of NLSA in capturing low frequency variability and intermittency of a time series. Using NLSA a hierarchy of Laplace-Beltrami (LB) eigen functions are extracted from the unfiltered daily GPCP rainfall data over the south Asian monsoon region. Two modes representing the full life cycle of complex northeastward propagating boreal summer MISO are identified from the hierarchy of Laplace-Beltrami eigen functions. These two MISO modes have a number of advantages over the conventionally used Extended Empirical Orthogonal Function (EEOF) MISO modes including higher memory and better predictability, higher fractional variance over the western Pacific, Western Ghats and adjoining Arabian Sea regions and more realistic representation of regional heat sources associated with the MISO. The skill of NLSA based MISO indices in real time prediction of MISO is demonstrated using hindcasts of CFSv2 extended range prediction runs. It is shown that these indices yield a higher prediction skill than the other conventional indices supporting the use of NLSA in real time prediction of MISO. Real time monitoring and prediction of MISO finds its application in agriculture, construction and hydro-electric power sectors and hence an important component of monsoon prediction.

Variability and rapid increase in body mass index during childhood are associated with adult obesity.

PubMed

Li, Shengxu; Chen, Wei; Sun, Dianjianyi; Fernandez, Camilo; Li, Jian; Kelly, Tanika; He, Jiang; Krousel-Wood, Marie; Whelton, Paul K

2015-12-01

Body mass index (BMI) in childhood predicts obesity in adults, but it is unknown whether rapid increase and variability in BMI during childhood are independent predictors of adult obesity. The study cohort consisted of 1622 Bogalusa Heart Study participants (aged 20 to 51 years at follow-up) who had been screened at least four times during childhood (aged 4-19 years). BMI rate of change during childhood for each individual was assessed by mixed models; BMI residual standard deviation (RSD) during childhoodwas used as a measure of variability. The average follow-up period was 20.9 years. One standard deviation increase in rate of change in BMI during childhood was associated with 1.39 [95% confidence interval (CI): 1.17-1.61] kg/m(2) increase in adult BMI and 2.98 (95% CI: 2.42-3.56) cm increase in adult waist circumference, independently of childhood mean BMI. Similarly, one standard deviation increase in RSD in BMI during childhood was associated with 0.46 (95% CI: 0.23-0.69) kg/m(2) increase in adult BMI and 1.42 (95% CI: 0.82-2.02) cm increase in adult waist circumference. Odds ratio for adult obesity progressively increased from the lowest to the highest quartile of BMI rate of change or RSD during childhood (P for trend < 0.05 for both). Rapid increase and greater variability in BMI during childhood appear to be independent risk factors for adult obesity. Our findings have implications for understanding body weight regulation and obesity development from childhood to adulthood. © The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

How well does the standard body mass index or variations with a different exponent predict human lifespan?

PubMed

Foster, Dean; Karloff, Howard; Shirley, Kenneth E

2016-02-01

The objective was twofold: (1) to estimate for each individual the body mass index (BMI) which is associated with the lowest risk of death, and (2) to study variants of the BMI formula to determine which gives the best predictions of death. Treating BMI = mass/height(2) as a continuous variable and estimating its interaction effects with several other variables, this study analyzed the NIH-AARP study data set of approximately 566,000 individuals and fit Cox proportional hazards models to the survival times. For each individual, a "personalized optimal BMI," the BMI for that individual which, according to the model, is associated with the lowest risk of death, is estimated. The average personalized optimal BMI is approximately 26, which is in the current "overweight" category. In fact, mass/height is a better predictor of death on the data set than BMI itself. The model suggests that an individual's "optimal" BMI depends on his or her features; "one-size-fits-all" recommendations may be not best. © 2016 The Obesity Society.

Effect of year-to-year variability of leaf area index on variable infiltration capacity model performance and simulation of streamflow during drought

NASA Astrophysics Data System (ADS)

Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

2014-09-01

This study assessed the effect of using observed monthly leaf area index (LAI) on hydrologic model performance and the simulation of streamflow during drought using the variable infiltration capacity (VIC) hydrological model in the Goulburn-Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) observed monthly LAI dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash-Sutcliffe efficiency, the logarithm transformed flow Nash-Sutcliffe efficiency and the percentage bias. Finally, the percentage deviation of the simulated monthly streamflow using the observed monthly LAI from simulated streamflow using long-term mean monthly LAI was computed. The VIC model predicted monthly streamflow in the selected sub-catchments with model efficiencies ranging from 61.5 to 95.9% during calibration (1982-1997) and 59 to 92.4% during validation (1998-2012). Our results suggest systematic improvements from 4 to 25% in the Nash-Sutcliffe efficiency in pasture dominated catchments when the VIC model was calibrated with the observed monthly LAI instead of the long-term mean monthly LAI. There was limited systematic improvement in tree dominated catchments. The results also suggest that the model overestimation or underestimation of streamflow during wet and dry periods can be reduced to some extent by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

Applying the concept of ecohydrological equilibrium to predict steady-state leaf area index for Australian ecosystems

NASA Astrophysics Data System (ADS)

Yang, J.; Medlyn, B.; De Kauwe, M. G.; Duursma, R.

2017-12-01

Leaf Area Index (LAI) is a key variable in modelling terrestrial vegetation, because it has a major impact on carbon, water and energy fluxes. However, LAI is difficult to predict: several recent intercomparisons have shown that modelled LAI differs significantly among models, and between models and satellite-derived estimates. Empirical studies show that long-term mean LAI is strongly related to mean annual precipitation. This observation is predicted by the theory of ecohydrological equilibrium, which provides a promising alternative means to predict steady-state LAI. We implemented this theory in a simple optimisation model. We hypothesized that, when water availability is limited, plants should adjust long-term LAI and stomatal behavior (g1) to maximize net canopy carbon export, under the constraint that canopy transpiration is a fixed fraction of total precipitation. We evaluated the predicted LAI (Lopt) for Australia against ground-based observations of LAI at 135 sites, and continental-scale satellite-derived estimates. For the site-level data, the RMSE of predicted Lopt was 0.14 m2 m-2, which was similar to the RMSE of a comparison of the data against nine-year mean satellite-derived LAI at those sites. Continentally, Lopt had a R2 of over 70% when compared to satellite-derived LAI, which is comparable to the R2 obtained when different satellite products are compared against each other. The predicted response of Lopt to the increase in atmospheric CO2 over the last 30 years also agreed with the estimate based on satellite-derivatives. Our results indicate that long-term equilibrium LAI can be successfully predicted from a simple application of ecohydrological theory. We suggest that this theory could be usefully incorporated into terrestrial vegetation models to improve their predictions of LAI.

Refractive Index Imaging of Cells with Variable-Angle Near-Total Internal Reflection (TIR) Microscopy.

PubMed

Bohannon, Kevin P; Holz, Ronald W; Axelrod, Daniel

2017-10-01

The refractive index in the interior of single cells affects the evanescent field depth in quantitative studies using total internal reflection (TIR) fluorescence, but often that index is not well known. We here present method to measure and spatially map the absolute index of refraction in a microscopic sample, by imaging a collimated light beam reflected from the substrate/buffer/cell interference at variable angles of incidence. Above the TIR critical angle (which is a strong function of refractive index), the reflection is 100%, but in the immediate sub-critical angle zone, the reflection intensity is a very strong ascending function of incidence angle. By analyzing the angular position of that edge at each location in the field of view, the local refractive index can be estimated. In addition, by analyzing the steepness of the edge, the distance-to-substrate can be determined. We apply the technique to liquid calibration samples, silica beads, cultured Chinese hamster ovary cells, and primary culture chromaffin cells. The optical technique suffers from decremented lateral resolution, scattering, and interference artifacts. However, it still provides reasonable results for both refractive index (~1.38) and for distance-to-substrate (~150 nm) for the cells, as well as a lateral resolution to about 1 µm.

Probabilistic approaches to accounting for data variability in the practical application of bioavailability in predicting aquatic risks from metals.

PubMed

Ciffroy, Philippe; Charlatchka, Rayna; Ferreira, Daniel; Marang, Laura

2013-07-01

The biotic ligand model (BLM) theoretically enables the derivation of environmental quality standards that are based on true bioavailable fractions of metals. Several physicochemical variables (especially pH, major cations, dissolved organic carbon, and dissolved metal concentrations) must, however, be assigned to run the BLM, but they are highly variable in time and space in natural systems. This article describes probabilistic approaches for integrating such variability during the derivation of risk indexes. To describe each variable using a probability density function (PDF), several methods were combined to 1) treat censored data (i.e., data below the limit of detection), 2) incorporate the uncertainty of the solid-to-liquid partitioning of metals, and 3) detect outliers. From a probabilistic perspective, 2 alternative approaches that are based on log-normal and Γ distributions were tested to estimate the probability of the predicted environmental concentration (PEC) exceeding the predicted non-effect concentration (PNEC), i.e., p(PEC/PNEC>1). The probabilistic approach was tested on 4 real-case studies based on Cu-related data collected from stations on the Loire and Moselle rivers. The approach described in this article is based on BLM tools that are freely available for end-users (i.e., the Bio-Met software) and on accessible statistical data treatments. This approach could be used by stakeholders who are involved in risk assessments of metals for improving site-specific studies. Copyright © 2013 SETAC.

Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables

NASA Astrophysics Data System (ADS)

Mortensen, Eric; Wu, Shu; Notaro, Michael; Vavrus, Stephen; Montgomery, Rob; De Piérola, José; Sánchez, Carlos; Block, Paul

2018-01-01

Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

[Non-invasive fibrosis indexes in predicting acute liver function deterioration after transcatheter arterial chemoembolization].

PubMed

Song, Y P; Zhao, Q Y; Li, S; Wang, H; Wu, P H

2016-03-08

To investigate the ability of two non-invasive fibrosis indexes-APRI, i. e. aspartate transaminase (AST) to platelet (PLT) ratio index, and fibrosis index based on the 4 factors (FIB-4)score in predicting ALFD in patients with unresectable primary HCC and underwent TACE. Clinical data of those patients treated with TACE in Department of Interventional Radiology of the Center from Jan 2010 to Aug 2014 were investigated retrospectively. A total of 366 cases were enrolled after randomized selection, 62 (18.5%) of which developed ALFD after TACE. Child-Pugh score, APRI and FIB-4 score in every case were calculated, receiver operating characteristic (ROC) curve of each model were performed and the predictive abilities of them were assessed by area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity. The AUC of Child-Pugh score, APRI and FIB-4 score were 0.783, 0.752 and 0.758 respectively, while the difference had no significance in statistics, indicating that predictive accuracies of them were similar. APRI≤1.15 and FIB-4≤3.08 had better NPV (90.6% and 93.6%) and sensitivity (65.6% and 80.0%) than Child-Pugh score>6 (NPV=85.8%, sensitivity=27.4%), PPV and specificity of them are 35.7%, 32.9%, 89.5% and 73.7%, 64.2%, 99.3% respectively. Comparing to Child-Pugh score, APRI and FIB-4 score have similar accuracy but better NPV and sensitivity in predicting post-TACE ALFD. Thereafter they are good for selection of low-risk patients for TACE treatment. Candidates with an APRI≤1.15 or a FIB-4≤3.08 or in Child-Pugh a stage are unlikely to develop ALFD thus could receive TACE safely.

Can biomechanical variables predict improvement in crouch gait?

PubMed Central

Hicks, Jennifer L.; Delp, Scott L.; Schwartz, Michael H.

2011-01-01

Many patients respond positively to treatments for crouch gait, yet surgical outcomes are inconsistent and unpredictable. In this study, we developed a multivariable regression model to determine if biomechanical variables and other subject characteristics measured during a physical exam and gait analysis can predict which subjects with crouch gait will demonstrate improved knee kinematics on a follow-up gait analysis. We formulated the model and tested its performance by retrospectively analyzing 353 limbs of subjects who walked with crouch gait. The regression model was able to predict which subjects would demonstrate ‘improved’ and ‘unimproved’ knee kinematics with over 70% accuracy, and was able to explain approximately 49% of the variance in subjects’ change in knee flexion between gait analyses. We found that improvement in stance phase knee flexion was positively associated with three variables that were drawn from knowledge about the biomechanical contributors to crouch gait: i) adequate hamstrings lengths and velocities, possibly achieved via hamstrings lengthening surgery, ii) normal tibial torsion, possibly achieved via tibial derotation osteotomy, and iii) sufficient muscle strength. PMID:21616666

Effect of Water Invasion on Outburst Predictive Index of Low Rank Coals in Dalong Mine

PubMed Central

Jiang, Jingyu; Cheng, Yuanping; Mou, Junhui; Jin, Kan; Cui, Jie

2015-01-01

To improve the coal permeability and outburst prevention, coal seam water injection and a series of outburst prevention measures were tested in outburst coal mines. These methods have become important technologies used for coal and gas outburst prevention and control by increasing the external moisture of coal or decreasing the stress of coal seam and changing the coal pore structure and gas desorption speed. In addition, techniques have had a significant impact on the gas extraction and outburst prevention indicators of coal seams. Globally, low rank coals reservoirs account for nearly half of hidden coal reserves and the most obvious feature of low rank coal is the high natural moisture content. Moisture will restrain the gas desorption and will affect the gas extraction and accuracy of the outburst prediction of coals. To study the influence of injected water on methane desorption dynamic characteristics and the outburst predictive index of coal, coal samples were collected from the Dalong Mine. The methane adsorption/desorption test was conducted on coal samples under conditions of different injected water contents. Selective analysis assessed the variations of the gas desorption quantities and the outburst prediction index (coal cutting desorption index). Adsorption tests indicated that the Langmuir volume of the Dalong coal sample is ~40.26 m3/t, indicating a strong gas adsorption ability. With the increase of injected water content, the gas desorption amount of the coal samples decreased under the same pressure and temperature. Higher moisture content lowered the accumulation desorption quantity after 120 minutes. The gas desorption volumes and moisture content conformed to a logarithmic relationship. After moisture correction, we obtained the long-flame coal outburst prediction (cutting desorption) index critical value. This value can provide a theoretical basis for outburst prediction and prevention of low rank coal mines and similar occurrence conditions

Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

NASA Astrophysics Data System (ADS)

Gengler, Sarah; Bogaert, Patrick

2014-12-01

First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression.

Relating trophic resources to community structure: a predictive index of food availability

PubMed Central

Edgar, Graham J.

2017-01-01

The abundance and the distribution of trophic resources available for consumers influence the productivity and the diversity of natural communities. Nevertheless, assessment of the actual abundance of food items available for individual trophic groups has been constrained by differences in methods and metrics used by various authors. Here we develop an index of food abundance, the framework of which can be adapted for different ecosystems. The relative available food index (RAFI) is computed by considering standard resource conditions of a habitat and the influence of various generalized anthropogenic and natural factors. RAFI was developed using published literature on food abundance and validated by comparison of predictions versus observed trophic resources across various marine sites. RAFI tables here proposed can be applied to a range of marine ecosystems for predictions of the potential abundance of food available for each trophic group, hence permitting exploration of ecological theories by focusing on the deviation from the observed to the expected. PMID:28386417

Evaluating high temporal and spatial resolution vegetation index for crop yield prediction

USDA-ARS?s Scientific Manuscript database

Remote sensing data have been widely used in estimating crop yield. Remote sensing derived parameters such as Vegetation Index (VI) were used either directly in building empirical models or by assimilating with crop growth models to predict crop yield. The abilities of remote sensing VI in crop yiel...

Prediction of poly(ethylene) glycol-drug eutectic compositions using an index based on the van't Hoff equation.

PubMed

Law, Devalina; Wang, Weili; Schmitt, Eric A; Long, Michelle A

2002-03-01

To define an index based on the van't Hoff equation that can be used as a screening tool for predicting poly(ethylene) glycol (PEG)-drug eutectic composition. Phase diagrams of PEG with ritonavir, ibuprofen, fenofibrate. naproxen, and griseofulvin were constructed using differential scanning calorimetry, hot stage microscopy and powder X-ray diftractometry. Previously reported phase diagrams were also used to test the predictive capability of the index. This work shows that a modified van't Hoff equation can be used to model the drug liquidus line of these phase diagrams. The slope of the liquidus line depends on the melting point (T(f)d) and heat of fusion (deltaH(f)d) of the drug and describes the initial rate at which the eutectic or monotectic point is approached. Based on this finding, a dimensionless index Ic was defined. The index can be calculated from the melting points of the pure components and heat of fusion of the drug. In addition to the compounds listed above, the index was found to predict the eutectic composition for flurbiprofen, temazepam and indomethacin. These compounds range over 150 degrees C in T(f)d, and from 25-65 kJ/mole in deltaH(f)d. Using Ic the approximate eutectic composition for eight different compounds was predicted. The index provides a useful screening tool for assessing the maximum drug loading in a drug-polymer eutectic/monotectic formulation.

An index predictive of cognitive outcome in retired professional American Football players with a history of sports concussion.

PubMed

Wright, Mathew J; Woo, Ellen; Birath, J Brandon; Siders, Craig A; Kelly, Daniel F; Wang, Christina; Swerdloff, Ronald; Romero, Elizabeth; Kernan, Claudia; Cantu, Robert C; Guskiewicz, Kevin

2016-01-01

Various concussion characteristics and personal factors are associated with cognitive recovery in athletes. We developed an index based on concussion frequency, severity, and timeframe, as well as cognitive reserve (CR), and we assessed its predictive power regarding cognitive ability in retired professional football players. Data from 40 retired professional American football players were used in the current study. On average, participants had been retired from football for 20 years. Current neuropsychological performances, indicators of CR, concussion history, and play data were used to create an index for predicting cognitive outcome. The sample displayed a range of concussions, concussion severities, seasons played, CR, and cognitive ability. Many of the participants demonstrated cognitive deficits. The index strongly predicted global cognitive ability (R(2) = .31). The index also predicted the number of areas of neuropsychological deficit, which varied as a function of the deficit classification system used (Heaton: R(2) = .15; Wechsler: R(2) = .28). The current study demonstrated that a unique combination of CR, sports concussion, and game-related data can predict cognitive outcomes in participants who had been retired from professional American football for an average of 20 years. Such indices may prove to be useful for clinical decision making and research.

A Behavioral Economic Reward Index Predicts Drinking Resolutions: Moderation Re-visited and Compared with Other Outcomes

PubMed Central

Tucker, Jalie A.; Roth, David L.; Vignolo, Mary J.; Westfall, Andrew O.

2014-01-01

Data were pooled from three studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1-2 year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes compared to abstinence or relapse and to be predicted by more balanced pre-resolution monetary allocations between short- and longer-term objectives (i.e., drinking and saving for the future). Standardized odds ratios (OR) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this “Alcohol-Savings Discretionary Expenditure” index predicted higher rates of both abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p < .0001) compared to moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of pre-resolution spending patterns aids in predicting moderation. PMID:19309182

The predictive content of CBOE crude oil volatility index

NASA Astrophysics Data System (ADS)

Chen, Hongtao; Liu, Li; Li, Xiaolei

2018-02-01

Volatility forecasting is an important issue in the area of econophysics. The information content of implied volatility for financial return volatility has been well documented in the literature but very few studies focus on oil volatility. In this paper, we show that the CBOE crude oil volatility index (OVX) has predictive ability for spot volatility of WTI and Brent oil returns, from both in-sample and out-of-sample perspectives. Including OVX-based implied volatility in GARCH-type volatility models can improve forecasting accuracy most of time. The predictability from OVX to spot volatility is also found for longer forecasting horizons of 5 days and 20 days. The simple GARCH(1,1) and fractionally integrated GARCH with OVX performs significantly better than the other OVX models and all 6 univariate GARCH-type models without OVX. Robustness test results suggest that OVX provides different information from as short-term interest rate.

Use of the AAVSO's International Variable Star Index (VSX) in an Undergraduate Astronomy Course Capstone Project

NASA Astrophysics Data System (ADS)

Larsen, Kristine

2017-06-01

The author discusses a capstone project that utilizes the AAVSO's International Variable Star Index (VSX), ASAS light curves and phase plots, and the SIMBAD astronomical data repository in a laboratory-based undergraduate Stellar and Galactic Astronomy course.

Quantitative predictions of streamflow variability in the Susquehanna River Basin

NASA Astrophysics Data System (ADS)

Alexander, R.; Boyer, E. W.; Leonard, L. N.; Duffy, C.; Schwarz, G. E.; Smith, R. A.

2012-12-01

Hydrologic researchers and water managers have increasingly sought an improved understanding of the major processes that control fluxes of water and solutes across diverse environmental settings and large spatial scales. Regional analyses of observed streamflow data have led to advances in our knowledge of relations among land use, climate, and streamflow, with methodologies ranging from statistical assessments of multiple monitoring sites to the regionalization of the parameters of catchment-scale mechanistic simulation models. However, gaps remain in our understanding of the best ways to transfer the knowledge of hydrologic response and governing processes among locations, including methods for regionalizing streamflow measurements and model predictions. We developed an approach to predict variations in streamflow using the SPARROW (SPAtially Referenced Regression On Watershed attributes) modeling infrastructure, with mechanistic functions, mass conservation constraints, and statistical estimation of regional and sub-regional parameters. We used the model to predict discharge in the Susquehanna River Basin (SRB) under varying hydrological regimes that are representative of contemporary flow conditions. The resulting basin-scale water balance describes mean monthly flows in stream reaches throughout the entire SRB (represented at a 1:100,000 scale using the National Hydrologic Data network), with water supply and demand components that are inclusive of a range of hydrologic, climatic, and cultural properties (e.g., precipitation, evapotranspiration, soil and groundwater storage, runoff, baseflow, water use). We compare alternative models of varying complexity that reflect differences in the number and types of explanatory variables and functional expressions as well as spatial and temporal variability in the model parameters. Statistical estimation of the models reveals the levels of complexity that can be uniquely identified, subject to the information content

Anxiety Sensitivity Index (ASI-3) subscales predict unique variance in anxiety and depressive symptoms.

PubMed

Olthuis, Janine V; Watt, Margo C; Stewart, Sherry H

2014-03-01

Anxiety sensitivity (AS) has been implicated in the development and maintenance of a range of mental health problems. The development of the Anxiety Sensitivity Index - 3, a psychometrically sound index of AS, has provided the opportunity to better understand how the lower-order factors of AS - physical, psychological, and social concerns - are associated with unique forms of psychopathology. The present study investigated these associations among 85 treatment-seeking adults with high AS. Participants completed measures of AS, anxiety, and depression. Multiple regression analyses controlling for other emotional disorder symptoms revealed unique associations between AS subscales and certain types of psychopathology. Only physical concerns predicted unique variance in panic, only cognitive concerns predicted unique variance in depressive symptoms, and social anxiety was predicted by only social concerns. Findings emphasize the importance of considering the multidimensional nature of AS in understanding its role in anxiety and depression and their treatment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Exploration of Machine Learning Approaches to Predict Pavement Performance

DOT National Transportation Integrated Search

2018-03-23

Machine learning (ML) techniques were used to model and predict pavement condition index (PCI) for various pavement types using a variety of input variables. The primary objective of this research was to develop and assess PCI predictive models for t...

Prediction of Cerebral Hyperperfusion Syndrome with Velocity Blood Pressure Index.

PubMed

Lai, Zhi-Chao; Liu, Bao; Chen, Yu; Ni, Leng; Liu, Chang-Wei

2015-06-20

Cerebral hyperperfusion syndrome is an important complication of carotid endarterectomy (CEA). An >100% increase in middle cerebral artery velocity (MCAV) after CEA is used to predict the cerebral hyperperfusion syndrome (CHS) development, but the accuracy is limited. The increase in blood pressure (BP) after surgery is a risk factor of CHS, but no study uses it to predict CHS. This study was to create a more precise parameter for prediction of CHS by combined the increase of MCAV and BP after CEA. Systolic MCAV measured by transcranial Doppler and systematic BP were recorded preoperatively; 30 min postoperatively. The new parameter velocity BP index (VBI) was calculated from the postoperative increase ratios of MCAV and BP. The prediction powers of VBI and the increase ratio of MCAV (velocity ratio [VR]) were compared for predicting CHS occurrence. Totally, 6/185 cases suffered CHS. The best-fit cut-off point of 2.0 for VBI was identified, which had 83.3% sensitivity, 98.3% specificity, 62.5% positive predictive value and 99.4% negative predictive value for CHS development. This result is significantly better than VR (33.3%, 97.2%, 28.6% and 97.8%). The area under the curve (AUC) of receiver operating characteristic: AUC(VBI) = 0.981, 95% confidence interval [CI] 0.949-0.995; AUC(VR) = 0.935, 95% CI 0.890-0.966, P = 0.02. The new parameter VBI can more accurately predict patients at risk of CHS after CEA. This observation needs to be validated by larger studies.

Combining biological and psychosocial baseline variables did not improve prediction of outcome of a very-low-energy diet in a clinic referral population.

PubMed

Sumithran, P; Purcell, K; Kuyruk, S; Proietto, J; Prendergast, L A

2018-02-01

Consistent, strong predictors of obesity treatment outcomes have not been identified. It has been suggested that broadening the range of predictor variables examined may be valuable. We explored methods to predict outcomes of a very-low-energy diet (VLED)-based programme in a clinically comparable setting, using a wide array of pre-intervention biological and psychosocial participant data. A total of 61 women and 39 men (mean ± standard deviation [SD] body mass index: 39.8 ± 7.3 kg/m 2 ) underwent an 8-week VLED and 12-month follow-up. At baseline, participants underwent a blood test and assessment of psychological, social and behavioural factors previously associated with treatment outcomes. Logistic regression, linear discriminant analysis, decision trees and random forests were used to model outcomes from baseline variables. Of the 100 participants, 88 completed the VLED and 42 attended the Week 60 visit. Overall prediction rates for weight loss of ≥10% at weeks 8 and 60, and attrition at Week 60, using combined data were between 77.8 and 87.6% for logistic regression, and lower for other methods. When logistic regression analyses included only baseline demographic and anthropometric variables, prediction rates were 76.2-86.1%. In this population, considering a wide range of biological and psychosocial data did not improve outcome prediction compared to simply-obtained baseline characteristics. © 2017 World Obesity Federation.

Development and validation of classifiers and variable subsets for predicting nursing home admission.

PubMed

Nuutinen, Mikko; Leskelä, Riikka-Leena; Suojalehto, Ella; Tirronen, Anniina; Komssi, Vesa

2017-04-13

In previous years a substantial number of studies have identified statistically important predictors of nursing home admission (NHA). However, as far as we know, the analyses have been done at the population-level. No prior research has analysed the prediction accuracy of a NHA model for individuals. This study is an analysis of 3056 longer-term home care customers in the city of Tampere, Finland. Data were collected from the records of social and health service usage and RAI-HC (Resident Assessment Instrument - Home Care) assessment system during January 2011 and September 2015. The aim was to find out the most efficient variable subsets to predict NHA for individuals and validate the accuracy. The variable subsets of predicting NHA were searched by sequential forward selection (SFS) method, a variable ranking metric and the classifiers of logistic regression (LR), support vector machine (SVM) and Gaussian naive Bayes (GNB). The validation of the results was guaranteed using randomly balanced data sets and cross-validation. The primary performance metrics for the classifiers were the prediction accuracy and AUC (average area under the curve). The LR and GNB classifiers achieved 78% accuracy for predicting NHA. The most important variables were RAI MAPLE (Method for Assigning Priority Levels), functional impairment (RAI IADL, Activities of Daily Living), cognitive impairment (RAI CPS, Cognitive Performance Scale), memory disorders (diagnoses G30-G32 and F00-F03) and the use of community-based health-service and prior hospital use (emergency visits and periods of care). The accuracy of the classifier for individuals was high enough to convince the officials of the city of Tampere to integrate the predictive model based on the findings of this study as a part of home care information system. Further work need to be done to evaluate variables that are modifiable and responsive to interventions.

Airborne fungal spores of Alternaria, meteorological parameters and predicting variables

NASA Astrophysics Data System (ADS)

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

2015-03-01

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

Evaluation of MM5 model resolution when applied to prediction of national fire danger rating indexes

Treesearch

Jeanne L. Hoadley; Miriam L. Rorig; Larry Bradshaw; Sue A. Ferguson; Kenneth J. Westrick; Scott L. Goodrick; Paul Werth

2006-01-01

Weather predictions from the MM5 mesoscale model were used to compute gridded predictions of National Fire Danger Rating System (NFDRS) indexes. The model output was applied to a case study of the 2000 fire season in Northern Idaho and Western Montana to simulate an extreme event. To determine the preferred resolution for automating NFD RS predictions, model...

Exercise heart rate gradient: a novel index to predict all-cause mortality.

PubMed

Duarte, Carlos Vieira; Myers, Jonathan; de Araújo, Claudio Gil Soares

2015-05-01

Although substantial evidence relates reduced exercise heart rate (HR) reserve and recovery to a higher risk of all-cause mortality, a combined indicator of these variables has not been explored. Our aim was to combine HR reserve and recovery into a single index and to assess its utility to predict all-cause mortality. Retrospective cohort analysis. Participants were 1476 subjects (937 males) aged between 41 and 79 years who completed a maximal cycle cardiopulmonary exercise test while not using medication with negative chronotropic effects or having an implantable cardiac pacemaker. HR reserve (HR maximum - HR resting) and recovery (HR maximum - HR at 1-min post exercise) were calculated and divided into quintiles. Quintile rankings were summed yielding an exercise HR gradient (EHRG) ranging from 2 to 10, reflecting the magnitude of on- and off-HR transients to exercise. Survival analyses were undertaken using EHRG scores and HR reserve and recovery in the lowest quintiles (Q1). During a mean follow up of 7.3 years, 44 participants died (3.1%). There was an inverse trend for EHRG scores and death rate (p < 0.05) that increased from 1.2% to 13.5%, respectively, for scores 10 and 2. An EHRG score of 2 was a better predictor of all-cause mortality than either Q1 for HR reserve (<80 bpm) or HR recovery alone (<27 bpm): age-adjusted hazard ratios: 3.53 (p = 0.011), 2.52 (p < 0.05), and 2.57 (p < 0.05), respectively. EHRG, a novel index combining HR reserve and HR recovery, is a better indicator of mortality risk than either response alone. © The European Society of Cardiology 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Impact of tidal density variability on orbital and reentry predictions

NASA Astrophysics Data System (ADS)

Leonard, J. M.; Forbes, J. M.; Born, G. H.

2012-12-01

Since the first satellites entered Earth orbit in the late 1950's and early 1960's, the influences of solar and geomagnetic variability on the satellite drag environment have been studied, and parameterized in empirical density models with increasing sophistication. However, only within the past 5 years has the realization emerged that "troposphere weather" contributes significantly to the "space weather" of the thermosphere, especially during solar minimum conditions. Much of the attendant variability is attributable to upward-propagating solar tides excited by latent heating due to deep tropical convection, and solar radiation absorption primarily by water vapor and ozone in the stratosphere and mesosphere, respectively. We know that this tidal spectrum significantly modifies the orbital (>200 km) and reentry (60-150 km) drag environments, and that these tidal components induce longitude variability not yet emulated in empirical density models. Yet, current requirements for improvements in orbital prediction make clear that further refinements to density models are needed. In this paper, the operational consequences of longitude-dependent tides are quantitatively assessed through a series of orbital and reentry predictions. We find that in-track prediction differences incurred by tidal effects are typically of order 200 ± 100 m for satellites in 400-km circular orbits and 15 ± 10 km for satellites in 200-km circular orbits for a 24-hour prediction. For an initial 200-km circular orbit, surface impact differences of order 15° ± 15° latitude are incurred. For operational problems with similar accuracy needs, a density model that includes a climatological representation of longitude-dependent tides should significantly reduce errors due to this source.

The prediction of nonlinear dynamic loads on helicopters from flight variables using artificial neural networks

NASA Technical Reports Server (NTRS)

Cook, A. B.; Fuller, C. R.; O'Brien, W. F.; Cabell, R. H.

1992-01-01

A method of indirectly monitoring component loads through common flight variables is proposed which requires an accurate model of the underlying nonlinear relationships. An artificial neural network (ANN) model learns relationships through exposure to a database of flight variable records and corresponding load histories from an instrumented military helicopter undergoing standard maneuvers. The ANN model, utilizing eight standard flight variables as inputs, is trained to predict normalized time-varying mean and oscillatory loads on two critical components over a range of seven maneuvers. Both interpolative and extrapolative capabilities are demonstrated with agreement between predicted and measured loads on the order of 90 percent to 95 percent. This work justifies pursuing the ANN method of predicting loads from flight variables.

Tunable two-dimensional liquid gradient refractive index (L-GRIN) lens for variable light focusing.

PubMed

Huang, Hua; Mao, Xiaole; Lin, Sz-Chin Steven; Kiraly, Brian; Huang, Yiping; Huang, Tony Jun

2010-09-21

We report a two-dimensional (2D) tunable liquid gradient refractive index (L-GRIN) lens for variable focusing of light in the out-of-plane direction. This lens focuses a light beam through a liquid medium with a 2D hyperbolic secant (HS) refractive index gradient. The refractive index gradient is established in a microfluidic chamber through the diffusion between two fluids with different refractive indices, i.e. CaCl(2) solution and deionized (DI) water. The 2D HS refractive index profile and subsequently the focal length of the L-GRIN lens can be tuned by changing the ratio of the flow rates of the CaCl(2) solution and DI water. The focusing effect is experimentally characterized through side-view and top-view image analysis, and the experimental data match well with the results from ray-tracing optical simulations. Advantages of the 2D L-GRIN lens include simple device fabrication procedure, low fluid consumption rate, convenient lens-tuning mechanism, and compatibility with existing microfluidic devices. We expect that with further optimizations, this 2D L-GRIN lens can be used in many optics-based lab-on-a-chip applications.

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets.

PubMed

Pyo, Sujin; Lee, Jaewook; Cha, Mincheol; Jang, Huisu

2017-01-01

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.

Use of social adaptability index to explain self-care and diabetes outcomes.

PubMed

Campbell, Jennifer A; Walker, Rebekah J; Smalls, Brittany L; Egede, Leonard E

2017-06-20

To examine whether the social adaptability index (SAI) alone or components of the index provide a better explanatory model for self-care and diabetes outcomes. Six hundred fifteen patients were recruited from two primary care settings. A series of multiple linear regression models were run to assess (1) associations between the SAI and diabetes self-care/outcomes, and (2) associations between individual SAI indicator variables and diabetes self-care/outcomes. Separate models were run for each self-care behavior and outcome. Two models were run for each dependent variable to compare associations with the SAI and components of the index. The SAI has a significant association with the mental component of quality of life (0.23, p < 0.01). In adjusted analyses, the SAI score did not have a significant association with any of the self-care behaviors. Individual components from the index had significant associations between self-care and multiple SAI indicator variables. Significant associations also exist between outcomes and the individual SAI indicators for education and employment. In this population, the SAI has low explanatory power and few significant associations with diabetes self-care/outcomes. While the use of a composite index to predict outcomes within a diabetes population would have high utility, particularly for clinical settings, this SAI lacks statistical and clinical significance in a representative diabetes population. Based on these results, the index does not provide a good model fit and masks the relationship of individual components to diabetes self-care and outcomes. These findings suggest that five items alone are not adequate to explain or predict outcomes for patients with type 2 diabetes.

Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models

NASA Astrophysics Data System (ADS)

Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin

2018-03-01

The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.

Heart Rate Variability Indexes in Dementia: A Systematic Review with a Quantitative Analysis.

PubMed

da Silva, Vanessa Pereira; Ramalho Oliveira, Bruno Ribeiro; Tavares Mello, Roger Gomes; Moraes, Helena; Deslandes, Andrea Camaz; Laks, Jerson

2018-01-01

Decreased heart rate variability (HRV) indexes indicate low vagal activity and may be associated with development of dementia. The neurodegenerative process is associated with the cardiovascular autonomic control. The aim of this systematic review was to investigate the effect size (ES) magnitude of the HRV indexes in the evaluation of autonomic dysfunction in older persons with dementia. PubMed (Medline), Web of Science, Scopus, Scielo, Lilacs, and APA Psycnet were consulted. Complete original articles published in English or Portuguese, investigating the association between autonomic dysfunction and dementia, using the HRV indexes were included. The search identified 97 potentially relevant articles. After screening the full text, eight articles were included in the qualitative analysis and six were included in the quantitative analysis. Almost all indexes showed a negative ES for all types of dementia and mild cognitive impairment. The most common frequency band of the power spectrum density function was the high frequency, which was reported by six studies. The meta-analysis of high frequency power in Alzheimer's disease group showed high heterogeneity and inconsistent results. The negative effect size suggests an autonomic dysfunction in all types of dementia as well as mild cognitive impairment. However, further analysis is necessary to support these results. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

An efficient link prediction index for complex military organization

NASA Astrophysics Data System (ADS)

Fan, Changjun; Liu, Zhong; Lu, Xin; Xiu, Baoxin; Chen, Qing

2017-03-01

Quality of information is crucial for decision-makers to judge the battlefield situations and design the best operation plans, however, real intelligence data are often incomplete and noisy, where missing links prediction methods and spurious links identification algorithms can be applied, if modeling the complex military organization as the complex network where nodes represent functional units and edges denote communication links. Traditional link prediction methods usually work well on homogeneous networks, but few for the heterogeneous ones. And the military network is a typical heterogeneous network, where there are different types of nodes and edges. In this paper, we proposed a combined link prediction index considering both the nodes' types effects and nodes' structural similarities, and demonstrated that it is remarkably superior to all the 25 existing similarity-based methods both in predicting missing links and identifying spurious links in a real military network data; we also investigated the algorithms' robustness under noisy environment, and found the mistaken information is more misleading than incomplete information in military areas, which is different from that in recommendation systems, and our method maintained the best performance under the condition of small noise. Since the real military network intelligence must be carefully checked at first due to its significance, and link prediction methods are just adopted to purify the network with the left latent noise, the method proposed here is applicable in real situations. In the end, as the FINC-E model, here used to describe the complex military organizations, is also suitable to many other social organizations, such as criminal networks, business organizations, etc., thus our method has its prospects in these areas for many tasks, like detecting the underground relationships between terrorists, predicting the potential business markets for decision-makers, and so on.

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

NASA Technical Reports Server (NTRS)

Kimes, Daniel S.; Nelson, Ross F.

1998-01-01

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.

An extended fatty liver index to predict non-alcoholic fatty liver disease.

PubMed

Kantartzis, K; Rettig, I; Staiger, H; Machann, J; Schick, F; Scheja, L; Gastaldelli, A; Bugianesi, E; Peter, A; Schulze, M B; Fritsche, A; Häring, H-U; Stefan, N

2017-06-01

In clinical practice, there is a strong interest in non-invasive markers of non-alcoholic fatty liver disease (NAFLD). Our hypothesis was that the fold-change in plasma triglycerides (TG) during a 2-h oral glucose tolerance test (fold-change TG OGTT ) in concert with blood glucose and lipid parameters, and the rs738409 C>G single nucleotide polymorphism (SNP) in PNPLA3 might improve the power of the widely used fatty liver index (FLI) to predict NAFLD. The liver fat content of 330 subjects was quantified by 1 H-magnetic resonance spectroscopy. Blood parameters were measured during fasting and after a 2-h OGTT. A subgroup of 213 subjects underwent these measurements before and after 9 months of a lifestyle intervention. The fold-change TG OGTT was closely associated with liver fat content (r=0.51, P<0.0001), but had less power to predict NAFLD (AUROC=0.75) than the FLI (AUROC=0.79). Not only was the fold-change TG OGTT independently associated with liver fat content and NAFLD, but so also were the 2-h blood glucose level and rs738409 C>G SNP in PNPLA3. In fact, a novel index (extended FLI) generated from these and the usual FLI parameters considerably increased its power to predict NAFLD (AUROC=0.79-0.86). The extended FLI also increased the power to predict changes in liver fat content with a lifestyle intervention (n=213; standardized beta coefficient: 0.23-0.29). This study has provided novel data confirming that the OGTT-derived fold-change TG OGTT and 2-h glucose level, together with the rs738409 C>G SNP in PNPLA3, allow calculation of an extended FLI that considerably improves its power to predict NAFLD. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

A Comparison of Frailty Indexes for the Prediction of Falls, Disability, Fractures and Mortality in Older Men

PubMed Central

Ensrud, Kristine E.; Ewing, Susan K.; Cawthon, Peggy M.; Fink, Howard A.; Taylor, Brent C.; Cauley, Jane A.; Dam, Thuy-Tien; Marshall, Lynn M.; Orwoll, Eric S.; Cummings, Steven R.

2010-01-01

Objective To compare validity of a parsimonious frailty index (components: weight loss, inability to rise from a chair, and poor energy [SOF index]) with that of the more complex CHS index (components: unintentional weight loss, low grip strength, poor energy, slowness, and low physical activity) for prediction of adverse outcomes in older men. Design Prospective cohort study Setting Six U.S. centers Participants 3132 men ≥67 years Measurements Men classified as robust, intermediate stage or frail using SOF index and criteria similar to those used in CHS index. Falls reported tri-annually for 1 year. Disability (≥1 new impairment in performing IADL) ascertained at 1 year. Fractures and deaths ascertained during 3 years of follow-up. Area under the curve (AUC) statistics from receiver operating characteristic curve analysis compared for models containing SOF index versus CHS index. Results Greater evidence of frailty as defined by either index was associated with increased risks of adverse outcomes. Frail men had a higher age-adjusted risk of recurrent falls (odds ratio [OR] 3.0–3.6), disability (OR 5.3–7.5), nonspine fracture (hazards ratio [HR] 2.2–2.3), and death (HR 2.5–3.5) (P<0.001 for all models). AUC comparisons revealed no differences between models with SOF index versus models with CHS index in discriminating falls (AUC=0.63, P= 0.97), disability (AUC=0.68, P=0.86), nonspine fracture (AUC=0.63, P=0.90), or death (AUC=0.71 for model with SOF index and 0.72 for model with CHS index, P=0.19). Conclusion The simple SOF index predicts risk of falls, disability, fracture and mortality in men as well as the more complex CHS index. PMID:19245414

Artificial neural network for normal, hypertensive, and preeclamptic pregnancy classification using maternal heart rate variability indexes.

PubMed

Tejera, Eduardo; Jose Areias, Maria; Rodrigues, Ana; Ramõa, Ana; Manuel Nieto-Villar, Jose; Rebelo, Irene

2011-09-01

A model construction for classification of women with normal, hypertensive and preeclamptic pregnancy in different gestational ages using maternal heart rate variability (HRV) indexes. In the present work, we applied the artificial neural network for the classification problem, using the signal composed by the time intervals between consecutive RR peaks (RR) (n = 568) obtained from ECG records. Beside the HRV indexes, we also considered other factors like maternal history and blood pressure measurements. The obtained result reveals sensitivity for preeclampsia around 80% that increases for hypertensive and normal pregnancy groups. On the other hand, specificity is around 85-90%. These results indicate that the combination of HRV indexes with artificial neural networks (ANN) could be helpful for pregnancy study and characterization.

Pretest variables that improve the predictive value of exercise testing in women.

PubMed

Lamont, L S; Bobb, J; Blissmer, B; Desai, V

2015-12-01

Graded exercise testing (GXT) is used in coronary artery disease (CAD) prevention and rehabilitation programs. In women, this test has a decreased accuracy and predictive value but there are few studies that examine the predictors of a verified positive test. The aim of this study was to determine those pretest variables that might enhance the predictive value of the GXT in women clients. Medical records of 1761 patients referred for GXT's over a 5 yr period of time were screened. Demographic, medical, and exercise test variables were analyzed. The GXT's of 403 women were available for inclusion and they were stratified into 3 groups: positive responders that were subsequently shown to have CAD (N.=28 verified positive [VP]), positive responders that were not shown to have CAD (N.=84 non-verified positive [NVP]) and negative GXT responders (N.=291). Both univariate and a multivariate step-wise regression statistics were performed on this data. Pretest variables that differentiated between VP and NVP groups are: (an older age=65.8 vs. 60.2 yrs. P<0.05; a greater BMI=30.8 vs. 28.8 kg/m2; diabetes status or an elevated fasting glucose =107.4 vs. 95.2 mg/dL P<0.05; and the use of some cardiovascular medications. Our subsequent linear regression analysis emphasized that HDL cholesterol and beta blocker usage were the most predictive of a positive exercise test in this cohort. The American Heart Association recommends GXT's in women with an intermediate pretest probability of CAD. But there are only two clinical variables available prior to testing to make this probability decision: age and quality of chest pain. This study outlined that other pre-exercise test variables such as: BMI, blood chemistry (glucose and lipoprotein levels) and the use of cardiovascular medications are useful in clinical decision making. These pre-exercise test variables improved the predictive value of the GXT's in our sample.

US Climate Variability and Predictability Project

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

Patterson, Mike

The US CLIVAR Project Office administers the US CLIVAR Program with its mission to advance understanding and prediction of climate variability and change across timescales with an emphasis on the role of the ocean and its interaction with other elements of the Earth system. The Project Office promotes and facilitates scientific collaboration within the US and international climate and Earth science communities, addressing priority topics from subseasonal to centennial climate variability and change; the global energy imbalance; the ocean’s role in climate, water, and carbon cycles; climate and weather extremes; and polar climate changes. This project provides essential one-year supportmore » of the Project Office, enabling the participation of US scientists in the meetings of the US CLIVAR bodies that guide scientific planning and implementation, including the scientific steering committee that establishes program goals and evaluates progress of activities to address them, the science team of funded investigators studying the ocean overturning circulation in the Atlantic, and two working groups tackling the priority research topics of Arctic change influence on midlatitude climate and weather extremes and the decadal-scale widening of the tropical belt.« less

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

PubMed Central

Pyo, Sujin; Lee, Jaewook; Cha, Mincheol

2017-01-01

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction. PMID:29136004

Optical coatings of variable refractive index and high laser-resistance from physical-vapor-deposited perfluorinated amorphous polymer

DOEpatents

Chow, Robert; Loomis, Gary E.; Thomas, Ian M.

1999-01-01

Variable index optical single-layers, optical multilayer, and laser-resistant coatings were made from a perfluorinated amorphous polymer material by physical vapor deposition. This was accomplished by physically vapor depositing a polymer material, such as bulk Teflon AF2400, for example, to form thin layers that have a very low refractive index (.about.1.10-1.31) and are highly transparent from the ultra-violet through the near infrared regime, and maintain the low refractive index of the bulk material. The refractive index can be varied by simply varying one process parameter, either the deposition rate or the substrate temperature. The thus forming coatings may be utilized in anti-reflectors and graded anti-reflection coatings, as well as in optical layers for laser-resistant coatings at optical wavelengths of less than about 2000 nm.

Interobserver variability of sonography for 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

2014-12-01

The sensitivity of sonography to predict accreta has been reported as higher than 90%. However, most studies are from single expert investigators. Our objective was to analyze interobserver variability of sonography for prediction of placenta accreta. Patients with previa with and without accreta were ascertained, and images with placental views were collected, deidentified, and placed in random sequence. Three radiologists and 3 maternal-fetal medicine specialists interpreted each study for the presence of accreta and specific findings reported to be associated with its diagnosis. Investigator-specific sensitivity, specificity, and accuracy were calculated. κ statistics were used to assess variability between individuals and types of investigators. A total of 229 sonographic studies from 55 patients with accreta and 56 control patients were examined. Accuracy ranged from 55.9% to 76.4%. Of imaging studies yielding diagnoses, sensitivity ranged from 53.4% to 74.4%, and specificity ranged from 70.8% to 94.8%. Overall interobserver agreement was moderate (mean κ ± SD = 0.47 ± 0.12). κ values between pairs of investigators ranged from 0.32 (fair agreement) to 0.73 (substantial agreement). Average individual agreement ranged from fair (κ = 0.35) to moderate (κ = 0.53). Blinded from clinical data, sonography has significant interobserver variability for the diagnosis of placenta accreta. © 2013 by the American Institute of Ultrasound in Medicine.

Fatty liver index and hepatic steatosis index for prediction of non-alcoholic fatty liver disease in type 1 diabetes.

PubMed

Sviklāne, Laura; Olmane, Evija; Dzērve, Zane; Kupčs, Kārlis; Pīrāgs, Valdis; Sokolovska, Jeļizaveta

2018-01-01

Little is known about the diagnostic value of hepatic steatosis index (HSI) and fatty liver index (FLI), as well as their link to metabolic syndrome in type 1 diabetes mellitus. We have screened the effectiveness of FLI and HSI in an observational pilot study of 40 patients with type 1 diabetes. FLI and HSI were calculated for 201 patients with type 1 diabetes. Forty patients with FLI/HSI values corresponding to different risk of liver steatosis were invited for liver magnetic resonance study. In-phase/opposed-phase technique of magnetic resonance was used. Accuracy of indices was assessed from the area under the receiver operating characteristic curve. Twelve (30.0%) patients had liver steatosis. For FLI, sensitivity was 90%; specificity, 74%; positive likelihood ratio, 3.46; negative likelihood ratio, 0.14; positive predictive value, 0.64; and negative predictive value, 0.93. For HSI, sensitivity was 86%; specificity, 66%; positive likelihood ratio, 1.95; negative likelihood ratio, 0.21; positive predictive value, 0.50; and negative predictive value, 0.92. Area under the receiver operating characteristic curve for FLI was 0.86 (95% confidence interval [0.72; 0.99]); for HSI 0.75 [0.58; 0.91]. Liver fat correlated with liver enzymes, waist circumference, triglycerides, and C-reactive protein. FLI correlated with C-reactive protein, liver enzymes, and blood pressure. HSI correlated with waist circumference and C-reactive protein. FLI ≥ 60 and HSI ≥ 36 were significantly associated with metabolic syndrome and nephropathy. The tested indices, especially FLI, can serve as surrogate markers for liver fat content and metabolic syndrome in type 1 diabetes. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data.

PubMed

Lee, Kyung Sang; Lee, Hyewon; Myung, Woojae; Song, Gil-Young; Lee, Kihwang; Kim, Ho; Carroll, Bernard J; Kim, Doh Kwan

2018-04-01

Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events.

Predictor variables for a half marathon race time in recreational male runners

PubMed Central

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Rosemann, Thomas

2011-01-01

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the ‘Half Marathon Basel’ completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = −0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r2 = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m2) −3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable. PMID:24198577

Predictor variables for a half marathon race time in recreational male runners.

PubMed

Rüst, Christoph Alexander; Knechtle, Beat; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Rosemann, Thomas

2011-01-01

The aim of this study was to investigate predictor variables of anthropometry, training, and previous experience in order to predict a half marathon race time for future novice recreational male half marathoners. Eighty-four male finishers in the 'Half Marathon Basel' completed the race distance within (mean and standard deviation, SD) 103.9 (16.5) min, running at a speed of 12.7 (1.9) km/h. After multivariate analysis of the anthropometric characteristics, body mass index (r = 0.56), suprailiacal (r = 0.36) and medial calf skin fold (r = 0.53) were related to race time. For the variables of training and previous experience, speed in running of the training sessions (r = -0.54) were associated with race time. After multivariate analysis of both the significant anthropometric and training variables, body mass index (P = 0.0150) and speed in running during training (P = 0.0045) were related to race time. Race time in a half marathon might be partially predicted by the following equation (r(2) = 0.44): Race time (min) = 72.91 + 3.045 * (body mass index, kg/m(2)) -3.884 * (speed in running during training, km/h) for recreational male runners. To conclude, variables of both anthropometry and training were related to half marathon race time in recreational male half marathoners and cannot be reduced to one single predictor variable.

Comparison of Methods for Predicting the Compositional Dependence of the Density and Refractive Index of Organic-Aqueous Aerosols.

PubMed

Cai, Chen; Miles, Rachael E H; Cotterell, Michael I; Marsh, Aleksandra; Rovelli, Grazia; Rickards, Andrew M J; Zhang, Yun-Hong; Reid, Jonathan P

2016-08-25

Representing the physicochemical properties of aerosol particles of complex composition is of crucial importance for understanding and predicting aerosol thermodynamic, kinetic, and optical properties and processes and for interpreting and comparing analysis methods. Here, we consider the representations of the density and refractive index of aqueous-organic aerosol with a particular focus on the dependence of these properties on relative humidity and water content, including an examination of the properties of solution aerosol droplets existing at supersaturated solute concentrations. Using bulk phase measurements of density and refractive index for typical organic aerosol components, we provide robust approaches for the estimation of these properties for aerosol at any intermediate composition between pure water and pure solute. Approximately 70 compounds are considered, including mono-, di- and tricarboxylic acids, alcohols, diols, nitriles, sulfoxides, amides, ethers, sugars, amino acids, aminium sulfates, and polyols. We conclude that the molar refraction mixing rule should be used to predict the refractive index of the solution using a density treatment that assumes ideal mixing or, preferably, a polynomial dependence on the square root of the mass fraction of solute, depending on the solubility limit of the organic component. Although the uncertainties in the density and refractive index predictions depend on the range of subsaturated compositional data available for each compound, typical errors for estimating the solution density and refractive index are less than ±0.1% and ±0.05%, respectively. Owing to the direct connection between molar refraction and the molecular polarizability, along with the availability of group contribution models for predicting molecular polarizability for organic species, our rigorous testing of the molar refraction mixing rule provides a route to predicting refractive indices for aqueous solutions containing organic molecules

Predicting pavement condition index using international roughness index in Washington DC.

DOT National Transportation Integrated Search

2014-09-01

A number of pavement condition indices are used to conduct pavement management assessments, two of which are the : International Roughness Index (IRI) and Pavement Condition Index (PCI). The IRI is typically measured using specialized : equipment tha...

Use of the AAVSO's International Variable Star Index (VSX) in an Undergraduate Astronomy Course Capstone Project (Abstract)

NASA Astrophysics Data System (ADS)

Larsen, K.

2017-12-01

(Abstract only) The author discusses a capstone project that utilizes the AAVSO's International Variable Star Index (VSX), ASAS light curves and phase plots, and the SIMBAD astronomical data repository in a laboratory-based undergraduate Stellar and Galactic Astronomy course.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul

2016-04-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning retrospective predictions at the decadal (5-years), seasonal and sub-seasonal time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and sub-seasonal time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste

The Climate Variability & Predictability (CVP) Program at NOAA - Recent Program Advancements in Understanding AMOC

NASA Astrophysics Data System (ADS)

Lucas, S. E.

2016-12-01

The Climate Variability & Predictability (CVP) Program supports research aimed at providing process-level understanding of the climate system through observation, modeling, analysis, and field studies. This vital knowledge is needed to improve climate models and predictions so that scientists can better anticipate the impacts of future climate variability and change. To achieve its mission, the CVP Program supports research carried out at NOAA and other federal laboratories, NOAA Cooperative Institutes, and academic institutions. The Program also coordinates its sponsored projects with major national and international scientific bodies including the World Climate Research Programme (WCRP), the International and U.S. Climate Variability and Predictability (CLIVAR/US CLIVAR) Program, and the U.S. Global Change Research Program (USGCRP). The CVP program sits within NOAA's Climate Program Office (http://cpo.noaa.gov/CVP). This poster will present the recently funded CVP projects on improving the understanding Atlantic Meridional Overturning Circulation (AMOC), its impact on decadal predictability, and its relationship with the overall climate system.

Mind wandering at the fingertips: automatic parsing of subjective states based on response time variability

PubMed Central

Bastian, Mikaël; Sackur, Jérôme

2013-01-01

Research from the last decade has successfully used two kinds of thought reports in order to assess whether the mind is wandering: random thought-probes and spontaneous reports. However, none of these two methods allows any assessment of the subjective state of the participant between two reports. In this paper, we present a step by step elaboration and testing of a continuous index, based on response time variability within Sustained Attention to Response Tasks (N = 106, for a total of 10 conditions). We first show that increased response time variability predicts mind wandering. We then compute a continuous index of response time variability throughout full experiments and show that the temporal position of a probe relative to the nearest local peak of the continuous index is predictive of mind wandering. This suggests that our index carries information about the subjective state of the subject even when he or she is not probed, and opens the way for on-line tracking of mind wandering. Finally we proceed a step further and infer the internal attentional states on the basis of the variability of response times. To this end we use the Hidden Markov Model framework, which allows us to estimate the durations of on-task and off-task episodes. PMID:24046753

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

PubMed Central

Pothula, Venu M.; Yuan, Stanley C.; Maerz, David A.; Montes, Lucresia; Oleszkiewicz, Stephen M.; Yusupov, Albert; Perline, Richard

2015-01-01

Background Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. Methods Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual. Results Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001). Conclusions ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities. PMID:26710254

Models that predict standing crop of stream fish from habitat variables: 1950-85.

Treesearch

K.D. Fausch; C.L. Hawkes; M.G. Parsons

1988-01-01

We reviewed mathematical models that predict standing crop of stream fish (number or biomass per unit area or length of stream) from measurable habitat variables and classified them by the types of independent habitat variables found significant, by mathematical structure, and by model quality. Habitat variables were of three types and were measured on different scales...

Classification tree models for predicting distributions of michigan stream fish from landscape variables

USGS Publications Warehouse

Steen, P.J.; Zorn, T.G.; Seelbach, P.W.; Schaeffer, J.S.

2008-01-01

Traditionally, fish habitat requirements have been described from local-scale environmental variables. However, recent studies have shown that studying landscape-scale processes improves our understanding of what drives species assemblages and distribution patterns across the landscape. Our goal was to learn more about constraints on the distribution of Michigan stream fish by examining landscape-scale habitat variables. We used classification trees and landscape-scale habitat variables to create and validate presence-absence models and relative abundance models for Michigan stream fishes. We developed 93 presence-absence models that on average were 72% correct in making predictions for an independent data set, and we developed 46 relative abundance models that were 76% correct in making predictions for independent data. The models were used to create statewide predictive distribution and abundance maps that have the potential to be used for a variety of conservation and scientific purposes. ?? Copyright by the American Fisheries Society 2008.

Optical coatings of variable refractive index and high laser-resistance from physical-vapor-deposited perfluorinated amorphous polymer

DOEpatents

Chow, R.; Loomis, G.E.; Thomas, I.M.

1999-03-16

Variable index optical single-layers, optical multilayer, and laser-resistant coatings were made from a perfluorinated amorphous polymer material by physical vapor deposition. This was accomplished by physically vapor depositing a polymer material, such as bulk Teflon AF2400, for example, to form thin layers that have a very low refractive index (ca. 1.10--1.31) and are highly transparent from the ultra-violet through the near infrared regime, and maintain the low refractive index of the bulk material. The refractive index can be varied by simply varying one process parameter, either the deposition rate or the substrate temperature. The thus forming coatings may be utilized in anti-reflectors and graded anti-reflection coatings, as well as in optical layers for laser-resistant coatings at optical wavelengths of less than about 2000 nm. 2 figs.

Emotionally excited eyeblink-rate variability predicts an experience of transportation into the narrative world

PubMed Central

Nomura, Ryota; Hino, Kojun; Shimazu, Makoto; Liang, Yingzong; Okada, Takeshi

2015-01-01

Collective spectator communications such as oral presentations, movies, and storytelling performances are ubiquitous in human culture. This study investigated the effects of past viewing experiences and differences in expressive performance on an audience’s transportive experience into a created world of a storytelling performance. In the experiment, 60 participants (mean age = 34.12 years, SD = 13.18 years, range 18–63 years) were assigned to watch one of two videotaped performances that were played (1) in an orthodox way for frequent viewers and (2) in a modified way aimed at easier comprehension for first-time viewers. Eyeblink synchronization among participants was quantified by employing distance-based measurements of spike trains, Dspike and Dinterval (Victor and Purpura, 1997). The results indicated that even non-familiar participants’ eyeblinks were synchronized as the story progressed and that the effect of the viewing experience on transportation was weak. Rather, the results of a multiple regression analysis demonstrated that the degrees of transportation could be predicted by a retrospectively reported humor experience and higher real-time variability (i.e., logarithmic transformed SD) of inter blink intervals during a performance viewing. The results are discussed from the viewpoint in which the extent of eyeblink synchronization and eyeblink-rate variability acts as an index of the inner experience of audience members. PMID:26029123

The Predictive Power of Electronic Polarizability for Tailoring the Refractivity of High Index Glasses Optical Basicity Versus the Single Oscillator Model

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

McCloy, John S.; Riley, Brian J.; Johnson, Bradley R.

Four compositions of high density (~8 g/cm3) heavy metal oxide glasses composed of PbO, Bi2O3, and Ga2O3 were produced and refractivity parameters (refractive index and density) were computed and measured. Optical basicity was computed using three different models – average electronegativity, ionic-covalent parameter, and energy gap – and the basicity results were used to compute oxygen polarizability and subsequently refractive index. Refractive indices were measured in the visible and infrared at 0.633 μm, 1.55 μm, 3.39 μm, 5.35 μm, 9.29 μm, and 10.59 μm using a unique prism coupler setup, and data were fitted to the Sellmeier expression to obtainmore » an equation of the dispersion of refractive index with wavelength. Using this dispersion relation, single oscillator energy, dispersion energy, and lattice energy were determined. Oscillator parameters were also calculated for the various glasses from their oxide values as an additional means of predicting index. Calculated dispersion parameters from oxides underestimate the index by 3 to 4%. Predicted glass index from optical basicity, based on component oxide energy gaps, underpredicts the index at 0.633 μm by only 2%, while other basicity scales are less accurate. The predicted energy gap of the glasses based on this optical basicity overpredicts the Tauc optical gap as determined by transmission measurements by 6 to 10%. These results show that for this system, density, refractive index in the visible, and energy gap can be reasonably predicted using only composition, optical basicity values for the constituent oxides, and partial molar volume coefficients. Calculations such as these are useful for a priori prediction of optical properties of glasses.« less

Diagnostic accuracy of a mathematical model to predict apnea-hypopnea index using nighttime pulse oximetry

NASA Astrophysics Data System (ADS)

Ebben, Matthew R.; Krieger, Ana C.

2016-03-01

The intent of this study is to develop a predictive model to convert an oxygen desaturation index (ODI) to an apnea-hypopnea index (AHI). This model will then be compared to actual AHI to determine its precision. One thousand four hundred and sixty-seven subjects given polysomnograms with concurrent pulse oximetry between April 14, 2010, and February 7, 2012, were divided into model development (n=733) and verification groups (n=734) in order to develop a predictive model of AHI using ODI. Quadratic regression was used for model development. The coefficient of determination (r2) between the actual AHI and the predicted AHI (PredAHI) was 0.80 (r=0.90), which was significant at a p<0.001. The areas under the receiver operating characteristic curve ranged from 0.96 for AHI thresholds of ≥10 and ≥15/h to 0.97 for thresholds of ≥5 and ≥30/h. The algorithm described in this paper provides a convenient and accurate way to convert ODI to a predicted AHI. This tool makes it easier for clinicians to understand oximetry data in the context of traditional measures of sleep apnea.

Wetland habitat disturbance best predicts metrics of an amphibian index of biotic integrity

USGS Publications Warehouse

Stapanian, Martin A.; Micacchion, Mick; Adams, Jean V.

2015-01-01

Regression and classification trees were used to identify the best predictors of the five component metrics of the Ohio Amphibian Index of Biotic Integrity (AmphIBI) in 54 wetlands in Ohio, USA. Of the 17 wetland- and surrounding landscape-scale variables considered, the best predictor for all AmphIBI metrics was habitat alteration and development within the wetland. The results were qualitatively similar to the best predictors for a wetland vegetation index of biotic integrity, suggesting that similar management practices (e.g., reducing or eliminating nutrient enrichment from agriculture, mowing, grazing, logging, and removing down woody debris) within the boundaries of the wetland can be applied to effectively increase the quality of wetland vegetation and amphibian communities.

Brain Signal Variability is Parametrically Modifiable

PubMed Central

Garrett, Douglas D.; McIntosh, Anthony R.; Grady, Cheryl L.

2014-01-01

Moment-to-moment brain signal variability is a ubiquitous neural characteristic, yet remains poorly understood. Evidence indicates that heightened signal variability can index and aid efficient neural function, but it is not known whether signal variability responds to precise levels of environmental demand, or instead whether variability is relatively static. Using multivariate modeling of functional magnetic resonance imaging-based parametric face processing data, we show here that within-person signal variability level responds to incremental adjustments in task difficulty, in a manner entirely distinct from results produced by examining mean brain signals. Using mixed modeling, we also linked parametric modulations in signal variability with modulations in task performance. We found that difficulty-related reductions in signal variability predicted reduced accuracy and longer reaction times within-person; mean signal changes were not predictive. We further probed the various differences between signal variance and signal means by examining all voxels, subjects, and conditions; this analysis of over 2 million data points failed to reveal any notable relations between voxel variances and means. Our results suggest that brain signal variability provides a systematic task-driven signal of interest from which we can understand the dynamic function of the human brain, and in a way that mean signals cannot capture. PMID:23749875

Emotion Awareness Predicts Body Mass Index Percentile Trajectories in Youth.

PubMed

Whalen, Diana J; Belden, Andy C; Barch, Deanna; Luby, Joan

2015-10-01

To examine the rate of change in body mass index (BMI) percentile across 3 years in relation to emotion identification ability and brain-based reactivity in emotional processing regions. A longitudinal sample of 202 youths completed 3 functional magnetic resonance imaging-based facial processing tasks and behavioral emotion differentiation tasks. We examined the rate of change in the youth's BMI percentile as a function of reactivity in emotional processing brain regions and behavioral emotion identification tasks using multilevel modeling. Lower correct identification of both happiness and sadness measured behaviorally predicted increases in BMI percentile across development, whereas higher correct identification of both happiness and sadness predicted decreases in BMI percentile, while controlling for children's pubertal status, sex, ethnicity, IQ score, exposure to antipsychotic medication, family income-to-needs ratio, and externalizing, internalizing, and depressive symptoms. Greater neural activation in emotional reactivity regions to sad faces also predicted increases in BMI percentile during development, also controlling for the aforementioned covariates. Our findings provide longitudinal developmental data demonstrating links between both emotion identification ability and greater neural reactivity in emotional processing regions with trajectories of BMI percentiles across childhood. Copyright © 2015 Elsevier Inc. All rights reserved.

Variability and predictability of decadal mean temperature and precipitation over China in the CCSM4 last millennium simulation

NASA Astrophysics Data System (ADS)

Ying, Kairan; Frederiksen, Carsten S.; Zheng, Xiaogu; Lou, Jiale; Zhao, Tianbao

2018-02-01

The modes of variability that arise from the slow-decadal (potentially predictable) and intra-decadal (unpredictable) components of decadal mean temperature and precipitation over China are examined, in a 1000 year (850-1850 AD) experiment using the CCSM4 model. Solar variations, volcanic aerosols, orbital forcing, land use, and greenhouse gas concentrations provide the main forcing and boundary conditions. The analysis is done using a decadal variance decomposition method that identifies sources of potential decadal predictability and uncertainty. The average potential decadal predictabilities (ratio of slow-to-total decadal variance) are 0.62 and 0.37 for the temperature and rainfall over China, respectively, indicating that the (multi-)decadal variations of temperature are dominated by slow-decadal variability, while precipitation is dominated by unpredictable decadal noise. Possible sources of decadal predictability for the two leading predictable modes of temperature are the external radiative forcing, and the combined effects of slow-decadal variability of the Arctic oscillation (AO) and the Pacific decadal oscillation (PDO), respectively. Combined AO and PDO slow-decadal variability is associated also with the leading predictable mode of precipitation. External radiative forcing as well as the slow-decadal variability of PDO are associated with the second predictable rainfall mode; the slow-decadal variability of Atlantic multi-decadal oscillation (AMO) is associated with the third predictable precipitation mode. The dominant unpredictable decadal modes are associated with intra-decadal/inter-annual phenomena. In particular, the El Niño-Southern Oscillation and the intra-decadal variability of the AMO, PDO and AO are the most important sources of prediction uncertainty.

Identification of cognitive and non-cognitive predictive variables related to attrition in baccalaureate nursing education programs in Mississippi

NASA Astrophysics Data System (ADS)

Hayes, Catherine

2005-07-01

This study sought to identify a variable or variables predictive of attrition among baccalaureate nursing students. The study was quantitative in design and multivariate correlational statistics and discriminant statistical analysis were used to identify a model for prediction of attrition. The analysis then weighted variables according to their predictive value to determine the most parsimonious model with the greatest predictive value. Three public university nursing education programs in Mississippi offering a Bachelors Degree in Nursing were selected for the study. The population consisted of students accepted and enrolled in these three programs for the years 2001 and 2002 and graduating in the years 2003 and 2004 (N = 195). The categorical dependent variable was attrition (includes academic failure or withdrawal) from the program of nursing education. The ten independent variables selected for the study and considered to have possible predictive value were: Grade Point Average for Pre-requisite Course Work; ACT Composite Score, ACT Reading Subscore, and ACT Mathematics Subscore; Letter Grades in the Courses: Anatomy & Physiology and Lab I, Algebra I, English I (101), Chemistry & Lab I, and Microbiology & Lab I; and Number of Institutions Attended (Universities, Colleges, Junior Colleges or Community Colleges). Descriptive analysis was performed and the means of each of the ten independent variables was compared for students who attrited and those who were retained in the population. The discriminant statistical analysis performed created a matrix using the ten variable model that was able to correctly predicted attrition in the study's population in 77.6% of the cases. Variables were then combined and recombined to produce the most efficient and parsimonious model for prediction. A six variable model resulted which weighted each variable according to predictive value: GPA for Prerequisite Coursework, ACT Composite, English I, Chemistry & Lab I, Microbiology

Interannual Variability, Global Teleconnection, and Potential Predictability Associated with the Asian Summer Monsoon

NASA Technical Reports Server (NTRS)

Lau, K. M.; Kim, K. M.; Li, J. Y.

2001-01-01

In this Chapter, aspects of global teleconnections associated with the interannual variability of the Asian summer monsoon (ASM) are discussed. The basic differences in the basic dynamics of the South Asian Monsoon and the East Asian monsoon, and their implications on global linkages are discussed. Two teleconnection modes linking ASM variability to summertime precipitation over the continental North America were identified. These modes link regional circulation and precipitation anomalies over East Asia and continental North America, via coupled atmosphere-ocean variations over the North Pacific. The first mode has a large zonally symmetrical component and appears to be associated with subtropical jetstream variability and the second mode with Rossby wave dispersion. Both modes possess strong sea surface temperature (SST) expressions in the North Pacific. Results show that the two teleconnection modes may have its origin in intrinsic modes of sea surface temperature variability in the extratropical oceans, which are forced in part by atmospheric variability and in part by air-sea interaction. The potential predictability of the ASM associated with SST variability in different ocean basins is explored using a new canonical ensemble correlation prediction scheme. It is found that SST anomalies in tropical Pacific, i.e., El Nino, is the most dominant forcing for the ASM, especially over the maritime continent and eastern Australia. SST anomalies in the India Ocean may trump the influence from El Nino in western Australia and western maritime continent. Both El Nino, and North Pacific SSTs contribute to monsoon precipitation anomalies over Japan, southern Korea, northern and central China. By optimizing SST variability signals from the world ocean basins using CEC, the overall predictability of ASM can be substantially improved.

A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures

PubMed Central

Chen, Yun; Yang, Hui

2016-01-01

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering. PMID:27966581

A Novel Information-Theoretic Approach for Variable Clustering and Predictive Modeling Using Dirichlet Process Mixtures.

PubMed

Chen, Yun; Yang, Hui

2016-12-14

In the era of big data, there are increasing interests on clustering variables for the minimization of data redundancy and the maximization of variable relevancy. Existing clustering methods, however, depend on nontrivial assumptions about the data structure. Note that nonlinear interdependence among variables poses significant challenges on the traditional framework of predictive modeling. In the present work, we reformulate the problem of variable clustering from an information theoretic perspective that does not require the assumption of data structure for the identification of nonlinear interdependence among variables. Specifically, we propose the use of mutual information to characterize and measure nonlinear correlation structures among variables. Further, we develop Dirichlet process (DP) models to cluster variables based on the mutual-information measures among variables. Finally, orthonormalized variables in each cluster are integrated with group elastic-net model to improve the performance of predictive modeling. Both simulation and real-world case studies showed that the proposed methodology not only effectively reveals the nonlinear interdependence structures among variables but also outperforms traditional variable clustering algorithms such as hierarchical clustering.

Predicting active-layer soil thickness using topographic variables at a small watershed scale

PubMed Central

Li, Aidi; Tan, Xing; Wu, Wei; Liu, Hongbin; Zhu, Jie

2017-01-01

Knowledge about the spatial distribution of active-layer (AL) soil thickness is indispensable for ecological modeling, precision agriculture, and land resource management. However, it is difficult to obtain the details on AL soil thickness by using conventional soil survey method. In this research, the objective is to investigate the possibility and accuracy of mapping the spatial distribution of AL soil thickness through random forest (RF) model by using terrain variables at a small watershed scale. A total of 1113 soil samples collected from the slope fields were randomly divided into calibration (770 soil samples) and validation (343 soil samples) sets. Seven terrain variables including elevation, aspect, relative slope position, valley depth, flow path length, slope height, and topographic wetness index were derived from a digital elevation map (30 m). The RF model was compared with multiple linear regression (MLR), geographically weighted regression (GWR) and support vector machines (SVM) approaches based on the validation set. Model performance was evaluated by precision criteria of mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Comparative results showed that RF outperformed MLR, GWR and SVM models. The RF gave better values of ME (0.39 cm), MAE (7.09 cm), and RMSE (10.85 cm) and higher R2 (62%). The sensitivity analysis demonstrated that the DEM had less uncertainty than the AL soil thickness. The outcome of the RF model indicated that elevation, flow path length and valley depth were the most important factors affecting the AL soil thickness variability across the watershed. These results demonstrated the RF model is a promising method for predicting spatial distribution of AL soil thickness using terrain parameters. PMID:28877196

Global scale variability of the mineral dust longwave refractive index from laboratory chamber experiments: re‒evaluation of its direct radiative effect

NASA Astrophysics Data System (ADS)

Di Biagio, C.; Formenti, P.; Balkanski, Y.; Caponi, L.; Cazaunau, M.; Pangui, E.; Journet, E.; Nowak, S.; Caquineau, S.; Andreae, M. O.; Kandler, K.; Saeed, T.; Piketh, S.; Seibert, D.; Williams, E.; Boucher, O.; Doussin, J. F.

2017-12-01

New measurements of the longwave complex refractive index (LW CRI) of mineral dust and its global variability were obtained in situ in the 4.2 m3CESAM simulation chamber at LISA (Laboratoire Interuniversitaire des Systemes Atmospheriques) in Créteil, France. Aerosols generated by mechanical shaking from nineteen natural soils with contrasted mineralogical composition were suspended in the chamber, where their LW extinction spectra (2-16 μm), size distribution, and mineralogical composition were measured. The CRI of the dust aerosol was obtained by optical calculations based upon the measured extinction spectrum and size distribution. Laboratory results indicate that the LW refractive index of dust strongly varies with the source region of emission in link with the changes of its mineralogy. In the 2-16 μm spectral range, the imaginary refractive index (k) is between 0.001 and 0.92, and the real part (n) in the range 0.84-1.94. The strength of the dust absorption at 7 and 11.4 µm depends on the amount of calcite within the samples, while the absorption between 8 and 14 µm is determined by the relative abundance of quartz and clays. A linear relationship between the magnitude of k at 7, 9.2, and 11.4 µm and the mass concentration of calcite and quartz absorbing at these wavelengths was found, which suggests that predictive rules could be established to estimate the LW refractive index of dust in specific bands based on an assumed or predicted mineralogical composition. Our observations also suggest that the LW CRI of dust does not change as a result of the loss of coarse particles by gravitational settling, so that a constant value can be assumed close to sources and following transport. This unprecedented dataset of refractive indices was used as input into the LMDZORINCA model coupled with the RRTM radiative transfer module in order to re‒evaluate the direct dust LW radiative effect. This represents a first attempt to use regional‒dependent values of the

[The influence of individually fitted controlled breathing frequency on the heart rate variability indexes].

PubMed

Chuian, O M; Biriukova, O O; Ravaieva, M Iu

2010-01-01

We studied the changes in indexes of variability of heart rate and fractal neurodynamics under conditions of controlled breathing on fluctuation frequency of a spectrum of heart rate. It is shown that the controlled breathing, which frequency corresponds to a frequency of localization of the maximum peak of capacity ofa heart rate in low-frequency is a powerful mechanism of management of heart rate and change of a functional condition of an organism as a whole.

A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe.

PubMed

Ritenberga, Olga; Sofiev, Mikhail; Siljamo, Pilvi; Saarto, Annika; Dahl, Aslog; Ekebom, Agneta; Sauliene, Ingrida; Shalaboda, Valentina; Severova, Elena; Hoebeke, Lucie; Ramfjord, Hallvard

2018-02-15

The paper suggests a methodology for predicting next-year seasonal pollen index (SPI, a sum of daily-mean pollen concentrations) over large regions and demonstrates its performance for birch in Northern and North-Eastern Europe. A statistical model is constructed using meteorological, geophysical and biological characteristics of the previous year). A cluster analysis of multi-annual data of European Aeroallergen Network (EAN) revealed several large regions in Europe, where the observed SPI exhibits similar patterns of the multi-annual variability. We built the model for the northern cluster of stations, which covers Finland, Sweden, Baltic States, part of Belarus, and, probably, Russia and Norway, where the lack of data did not allow for conclusive analysis. The constructed model was capable of predicting the SPI with correlation coefficient reaching up to 0.9 for some stations, odds ratio is infinitely high for 50% of sites inside the region and the fraction of prediction falling within factor of 2 from observations, stays within 40-70%. In particular, model successfully reproduced both the bi-annual cycle of the SPI and years when this cycle breaks down. Copyright © 2017 Elsevier B.V. All rights reserved.

Significance of a Behavioral Economic Index of Reward Value in Predicting Drinking Problem Resolution

ERIC Educational Resources Information Center

Tucker, Jalie A.; Vuchinich, Rudy E.; Black, Bethany C.; Rippens, Paula D.

2006-01-01

This study investigated whether a behavioral economic index of the value of rewards available over different time horizons improved prediction of drinking outcomes beyond established biopsychosocial predictors. Preferences for immediate drinking versus more delayed rewards made possible by saving money were determined from expenditures prior to…

Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia

NASA Astrophysics Data System (ADS)

Deo, Ravinesh C.; Şahin, Mehmet

2015-07-01

The forecasting of drought based on cumulative influence of rainfall, temperature and evaporation is greatly beneficial for mitigating adverse consequences on water-sensitive sectors such as agriculture, ecosystems, wildlife, tourism, recreation, crop health and hydrologic engineering. Predictive models of drought indices help in assessing water scarcity situations, drought identification and severity characterization. In this paper, we tested the feasibility of the Artificial Neural Network (ANN) as a data-driven model for predicting the monthly Standardized Precipitation and Evapotranspiration Index (SPEI) for eight candidate stations in eastern Australia using predictive variable data from 1915 to 2005 (training) and simulated data for the period 2006-2012. The predictive variables were: monthly rainfall totals, mean temperature, minimum temperature, maximum temperature and evapotranspiration, which were supplemented by large-scale climate indices (Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and Indian Ocean Dipole) and the Sea Surface Temperatures (Nino 3.0, 3.4 and 4.0). A total of 30 ANN models were developed with 3-layer ANN networks. To determine the best combination of learning algorithms, hidden transfer and output functions of the optimum model, the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton backpropagation algorithms were utilized to train the network, tangent and logarithmic sigmoid equations used as the activation functions and the linear, logarithmic and tangent sigmoid equations used as the output function. The best ANN architecture had 18 input neurons, 43 hidden neurons and 1 output neuron, trained using the Levenberg-Marquardt learning algorithm using tangent sigmoid equation as the activation and output functions. An evaluation of the model performance based on statistical rules yielded time-averaged Coefficient of Determination, Root Mean Squared Error and the Mean Absolute

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability.

PubMed

Wu, Howard G; Miyamoto, Yohsuke R; Gonzalez Castro, Luis Nicolas; Ölveczky, Bence P; Smith, Maurice A

2014-02-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning.

Temporal structure of motor variability is dynamically regulated and predicts motor learning ability

PubMed Central

Wu, Howard G; Miyamoto, Yohsuke R; Castro, Luis Nicolas Gonzalez; Ölveczky, Bence P; Smith, Maurice A

2015-01-01

Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning. PMID:24413700

Variables Predicting Prospective Biology Teachers' Acceptance Perceptions Regarding Gene Technology

ERIC Educational Resources Information Center

Yilmaz, Mirac; Demirhan, Haydar

2014-01-01

The different opinions on products and applications of gene technology (GT) draw attention to the training and education activities related to GT. The purpose of this study is to review some variables predicting the acceptance perception regarding GT, and to investigate their changes at levels. The prospective teachers' subjective knowledge and…

Advanced Daily Prediction Model for National Suicide Numbers with Social Media Data

PubMed Central

Lee, Kyung Sang; Lee, Hyewon; Myung, Woojae; Song, Gil-Young; Lee, Kihwang; Kim, Ho; Carroll, Bernard J.; Kim, Doh Kwan

2018-01-01

Objective Suicide is a significant public health concern worldwide. Social media data have a potential role in identifying high suicide risk individuals and also in predicting suicide rate at the population level. In this study, we report an advanced daily suicide prediction model using social media data combined with economic/meteorological variables along with observed suicide data lagged by 1 week. Methods The social media data were drawn from weblog posts. We examined a total of 10,035 social media keywords for suicide prediction. We made predictions of national suicide numbers 7 days in advance daily for 2 years, based on a daily moving 5-year prediction modeling period. Results Our model predicted the likely range of daily national suicide numbers with 82.9% accuracy. Among the social media variables, words denoting economic issues and mood status showed high predictive strength. Observed number of suicides one week previously, recent celebrity suicide, and day of week followed by stock index, consumer price index, and sunlight duration 7 days before the target date were notable predictors along with the social media variables. Conclusion These results strengthen the case for social media data to supplement classical social/economic/climatic data in forecasting national suicide events. PMID:29614852

Prediction of Core Body Temperature from Multiple Variables.

PubMed

Richmond, Victoria L; Davey, Sarah; Griggs, Katy; Havenith, George

2015-11-01

This paper aims to improve the prediction of rectal temperature (T re) from insulated skin temperature (T is) and micro-climate temperature (T mc) previously reported (Richmond et al., Insulated skin temperature as a measure of core body temperature for individuals wearing CBRN protective clothing. Physiol Meas 2013; 34:1531-43.) using additional physiological and/or environmental variables, under several clothing and climatic conditions. Twelve male (25.8±5.1 years; 73.6±11.5kg; 178±6cm) and nine female (24.2±5.1 years; 62.4±11.5kg; 169±3cm) volunteers completed six trials, each consisting of two 40-min periods of treadmill walking separated by a 20-min rest, wearing permeable or impermeable clothing, under neutral (25°C, 50%), moderate (35°C, 35%), and hot (40°C, 25%) conditions, with and without solar radiation (600W m(-2)). Participants were measured for heart rate (HR) (Polar, Finland), skin temperature (T s) at 11 sites, T is (Grant, Cambridge, UK), and breathing rate (f) (Hidalgo, Cambridge, UK). T mc and relative humidity were measured within the clothing. T re was monitored as the 'gold standard' measure of T c for industrial or military applications using a 10cm flexible probe (Grant, Cambridge, UK). A stepwise multiple regression analysis was run to determine which of 30 variables (T is, T s at 11 sites, HR, f, T mc, temperature, and humidity inside the clothing front and back, body mass, age, body fat, sex, clothing, Thermal comfort, sensation and perception, and sweat rate) were the strongest on which to base the model. Using a bootstrap methodology to develop the equation, the best model in terms of practicality and validity included T is, T mc, HR, and 'work' (0 = rest; 1 = exercise), predicting T re with a standard error of the estimate of 0.27°C and adjusted r (2) of 0.86. The sensitivity and specificity for predicting individuals who reached 39°C was 97 and 85%, respectively. Insulated skin temperature was the most important individual

Surgical Risk Preoperative Assessment System (SURPAS): II. Parsimonious Risk Models for Postoperative Adverse Outcomes Addressing Need for Laboratory Variables and Surgeon Specialty-specific Models.

PubMed

Meguid, Robert A; Bronsert, Michael R; Juarez-Colunga, Elizabeth; Hammermeister, Karl E; Henderson, William G

2016-07-01

To develop parsimonious prediction models for postoperative mortality, overall morbidity, and 6 complication clusters applicable to a broad range of surgical operations in adult patients. Quantitative risk assessment tools are not routinely used for preoperative patient assessment, shared decision making, informed consent, and preoperative patient optimization, likely due in part to the burden of data collection and the complexity of incorporation into routine surgical practice. Multivariable forward selection stepwise logistic regression analyses were used to develop predictive models for 30-day mortality, overall morbidity, and 6 postoperative complication clusters, using 40 preoperative variables from 2,275,240 surgical cases in the American College of Surgeons National Surgical Quality Improvement Program data set, 2005 to 2012. For the mortality and overall morbidity outcomes, prediction models were compared with and without preoperative laboratory variables, and generic models (based on all of the data from 9 surgical specialties) were compared with specialty-specific models. In each model, the cumulative c-index was used to examine the contribution of each added predictor variable. C-indexes, Hosmer-Lemeshow analyses, and Brier scores were used to compare discrimination and calibration between models. For the mortality and overall morbidity outcomes, the prediction models without the preoperative laboratory variables performed as well as the models with the laboratory variables, and the generic models performed as well as the specialty-specific models. The c-indexes were 0.938 for mortality, 0.810 for overall morbidity, and for the 6 complication clusters ranged from 0.757 for infectious to 0.897 for pulmonary complications. Across the 8 prediction models, the first 7 to 11 variables entered accounted for at least 99% of the c-index of the full model (using up to 28 nonlaboratory predictor variables). Our results suggest that it will be possible to develop

Development of a risk index for prediction of abnormal pap test results in Serbia.

PubMed

Vukovic, Dejana; Antic, Ljiljana; Vasiljevic, Mladenko; Antic, Dragan; Matejic, Bojana

2015-01-01

Serbia is one of the countries with highest incidence and mortality rates for cervical cancer in Central and South Eastern Europe. Introducing a risk index could provide a powerful means for targeting groups at high likelihood of having an abnormal cervical smear and increase efficiency of screening. The aim of the present study was to create and assess validity ofa index for prediction of an abnormal Pap test result. The study population was drawn from patients attending Departments for Women's Health in two primary health care centers in Serbia. Out of 525 respondents 350 were randomly selected and data obtained from them were used as the index creation dataset. Data obtained from the remaining 175 were used as an index validation data set. Age at first intercourse under 18, more than 4 sexual partners, history of STD and multiparity were attributed statistical weights 16, 15, 14 and 13, respectively. The distribution of index scores in index-creation data set showed that most respondents had a score 0 (54.9%). In the index-creation dataset mean index score was 10.3 (SD-13.8), and in the validation dataset the mean was 9.1 (SD=13.2). The advantage of such scoring system is that it is simple, consisting of only four elements, so it could be applied to identify women with high risk for cervical cancer that would be referred for further examination.

Predicting suicidal ideation in primary care: An approach to identify easily assessable key variables.

PubMed

Jordan, Pascal; Shedden-Mora, Meike C; Löwe, Bernd

To obtain predictors of suicidal ideation, which can also be used for an indirect assessment of suicidal ideation (SI). To create a classifier for SI based on variables of the Patient Health Questionnaire (PHQ) and sociodemographic variables, and to obtain an upper bound on the best possible performance of a predictor based on those variables. From a consecutive sample of 9025 primary care patients, 6805 eligible patients (60% female; mean age = 51.5 years) participated. Advanced methods of machine learning were used to derive the prediction equation. Various classifiers were applied and the area under the curve (AUC) was computed as a performance measure. Classifiers based on methods of machine learning outperformed ordinary regression methods and achieved AUCs around 0.87. The key variables in the prediction equation comprised four items - namely feelings of depression/hopelessness, low self-esteem, worrying, and severe sleep disturbances. The generalized anxiety disorder scale (GAD-7) and the somatic symptom subscale (PHQ-15) did not enhance prediction substantially. In predicting suicidal ideation researchers should refrain from using ordinary regression tools. The relevant information is primarily captured by the depression subscale and should be incorporated in a nonlinear model. For clinical practice, a classification tree using only four items of the whole PHQ may be advocated. Copyright © 2018 Elsevier Inc. All rights reserved.

Frailty Index Predicts All-Cause Mortality for Middle-Aged and Older Taiwanese: Implications for Active-Aging Programs.

PubMed

Lin, Shu-Yu; Lee, Wei-Ju; Chou, Ming-Yueh; Peng, Li-Ning; Chiou, Shu-Ti; Chen, Liang-Kung

2016-01-01

Frailty Index, defined as an individual's accumulated proportion of listed health-related deficits, is a well-established metric used to assess the health status of old adults; however, it has not yet been developed in Taiwan, and its local related structure factors remain unclear. The objectives were to construct a Taiwan Frailty Index to predict mortality risk, and to explore the structure of its factors. Analytic data on 1,284 participants aged 53 and older were excerpted from the Social Environment and Biomarkers of Aging Study (2006), in Taiwan. A consensus workgroup of geriatricians selected 159 items according to the standard procedure for creating a Frailty Index. Cox proportional hazard modeling was used to explore the association between the Taiwan Frailty Index and mortality. Exploratory factor analysis was used to identify structure factors and produce a shorter version-the Taiwan Frailty Index Short-Form. During an average follow-up of 4.3 ± 0.8 years, 140 (11%) subjects died. Compared to those in the lowest Taiwan Frailty Index tertile (< 0.18), those in the uppermost tertile (> 0.23) had significantly higher risk of death (Hazard ratio: 3.2; 95% CI 1.9-5.4). Thirty-five items of five structure factors identified by exploratory factor analysis, included: physical activities, life satisfaction and financial status, health status, cognitive function, and stresses. Area under the receiver operating characteristic curves (C-statistics) of the Taiwan Frailty Index and its Short-Form were 0.80 and 0.78, respectively, with no statistically significant difference between them. Although both the Taiwan Frailty Index and Short-Form were associated with mortality, the Short-Form, which had similar accuracy in predicting mortality as the full Taiwan Frailty Index, would be more expedient in clinical practice and community settings to target frailty screening and intervention.

MEDEX 2015: Heart Rate Variability Predicts Development of Acute Mountain Sickness.

PubMed

Sutherland, Angus; Freer, Joseph; Evans, Laura; Dolci, Alberto; Crotti, Matteo; Macdonald, Jamie Hugo

2017-09-01

Sutherland, Angus, Joseph Freer, Laura Evans, Alberto Dolci, Matteo Crotti, and Jamie Hugo Macdonald. MEDEX 2015: Heart rate variability predicts development of acute mountain sickness. High Alt Med Biol. 18: 199-208, 2017. Acute mountain sickness (AMS) develops when the body fails to acclimatize to atmospheric changes at altitude. Preascent prediction of susceptibility to AMS would be a useful tool to prevent subsequent harm. Changes to peripheral oxygen saturation (SpO 2 ) on hypoxic exposure have previously been shown to be of poor predictive value. Heart rate variability (HRV) has shown promise in the early prediction of AMS, but its use pre-expedition has not previously been investigated. We aimed to determine whether pre- and intraexpedition HRV assessment could predict susceptibility to AMS at high altitude with better diagnostic accuracy than SpO 2 . Forty-four healthy volunteers undertook an expedition in the Nepali Himalaya to >5000 m. SpO 2 and HRV parameters were recorded at rest in normoxia and in a normobaric hypoxic chamber before the expedition. On the expedition HRV parameters and SpO 2 were collected again at 3841 m. A daily Lake Louise Score was obtained to assess AMS symptomology. Low frequency/high frequency (LF/HF) ratio in normoxia (cutpoint ≤2.28 a.u.) and LF following 15 minutes of exposure to normobaric hypoxia had moderate (area under the curve ≥0.8) diagnostic accuracy. LF/HF ratio in normoxia had the highest sensitivity (85%) and specificity (88%) for predicting AMS on subsequent ascent to altitude. In contrast, pre-expedition SpO 2 measurements had poor (area under the curve <0.7) diagnostic accuracy and inferior sensitivity and specificity. Pre-ascent measurement of HRV in normoxia was found to be of better diagnostic accuracy for AMS prediction than all measures of HRV in hypoxia, and better than peripheral oxygen saturation monitoring.

Index for Predicting Insurance Claims from Wind Storms with an Application in France.

PubMed

Mornet, Alexandre; Opitz, Thomas; Luzi, Michel; Loisel, Stéphane

2015-11-01

For insurance companies, wind storms represent a main source of volatility, leading to potentially huge aggregated claim amounts. In this article, we compare different constructions of a storm index allowing us to assess the economic impact of storms on an insurance portfolio by exploiting information from historical wind speed data. Contrary to historical insurance portfolio data, meteorological variables show fewer nonstationarities between years and are easily available with long observation records; hence, they represent a valuable source of additional information for insurers if the relation between observations of claims and wind speeds can be revealed. Since standard correlation measures between raw wind speeds and insurance claims are weak, a storm index focusing on high wind speeds can afford better information. A storm index approach has been applied to yearly aggregated claim amounts in Germany with promising results. Using historical meteorological and insurance data, we assess the consistency of the proposed index constructions with respect to various parameters and weights. Moreover, we are able to place the major insurance events since 1998 on a broader horizon beyond 40 years. Our approach provides a meteorological justification for calculating the return periods of extreme-storm-related insurance events whose magnitude has rarely been reached. © 2015 Society for Risk Analysis.

Effect of Spatio-Temporal Variability of Rainfall on Stream flow Prediction of Birr Watershed

NASA Astrophysics Data System (ADS)

Demisse, N. S.; Bitew, M. M.; Gebremichael, M.

2012-12-01

The effect of rainfall variability on our ability to forecast flooding events was poorly studied in complex terrain region of Ethiopia. In order to establish relation between rainfall variability and stream flow, we deployed 24 rain gauges across Birr watershed. Birr watershed is a medium size mountainous watershed with an area of 3000 km2 and elevation ranging between 1435 m.a.s.l and 3400 m.a.s.l in the central Ethiopia highlands. One summer monsoon rainfall of 2012 recorded at high temporal scale of 15 minutes interval and stream flow recorded at an hourly interval in three sub-watershed locations representing different scales were used in this study. Based on the data obtained from the rain gauges and stream flow observations, we quantify extent of temporal and spatial variability of rainfall across the watershed using standard statistical measures including mean, standard deviation and coefficient of variation. We also establish rainfall-runoff modeling system using a physically distributed hydrological model: the Soil and Water Assessment Tool (SWAT) and examine the effect of rainfall variability on stream flow prediction. The accuracy of predicted stream flow is measured through direct comparison with observed flooding events. The results demonstrate the significance of relation between stream flow prediction and rainfall variability in the understanding of runoff generation mechanisms at watershed scale, determination of dominant water balance components, and effect of variability on accuracy of flood forecasting activities.

24-Hour Blood Pressure Variability Assessed by Average Real Variability: A Systematic Review and Meta-Analysis.

PubMed

Mena, Luis J; Felix, Vanessa G; Melgarejo, Jesus D; Maestre, Gladys E

2017-10-19

Although 24-hour blood pressure (BP) variability (BPV) is predictive of cardiovascular outcomes independent of absolute BP levels, it is not regularly assessed in clinical practice. One possible limitation to routine BPV assessment is the lack of standardized methods for accurately estimating 24-hour BPV. We conducted a systematic review to assess the predictive power of reported BPV indexes to address appropriate quantification of 24-hour BPV, including the average real variability (ARV) index. Studies chosen for review were those that presented data for 24-hour BPV in adults from meta-analysis, longitudinal or cross-sectional design, and examined BPV in terms of the following issues: (1) methods used to calculate and evaluate ARV; (2) assessment of 24-hour BPV determined using noninvasive ambulatory BP monitoring; (3) multivariate analysis adjusted for covariates, including some measure of BP; (4) association of 24-hour BPV with subclinical organ damage; and (5) the predictive value of 24-hour BPV on target organ damage and rate of cardiovascular events. Of the 19 assessed studies, 17 reported significant associations between high ARV and the presence and progression of subclinical organ damage, as well as the incidence of hard end points, such as cardiovascular events. In all these cases, ARV remained a significant independent predictor ( P <0.05) after adjustment for BP and other clinical factors. In addition, increased ARV in systolic BP was associated with risk of all cardiovascular events (hazard ratio, 1.18; 95% confidence interval, 1.09-1.27). Only 2 cross-sectional studies did not find that high ARV was a significant risk factor. Current evidence suggests that ARV index adds significant prognostic information to 24-hour ambulatory BP monitoring and is a useful approach for studying the clinical value of BPV. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Brain signal variability is parametrically modifiable.

PubMed

Garrett, Douglas D; McIntosh, Anthony R; Grady, Cheryl L

2014-11-01

Moment-to-moment brain signal variability is a ubiquitous neural characteristic, yet remains poorly understood. Evidence indicates that heightened signal variability can index and aid efficient neural function, but it is not known whether signal variability responds to precise levels of environmental demand, or instead whether variability is relatively static. Using multivariate modeling of functional magnetic resonance imaging-based parametric face processing data, we show here that within-person signal variability level responds to incremental adjustments in task difficulty, in a manner entirely distinct from results produced by examining mean brain signals. Using mixed modeling, we also linked parametric modulations in signal variability with modulations in task performance. We found that difficulty-related reductions in signal variability predicted reduced accuracy and longer reaction times within-person; mean signal changes were not predictive. We further probed the various differences between signal variance and signal means by examining all voxels, subjects, and conditions; this analysis of over 2 million data points failed to reveal any notable relations between voxel variances and means. Our results suggest that brain signal variability provides a systematic task-driven signal of interest from which we can understand the dynamic function of the human brain, and in a way that mean signals cannot capture. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Relationship between depression with FEV1 percent predicted and BODE index in chronic obstructive pulmonary disease

NASA Astrophysics Data System (ADS)

Gunawan, H.; Hanum, H.; Abidin, A.; Hanida, W.

2018-03-01

WHO reported more than 3 million people die from COPD in 2012 and are expected to rank third after cardiovascular and cancer diseases in the future. Recent studies reported the prevalence of depression in COPD patients was higher than in control group. So, it’s important for clinicians to understand the relationship of depression symptoms with clinical aspects of COPD. For determining the association of depression symptoms with lung function and BODE index in patients with stable COPD, a cross-sectional study was in 98 stable COPD outpatients from January to June 2017. Data were analyzed using Independent t-test, Mann-Whitney test, and Spearman’s rank correlation. COPD patients with depression had higher mMRC scores, and lower FEV1 percent predicted, and then 6-Minutes Walk Test compared to those without depression. There was a moderate strength of correlation (r=-0.43) between depression symptoms and FEV1 percent predicted, and strong correlation (r=0.614) between depression symptoms and BODE index. It indicates that BODE index is more accurate to describe symptoms of depression in COPD patients.

Central venous pressure and shock index predict lack of hemodynamic response to volume expansion in septic shock: a prospective, observational study.

PubMed

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

2012-12-01

Volume expansion is a common therapeutic intervention in septic shock, although patient response to the intervention is difficult to predict. Central venous pressure (CVP) and shock index have been used independently to guide volume expansion, although their use is questionable. We hypothesize that a combination of these measurements will be useful. In a prospective, observational study, patients with early septic shock received 10-mL/kg volume expansion at their treating physician's discretion after brief initial resuscitation in the emergency department. Central venous pressure and shock index were measured before volume expansion interventions. Cardiac index was measured immediately before and after the volume expansion using transthoracic echocardiography. Hemodynamic response was defined as an increase in a cardiac index of 15% or greater. Thirty-four volume expansions were observed in 25 patients. A CVP of 8 mm Hg or greater and a shock index of 1 beat min(-1) mm Hg(-1) or less individually had a good negative predictive value (83% and 88%, respectively). Of 34 volume expansions, the combination of both a high CVP and a low shock index was extremely unlikely to elicit hemodynamic response (negative predictive value, 93%; P = .02). Volume expansion in patients with early septic shock with a CVP of 8 mm Hg or greater and a shock index of 1 beat min(-1) mm Hg(-1) or less is unlikely to lead to an increase in cardiac index. Copyright © 2012 Elsevier Inc. All rights reserved.

Predicting the Spatial Distribution of Aspen Growth Potential in the Upper Great Lakes Region

Treesearch

Eric J. Gustafson; Sue M. Lietz; John L. Wright

2003-01-01

One way to increase aspen yields is to produce aspen on sites where aspen growth potential is highest. Aspen growth rates are typically predicted using site index, but this is impractical for landscape-level assessments. We tested the hypothesis that aspen growth can be predicted from site and climate variables and generated a model to map the spatial variability of...

An antiapoptotic Bcl-2 family protein index predicts the response of leukaemic cells to the pan-Bcl-2 inhibitor S1

PubMed Central

Zhang, Z; Liu, Y; Song, T; Xue, Z; Shen, X; Liang, F; Zhao, Y; Li, Z; Sheng, H

2013-01-01

Background: Bcl-2-like members have been found to be inherently overexpressed in many types of haematologic malignancies. The small-molecule S1 is a BH3 mimetic and a triple inhibitor of Bcl-2, Mcl-1 and Bcl-XL. Methods: The lethal dose 50 (LD50) values of S1 in five leukaemic cell lines and 41 newly diagnosed leukaemia samples were tested. The levels of Bcl-2 family members and phosphorylated Bcl-2 were semiquantitatively measured by western blotting. The interactions between Bcl-2 family members were tested by co-immunoprecipitation. The correlation between the LD50 and expression levels of Bcl-2 family members, alone or in combination, was analysed. Results: S1 exhibited variable sensitivity with LD50 values ranging >2 logs in both established and primary leukaemic cells. The ratio of pBcl-2/(Bcl-2+Mcl-1) could predict the S1 response. Furthermore, we demonstrated that pBcl-2 antagonised S1 by sequestering the Bak and Bim proteins that were released from Mcl-1, andpBcl-2/Bak, pBcl-2/Bax and pBcl-2/Bim complexes cannot be disrupted by S1. Conclusion: A predictive index was obtained for the novel BH3 mimetic S1. The shift of proapoptotic proteins from being complexed with Mcl-1 to being complexed with pBcl-2 was revealed for the first time, which is the mechanism underlying the index value described herein. PMID:23558901

Evaluation of a quantitative structure-property relationship (QSPR) for predicting mid-visible refractive index of secondary organic aerosol (SOA).

PubMed

Redmond, Haley; Thompson, Jonathan E

2011-04-21

In this work we describe and evaluate a simple scheme by which the refractive index (λ = 589 nm) of non-absorbing components common to secondary organic aerosols (SOA) may be predicted from molecular formula and density (g cm(-3)). The QSPR approach described is based on three parameters linked to refractive index-molecular polarizability, the ratio of mass density to molecular weight, and degree of unsaturation. After computing these quantities for a training set of 111 compounds common to atmospheric aerosols, multi-linear regression analysis was conducted to establish a quantitative relationship between the parameters and accepted value of refractive index. The resulting quantitative relationship can often estimate refractive index to ±0.01 when averaged across a variety of compound classes. A notable exception is for alcohols for which the model consistently underestimates refractive index. Homogenous internal mixtures can conceivably be addressed through use of either the volume or mole fraction mixing rules commonly used in the aerosol community. Predicted refractive indices reconstructed from chemical composition data presented in the literature generally agree with previous reports of SOA refractive index. Additionally, the predicted refractive indices lie near measured values we report for λ = 532 nm for SOA generated from vapors of α-pinene (R.I. 1.49-1.51) and toluene (R.I. 1.49-1.50). We envision the QSPR method may find use in reconstructing optical scattering of organic aerosols if mass composition data is known. Alternatively, the method described could be incorporated into in models of organic aerosol formation/phase partitioning to better constrain organic aerosol optical properties.

Fatigue life prediction of rotor blade composites: Validation of constant amplitude formulations with variable amplitude experiments

NASA Astrophysics Data System (ADS)

Westphal, T.; Nijssen, R. P. L.

2014-12-01

The effect of Constant Life Diagram (CLD) formulation on the fatigue life prediction under variable amplitude (VA) loading was investigated based on variable amplitude tests using three different load spectra representative for wind turbine loading. Next to the Wisper and WisperX spectra, the recently developed NewWisper2 spectrum was used. Based on these variable amplitude fatigue results the prediction accuracy of 4 CLD formulations is investigated. In the study a piecewise linear CLD based on the S-N curves for 9 load ratios compares favourably in terms of prediction accuracy and conservativeness. For the specific laminate used in this study Boerstra's Multislope model provides a good alternative at reduced test effort.

A multilateral modelling of Youth Soccer Performance Index (YSPI)

NASA Astrophysics Data System (ADS)

Bisyri Husin Musawi Maliki, Ahmad; Razali Abdullah, Mohamad; Juahir, Hafizan; Abdullah, Farhana; Ain Shahirah Abdullah, Nurul; Muazu Musa, Rabiu; Musliha Mat-Rasid, Siti; Adnan, Aleesha; Azura Kosni, Norlaila; Muhamad, Wan Siti Amalina Wan; Afiqah Mohamad Nasir, Nur

2018-04-01

This study aims to identify the most dominant factors that influencing performance of soccer player and to predict group performance for soccer players. A total of 184 of youth soccer players from Malaysia sport school and six soccer academy encompasses as respondence of the study. Exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA) were computed to identify the most dominant factors whereas reducing the initial 26 parameters with recommended >0.5 of factor loading. Meanwhile, prediction of the soccer performance was predicted by regression model. CFA revealed that sit and reach, vertical jump, VO2max, age, weight, height, sitting height, calf circumference (cc), medial upper arm circumference (muac), maturation, bicep, triceps, subscapular, suprailiac, 5M, 10M, and 20M speed were the most dominant factors. Further index analysis forming Youth Soccer Performance Index (YSPI) resulting by categorizing three groups namely, high, moderate, and low. The regression model for this study was significant set as p < 0.001 and R2 is 0.8222 which explained that the model contributed a total of 82% prediction ability to predict the whole set of the variables. The significant parameters in contributing prediction of YSPI are discussed. As a conclusion, the precision of the prediction models by integrating a multilateral factor reflecting for predicting potential soccer player and hopefully can create a competitive soccer games.

Anatomical and neuromuscular variables strongly predict maximum knee extension torque in healthy men.

PubMed

Trezise, J; Collier, N; Blazevich, A J

2016-06-01

This study examined the relative influence of anatomical and neuromuscular variables on maximal isometric and concentric knee extensor torque and provided a comparative dataset for healthy young males. Quadriceps cross-sectional area (CSA) and fascicle length (l f) and angle (θ f) from the four quadriceps components; agonist (EMG:M) and antagonist muscle activity, and percent voluntary activation (%VA); patellar tendon moment arm distance (MA) and maximal voluntary isometric and concentric (60° s(-1)) torques, were measured in 56 men. Linear regression models predicting maximum torque were ranked using Akaike's Information Criterion (AICc), and Pearson's correlation coefficients assessed relationships between variables. The best-fit models explained up to 72 % of the variance in maximal voluntary knee extension torque. The combination of 'CSA + θ f + EMG:M + %VA' best predicted maximum isometric torque (R (2) = 72 %, AICc weight = 0.38) and 'CSA + θ f + MA' (R (2) = 65 %, AICc weight = 0.21) best predicted maximum concentric torque. Proximal quadriceps CSA was included in all models rather than the traditionally used mid-muscle CSA. Fascicle angle appeared consistently in all models despite its weak correlation with maximum torque in isolation, emphasising the importance of examining interactions among variables. While muscle activity was important for torque prediction in both contraction modes, MA only strongly influenced maximal concentric torque. These models identify the main sources of inter-individual differences strongly influencing maximal knee extension torque production in healthy men. The comparative dataset allows the identification of potential variables to target (i.e. weaknesses) in individuals.

How long the singular value decomposed entropy predicts the stock market? - Evidence from the Dow Jones Industrial Average Index

NASA Astrophysics Data System (ADS)

Gu, Rongbao; Shao, Yanmin

2016-07-01

In this paper, a new concept of multi-scales singular value decomposition entropy based on DCCA cross correlation analysis is proposed and its predictive power for the Dow Jones Industrial Average Index is studied. Using Granger causality analysis with different time scales, it is found that, the singular value decomposition entropy has predictive power for the Dow Jones Industrial Average Index for period less than one month, but not for more than one month. This shows how long the singular value decomposition entropy predicts the stock market that extends Caraiani's result obtained in Caraiani (2014). On the other hand, the result also shows an essential characteristic of stock market as a chaotic dynamic system.

Seasonal-to-Interannual Precipitation Variability and Predictability in a Coupled Land-Atmosphere System

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Suarez, M. J.; Heiser, M.

1998-01-01

In an earlier GCM study, we showed that interactive land surface processes generally contribute more to continental precipitation variance than do variable sea surface temperatures (SSTs). A new study extends this result through an analysis of 16-member ensembles of multi-decade GCM simulations. We can now show that in many regions, although land processes determine the amplitude of the interannual precipitation anomalies, variable SSTs nevertheless control their timing. The GCM data can be processed into indices that describe geographical variations in (1) the potential for seasonal-to-interannual prediction, and (2) the extent to which the predictability relies on the proper representation of land-atmosphere feedback.

A GIS-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.)

Treesearch

Louis R. Iverson; Martin E. Dale; Charles T. Scott; Anantha Prasad; Anantha Prasad

1997-01-01

A geographic information system (GIS) approach was used in conjunction with forest-plot data to develop an integrated moisture index (IMI), which was then used to predict forest productivity (site index) and species composition for forests in Ohio. In this region, typical of eastern hardwoods across the Midwest and southern Appalachians, topographic aspect and position...

[Reliability of the PROFUND index to predict 4-year mortality in polypathological patients].

PubMed

Díez-Manglano, Jesús; Del Corral Beamonte, Esther; Ramos Ibáñez, Rosa; Lambán Aranda, María Pilar; Toyas Miazza, Carla; Rodero Roldán, María Del Mar; Ortiz Domingo, Concepción; Munilla López, Eulalia; de Escalante Yangüela, Begoña

2016-09-16

To determine the usefullness of the PROFUND index to assess the risk of global death after 4 years in polypathological patients. Multicenter prospective cohort (Internal Medicine and Geriatrics) study. Polypathological patients admitted between March 1st and June 30th 2011 were included. For each patient, data concerning age, sex, living at home or in a nursing residence, polypathology categories, Charlson, Barthel and Lawton-Brody indexes, Pfeiffer questionnaire, socio-familial Gijon scale, delirium, number of drugs, hemoglobin and creatinine values were gathered, and the PROFUND index was calculated. The follow-up lasted 4 years. We included 441 patients, 324 from Internal Medicine and 117 from Geriatrics, with a mean age of 80.9 (8.7) years. Of them, 245 (55.6%) were women. Heart (62.7%), neurological (41.4%) and respiratory (37.3%) diseases were the most frequent. Geriatrics inpatients were older and more dependants and presented greater cognitive deterioration. After 4 years, 335 (76%) patients died. Mortality was associated with age, dyspnoea, Barthel index<60, delirium, advanced neoplasia and≥4 admissions in the last year. The area under the curve of the PROFUND index was 0.748, 95% CI 0.689-0.806, P<.001 in Internal Medicine and 0.517, 95% CI 0.369-0.666, P=.818 in Geriatrics patients, respectively. The PROFUND index is a reliable tool for predicting long-term global mortality in polypathological patients from Internal Medicine but not from Geriatrics departments. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, A.; Catalano, F.; De Felice, M.; van den Hurk, B.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

2016-12-01

The European consortium earth system model (EC-Earth; http://www.ec-earth.org) has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul A.

2017-08-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul A.

2017-04-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel

Body mass index as a predictor of firefighter injury and workers' compensation claims.

PubMed

Kuehl, Kerry S; Kisbu-Sakarya, Yasemin; Elliot, Diane L; Moe, Esther L; Defrancesco, Carol A; Mackinnon, David P; Lockhart, Ginger; Goldberg, Linn; Kuehl, Hannah E

2012-05-01

To determine the relationship between lifestyle variables including body mass index and filing a workers' compensation claim due to firefighter injury. A cross-sectional evaluation of firefighter injury related to workers" compensation claims occurring 5 years after the original Promoting Healthy Lifestyles: Alternative Models' Effects study intervention. A logistic regression analysis for variables predicting filing a workers' compensation claim due to an injury was performed with a total of 433 participants. The odds of filing a compensation claim were almost 3 times higher for firefighters with a body mass index of more than 30 kg/m than firefighters with a normal body mass index (odds ratio, 2.89; P < 0.05). This study addresses a high-priority area of reducing firefighter injuries and workers' compensation claims. Maintaining a healthy body weight is important to reduce injury and workers' compensation claims among firefighters.

Uncertainty in the evaluation of the Predicted Mean Vote index using Monte Carlo analysis.

PubMed

Ricciu, R; Galatioto, A; Desogus, G; Besalduch, L A

2018-06-06

Today, evaluation of thermohygrometric indoor conditions is one of the most useful tools for building design and re-design and can be used to determine energy consumption in conditioned buildings. Since the beginning of the Predicted Mean Vote index (PMV), researchers have thoroughly investigated its issues in order to reach more accurate results; however, several shortcomings have yet to be solved. Among them is the uncertainty of environmental and subjective parameters linked to the standard PMV approach of ISO 7730 that classifies the thermal environment. To this end, this paper discusses the known thermal comfort models and the measurement approaches, paying particular attention to measurement uncertainties and their influence on PMV determination. Monte Carlo analysis has been applied on a data series in a "black-box" environment, and each involved parameter has been analysed in the PMV range from -0.9 to 0.9 under different Relative Humidity conditions. Furthermore, a sensitivity analysis has been performed in order to define the role of each variable. The results showed that an uncertainty propagation method could improve PMV model application, especially where it should be very accurate (-0.2 < PMV<0.2 range; winter season with Relative Humidity of 30%). Copyright © 2018 Elsevier Ltd. All rights reserved.

An evaluation of the effect of recent temperature variability on the prediction of coral bleaching events.

PubMed

Donner, Simon D

2011-07-01

Over the past 30 years, warm thermal disturbances have become commonplace on coral reefs worldwide. These periods of anomalous sea surface temperature (SST) can lead to coral bleaching, a breakdown of the symbiosis between the host coral and symbiotic dinoflagellates which reside in coral tissue. The onset of bleaching is typically predicted to occur when the SST exceeds a local climatological maximum by 1 degrees C for a month or more. However, recent evidence suggests that the threshold at which bleaching occurs may depend on thermal history. This study uses global SST data sets (HadISST and NOAA AVHRR) and mass coral bleaching reports (from Reefbase) to examine the effect of historical SST variability on the accuracy of bleaching prediction. Two variability-based bleaching prediction methods are developed from global analysis of seasonal and interannual SST variability. The first method employs a local bleaching threshold derived from the historical variability in maximum annual SST to account for spatial variability in past thermal disturbance frequency. The second method uses a different formula to estimate the local climatological maximum to account for the low seasonality of SST in the tropics. The new prediction methods are tested against the common globally fixed threshold method using the observed bleaching reports. The results find that estimating the bleaching threshold from local historical SST variability delivers the highest predictive power, but also a higher rate of Type I errors. The second method has the lowest predictive power globally, though regional analysis suggests that it may be applicable in equatorial regions. The historical data analysis suggests that the bleaching threshold may have appeared to be constant globally because the magnitude of interannual variability in maximum SST is similar for many of the world's coral reef ecosystems. For example, the results show that a SST anomaly of 1 degrees C is equivalent to 1.73-2.94 standard

Does the arrival index predict physiological stress reactivity in children.

PubMed

de Veld, Danielle M J; Riksen-Walraven, J Marianne; de Weerth, Carolina

2014-09-01

Knowledge about children's stress reactivity and its correlates is mostly based on one stress task, making it hard to assess the generalizability of the results. The development of an additional stress paradigm for children, that also limits stress exposure and test time, could greatly advance this field of research. Research in adults may provide a starting point for the development of such an additional stress paradigm, as changes in salivary cortisol and alpha-amylase (sAA) over a 1-h pre-stress period in the laboratory correlated strongly with subsequent reactivity to stress task (Balodis et al., 2010, Psychoneuroendocrinology 35:1363-73). The present study examined whether such strong correlations could be replicated in 9- to 11-year-old children. Cortisol and sAA samples were collected from 158 children (83 girls) during a 2.5-h visit to the laboratory. This visit included a 1-h pre-stress period in which children performed some non-stressful tasks and relaxed before taking part in a psychosocial stress task (TSST-C). A higher cortisol arrival index was significantly and weakly correlated with a higher AUCg but unrelated to cortisol reactivity to the stressor. A higher sAA arrival index was significantly and moderately related to lower stress reactivity and to a lower AUCi. Children's personality and emotion regulation variables were unrelated to the cortisol and sAA arrival indices. The results of this study do not provide a basis for the development of an additional stress paradigm for children. Further replications in children and adults are needed to clarify the potential meaning of an arrival index.

Using abiotic variables to predict importance of sites for species representation.

PubMed

Albuquerque, Fabio; Beier, Paul

2015-10-01

In systematic conservation planning, species distribution data for all sites in a planning area are used to prioritize each site in terms of the site's importance toward meeting the goal of species representation. But comprehensive species data are not available in most planning areas and would be expensive to acquire. As a shortcut, ecologists use surrogates, such as occurrences of birds or another well-surveyed taxon, or land types defined from remotely sensed data, in the hope that sites that represent the surrogates also represent biodiversity. Unfortunately, surrogates have not performed reliably. We propose a new type of surrogate, predicted importance, that can be developed from species data for a q% subset of sites. With species data from this subset of sites, importance can be modeled as a function of abiotic variables available at no charge for all terrestrial areas on Earth. Predicted importance can then be used as a surrogate to prioritize all sites. We tested this surrogate with 8 sets of species data. For each data set, we used a q% subset of sites to model importance as a function of abiotic variables, used the resulting function to predict importance for all sites, and evaluated the number of species in the sites with highest predicted importance. Sites with the highest predicted importance represented species efficiently for all data sets when q = 25% and for 7 of 8 data sets when q = 20%. Predicted importance requires less survey effort than direct selection for species representation and meets representation goals well compared with other surrogates currently in use. This less expensive surrogate may be useful in those areas of the world that need it most, namely tropical regions with the highest biodiversity, greatest biodiversity loss, most severe lack of inventory data, and poorly developed protected area networks. © 2015 Society for Conservation Biology.

Relation of biomass to basal area and site index on an Appalachian watershed

Treesearch

Harry V., Jr. Wiant; Robert Knight; John E. Baumgras

1984-01-01

The biomass of 50-year-old cove hardwood and upland oak stands on an Appalachian watershed was more strongly related to basal area than to site index. Equations are presented for predicting the green and dry weight per acre of biomass components with basal area as the independent variable.

Coupled Modes over Indian Ocean at Sub-seasonal time Scales and its Prediction

NASA Astrophysics Data System (ADS)

Jung, E.; Kirtman, B. P.

2014-12-01

Sub-seasonal variability over the Indian Ocean, such as Madden-Julian Oscillation impacts weather and climate globally. However, the prediction of tropical sub-seasonal variability (TSV) remains a challenge, and understanding air-sea interactions on TSV time-scales is likely to be an important part of the prediction problem. The purpose of this paper is to examine the predictability of sub-seasonal variability in the tropical Indo-Pacific region. The analysis emphasizes on variability associated with coupled air-sea interactions in observational estimates, and how well these coupled modes are simulated and predicted within the context of a 30-year retrospective forecast experiment with a state-of-the-art atmosphere-ocean coupled model. The analysis shows that Sea Surface Temperature anomalies (SSTA) over the Indian Ocean tend to precede precipitation anomalies by 7-11 days with maximum amplitude over the Arabian Sea and the Bay of Bengal for summer and along the Seychelles-Chagos Thermocline Ridge (SCTR) region for winter. Though these coupled modes are captured by the models, the forecasts fail to predict its evolution. Based on the diagnosis of these coupled modes, we introduce a SCTR-SST index and an index that measures the modulation of the low-frequency amplitude (LFAM) of sub-seasonal SSTA variability over SCTR as a way to predict the coupled modes. Based on correlation with the observed variability, SCTR-SST has forecast skill of about 45 days over the Indian Ocean. However the sub-seasonal SSTAs in the predictions and the observational estimates do not have any direct ENSO tele-connection. In contrast, the LFAM of the sub-seasonal SSTA variance over SCTR is strongly correlated with ENSO, suggesting enhanced sub-seasonal variance on seasonal time-scales is potentially predictable.

Assessing conservation relevance of organism-environment relations using predicted changes in response variables

USGS Publications Warehouse

Gutzwiller, Kevin J.; Barrow, Wylie C.; White, Joseph D.; Johnson-Randall, Lori; Cade, Brian S.; Zygo, Lisa M.

2010-01-01

1. Organism–environment models are used widely in conservation. The degree to which they are useful for informing conservation decisions – the conservation relevance of these relations – is important because lack of relevance may lead to misapplication of scarce conservation resources or failure to resolve important conservation dilemmas. Even when models perform well based on model fit and predictive ability, conservation relevance of associations may not be clear without also knowing the magnitude and variability of predicted changes in response variables. 2. We introduce a method for evaluating the conservation relevance of organism–environment relations that employs confidence intervals for predicted changes in response variables. The confidence intervals are compared to a preselected magnitude of change that marks a threshold (trigger) for conservation action. To demonstrate the approach, we used a case study from the Chihuahuan Desert involving relations between avian richness and broad-scale patterns of shrubland. We considered relations for three winters and two spatial extents (1- and 2-km-radius areas) and compared predicted changes in richness to three thresholds (10%, 20% and 30% change). For each threshold, we examined 48 relations. 3. The method identified seven, four and zero conservation-relevant changes in mean richness for the 10%, 20% and 30% thresholds respectively. These changes were associated with major (20%) changes in shrubland cover, mean patch size, the coefficient of variation for patch size, or edge density but not with major changes in shrubland patch density. The relative rarity of conservation-relevant changes indicated that, overall, the relations had little practical value for informing conservation decisions about avian richness. 4. The approach we illustrate is appropriate for various response and predictor variables measured at any temporal or spatial scale. The method is broadly applicable across ecological

Examining impulse-variability in overarm throwing.

PubMed

Urbin, M A; Stodden, David; Boros, Rhonda; Shannon, David

2012-01-01

The purpose of this study was to examine variability in overarm throwing velocity and spatial output error at various percentages of maximum to test the prediction of an inverted-U function as predicted by impulse-variability theory and a speed-accuracy trade-off as predicted by Fitts' Law Thirty subjects (16 skilled, 14 unskilled) were instructed to throw a tennis ball at seven percentages of their maximum velocity (40-100%) in random order (9 trials per condition) at a target 30 feet away. Throwing velocity was measured with a radar gun and interpreted as an index of overall systemic power output. Within-subject throwing velocity variability was examined using within-subjects repeated-measures ANOVAs (7 repeated conditions) with built-in polynomial contrasts. Spatial error was analyzed using mixed model regression. Results indicated a quadratic fit with variability in throwing velocity increasing from 40% up to 60%, where it peaked, and then decreasing at each subsequent interval to maximum (p < .001, η2 = .555). There was no linear relationship between speed and accuracy. Overall, these data support the notion of an inverted-U function in overarm throwing velocity variability as both skilled and unskilled subjects approach maximum effort. However, these data do not support the notion of a speed-accuracy trade-off. The consistent demonstration of an inverted-U function associated with systemic power output variability indicates an enhanced capability to regulate aspects of force production and relative timing between segments as individuals approach maximum effort, even in a complex ballistic skill.

Specification of variables predictive of victories in the sport of boxing.

PubMed

Warnick, Jason E; Warnick, Kyla

2007-08-01

Compared to other sports, very little research has been conducted on which variables can predict victory in the sport of boxing. This investigation examined whether boxers' age, weight change from their preceding contest, country of origin, total number of wins, total number of losses, performance in their preceding contest, or the possession of a championship title was predictive of a winning performance in a given bout. A 1-mo. sample of male professional boxing records for all contests held in the USA (N = 400) were collected from the BoxRec online database. Logistic regression analysis indicated that only boxers' age, total number of wins and losses, and the performance in the preceding contest predicted significant variance in outcome.

Predictive Value of Triglyceride Glucose Index for the Risk of Incident Diabetes: A 4-Year Retrospective Longitudinal Study.

PubMed

Lee, Da Young; Lee, Eun Seo; Kim, Ji Hyun; Park, Se Eun; Park, Cheol-Young; Oh, Ki-Won; Park, Sung-Woo; Rhee, Eun-Jung; Lee, Won-Young

The Triglyceride Glucose Index (TyG index) is considered a surrogate marker of insulin resistance. The aim of this study is to investigate whether the TyG index has a predictive role in identifying individuals with a high risk of incident diabetes and to compare it with other indicators of metabolic health. A total 2900 non-diabetic adults who attended five consecutive annual health check-ups at Kangbuk Samsung Hospital was divided into four subgroups using three methods: (1) baseline TyG index; (2) obesity status (body mass index ≥25 kg/m2) and cutoff value of TyG index; (3) obesity status and metabolic health, defined as having fewer than two of the five components of high blood pressure, fasting blood glucose, triglyceride, low high-density lipoprotein cholesterol, and highest decile of homeostasis model assessment-insulin resistance. The development of diabetes was assessed annually using self-questionnaire, fasting glucose, and glycated hemoglobin. We compared the risk of incident diabetes using multivariate Cox analysis. During 11623 person-years there were 101 case of incident diabetes. Subjects with high TyG index had a high risk of diabetes. For TyG index quartiles, hazard ratios (HRs) of quartiles 3 and 4 were 4.06 (p = 0.033) and 5.65 (p = 0.006) respectively. When the subjects were divided by obesity status and cutoff value of TyG index of 8.8, the subgroups with TyG index ≥ 8.8 regardless of obesity had a significantly high risk for diabetes (HR 2.40 [p = 0.024] and 2.25 [p = 0.048]). For obesity status and metabolic health, the two metabolically unhealthy subgroups regardless of obesity had a significantly high risk for diabetes (HRs 2.54 [p = 0.024] and 2.73 [p = 0.021]). In conclusion, the TyG index measured at a single time point may be an indicator of the risk for incident diabetes. The predictive value of the TyG index was comparable to that of metabolic health.

GIS Based Distributed Runoff Predictions in Variable Source Area Watersheds Employing the SCS-Curve Number

NASA Astrophysics Data System (ADS)

Steenhuis, T. S.; Mendoza, G.; Lyon, S. W.; Gerard Marchant, P.; Walter, M. T.; Schneiderman, E.

2003-04-01

Because the traditional Soil Conservation Service Curve Number (SCS-CN) approach continues to be ubiquitously used in GIS-BASED water quality models, new application methods are needed that are consistent with variable source area (VSA) hydrological processes in the landscape. We developed within an integrated GIS modeling environment a distributed approach for applying the traditional SCS-CN equation to watersheds where VSA hydrology is a dominant process. Spatial representation of hydrologic processes is important for watershed planning because restricting potentially polluting activities from runoff source areas is fundamental to controlling non-point source pollution. The methodology presented here uses the traditional SCS-CN method to predict runoff volume and spatial extent of saturated areas and uses a topographic index to distribute runoff source areas through watersheds. The resulting distributed CN-VSA method was incorporated in an existing GWLF water quality model and applied to sub-watersheds of the Delaware basin in the Catskill Mountains region of New York State. We found that the distributed CN-VSA approach provided a physically-based method that gives realistic results for watersheds with VSA hydrology.

Predictive Coding of Dynamical Variables in Balanced Spiking Networks

PubMed Central

Boerlin, Martin; Machens, Christian K.; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated. PMID:24244113

Oxygenation Saturation Index Predicts Clinical Outcomes in ARDS.

PubMed

DesPrez, Katherine; McNeil, J Brennan; Wang, Chunxue; Bastarache, Julie A; Shaver, Ciara M; Ware, Lorraine B

2017-12-01

Traditional measures of ARDS severity such as Pao 2 /Fio 2 may not reliably predict clinical outcomes. The oxygenation index (OI [Fio 2  × mean airway pressure × 100)/Pao 2 ]) may more accurately reflect ARDS severity but requires arterial blood gas measurement. We hypothesized that the oxygenation saturation index (OSI [Fio 2  × mean airway pressure × 100)/oxygen saturation by pulse oximetry (Spo 2 )]) is a reliable noninvasive surrogate for the OI that is associated with hospital mortality and ventilator-free days (VFDs) in patients with ARDS. Critically ill patients enrolled in a prospective cohort study were eligible if they developed ARDS (Berlin criteria) during the first 4 ICU days and had mean airway pressure, Spo 2 /Fio 2 , and Pao 2 /Fio 2 values recorded on the first day of ARDS (N = 329). The highest mean airway pressure and lowest Spo 2 /Fio 2 and Pao 2 /Fio 2 values were used to calculate OI and OSI. The association between OI or OSI and hospital mortality or VFD was analyzed by using logistic regression and linear regression, respectively. The area under the receiver-operating characteristic curve (AUC) for mortality was compared among OI, OSI, Spo 2 /Fio 2 , Pao 2 /Fio 2 , and Acute Physiology and Chronic Health Evaluation II scores. OI and OSI were strongly correlated (rho = 0.862; P < .001). OSI was independently associated with hospital mortality (OR per 5-point increase in OSI, 1.228 [95% CI, 1.056-1.429]; P = .008). OI and OSI were each associated with a reduction in VFD (OI, P = .023; OSI, P = .005). The AUC for mortality prediction was greatest for Acute Physiology and Chronic Health Evaluation II scores (AUC, 0.695; P < .005) and OSI (AUC, 0.602; P = .007). The AUC for OSI was substantially better in patients aged < 40 years (AUC, 0.779; P < .001). In patients with ARDS, the OSI was correlated with the OI. The OSI on the day of ARDS diagnosis was significantly associated with increased mortality and fewer VFDs. The

Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability.

PubMed

Beda, Alessandro; Simpson, David M; Faes, Luca

2017-01-01

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications

Seizure-related variables are predictive of attention and memory in children with epilepsy.

PubMed

Lordo, Danielle N; Van Patten, Ryan; Sudikoff, Eliana L; Harker, Lisa

2017-08-01

Children with epilepsy (CWE) are at greater risk for cognitive deficits and behavioral difficulties than are typically developing healthy children, and particular epileptic symptoms and treatments may contribute to this risk. The current study examined the relationships between four seizure-related variables and attention and memory functioning in a sample of 207 CWE (ages 6-16) using both neurocognitive and parent/teacher-report measures. Sociodemographic, medical, and neuropsychological data were collected from patients' medical charts in a retrospective fashion. Hierarchical multiple regressions were performed with sociodemographic variables (age, gender, race) entered as step one and seizure-related variables (number of anti-epileptic drugs [AEDs], EEG laterality, EEG lobe of focus, lifetime seizure duration) entered as step two. Results indicated that seizure-related variables were consistently predictive of poor cognitive performances above and beyond sociodemographic variables, although only minimally predictive of parent/teacher-reports. A longer duration of seizure burden and greater number of AEDs were robust predictors of performances on most cognitive measures. These findings indicate that CWE with long lifetime seizure durations and multiple AEDs are at risk for inefficiencies in attention and memory. Knowledge of this risk will allow treating providers greater accuracy and precision when planning medical treatment and making recommendations to families. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Global variability in angina pectoris and its association with body mass index and poverty.

PubMed

Liu, Longjian; Ma, Jixiang; Yin, Xiaoyan; Kelepouris, Ellie; Eisen, Howard J

2011-03-01

In the absence of a previous global comparison, we examined the variability in the prevalence of angina across 52 countries and its association with body weight and the poverty index using data from the World Health Organization-World Health Survey. The participants with angina were defined as those who had positive results using a Rose angina questionnaire and/or self-report of a physician diagnosis of angina. The body mass index (BMI) was determined as the weight in kilograms divided by the square of the height in meters. The poverty index (a standard score of socioeconomic status for a given country) was extracted from the United Nations' statistics. The associations of angina with the BMI and poverty index were analyzed cross-sectionally using univariate and multivariate analyses. The results showed that the total participants (n = 210,787) had an average age of 40.64 years. The prevalence of angina ranged from 2.44% in Tunisia to 23.89% in Chad. Those participants with a BMI of <18.5 kg/m(2) (underweight), 25 to 29 kg/m(2) (overweight), or BMI ≥ 30 kg/m(2) (obese) had a significantly greater risk of having angina compared to those with a normal BMI (≥ 18.5 but <25 k/m(2)). The odds ratios of overweight and obese for angina remained significant in the multilevel models, in which the influence of the country-level poverty status was considered. A tendency was seen for underweight status and a poverty index >14.65% to be associated with the risk of having angina, although these associations were not statistically significant in the multilevel models. In conclusion, significant variations were found in the anginal rates across 52 countries worldwide. An increased BMI was significantly associated with the odds of having angina. Published by Elsevier Inc.

Variables Predicting Foreign Language Reading Comprehension and Vocabulary Acquisition in a Linear Hypermedia Environment

ERIC Educational Resources Information Center

Akbulut, Yavuz

2007-01-01

Factors predicting vocabulary learning and reading comprehension of advanced language learners of English in a linear multimedia text were investigated in the current study. Predictor variables of interest were multimedia type, reading proficiency, learning styles, topic interest and background knowledge about the topic. The outcome variables of…

Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

PubMed Central

2012-01-01

Background The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. Results We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest index score, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence

Seasonal forecasts in the Sahel region: the use of rainfall-based predictive variables

NASA Astrophysics Data System (ADS)

Lodoun, Tiganadaba; Sanon, Moussa; Giannini, Alessandra; Traoré, Pierre Sibiry; Somé, Léopold; Rasolodimby, Jeanne Millogo

2014-08-01

In the Sahel region, seasonal predictions are crucial to alleviate the impacts of climate variability on populations' livelihoods. Agricultural planning (e.g., decisions about sowing date, fertilizer application date, and choice of crop or cultivar) is based on empirical predictive indices whose accuracy to date has not been scientifically proven. This paper attempts to statistically test whether the pattern of rainfall distribution over the May-July period contributes to predicting the real onset date and the nature (wet or dry) of the rainy season, as farmers believe. To that end, we considered historical records of daily rainfall from 51 stations spanning the period 1920-2008 and the different agro-climatic zones in Burkina Faso. We performed (1) principal component analysis to identify climatic zones, based on the patterns of intra-seasonal rainfall, (2) and linear discriminant analysis to find the best rainfall-based variables to distinguish between real and false onset dates of the rainy season, and between wet and dry seasons in each climatic zone. A total of nine climatic zones were identified in each of which, based on rainfall records from May to July, we derived linear discriminant functions to correctly predict the nature of a potential onset date of the rainy season (real or false) and that of the rainy season (dry or wet) in at least three cases out of five. These functions should contribute to alleviating the negative impacts of climate variability in the different climatic zones of Burkina Faso.

Relationships between 24-h blood pressure variability and 24-h central arterial pressure, pulse wave velocity and augmentation index in hypertensive patients.

PubMed

Omboni, Stefano; Posokhov, Igor N; Rogoza, Anatoly N

2017-04-01

Twenty-four-h blood pressure variability (BPV) predicts cardiovascular complications in hypertension, but its association with pulse wave indices (central arterial pressure, pulse wave velocity (PWV) and augmentation index (AIx)) is poorly understood. In the present study, we assessed the degree of the effect of 24-h BPV on 24-h pulse wave indices. Brachial blood pressure was measured non-invasively over the 24 h with an electronic, oscillometric, automated device (BPLab) in 661 uncomplicated treated or untreated hypertensive patients. Digitalized oscillometric waveforms were analyzed with a validated algorithm to obtain pulse wave indices. Twenty-four-h BPV was calculated as the unweighted (SDu) or weighted s.d. (SDw) of the mean blood pressure or as the average real variability (ARV). Twenty-four-h systolic BPV showed a direct and significant relationship with the central arterial systolic pressure (r=0.28 SDu, r=0.40 SDw, r=0.34 ARV), PWV (r=0.10 SDu, r=0.21 SDw, r=0.19 ARV) and AIx (r=0.17 SDu, r=0.27 SDw, r=0.23 ARV). After adjustment for age, sex, body mass index, antihypertensive treatment and 24-h systolic blood pressure, the relationship lost some power but was still significant for all measures, except for the AIx. Pulse wave indices were higher in patients with high BPV than in those with low BPV: after adjustment, these differences were abolished for the AIx. The diastolic BPV showed a weak association with the pulse wave indices. In conclusion, in hypertensive patients, 24-h systolic BPV is moderately and independently associated with 24-h central arterial pressure and stiffness.

The Stochastic predictability limits of GCM internal variability and the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

NASA Astrophysics Data System (ADS)

Del Rio Amador, Lenin; Lovejoy, Shaun

2017-04-01

Over the past ten years, a key advance in our understanding of atmospheric variability is the discovery that between the weather and climate regime lies an intermediate "macroweather" regime, spanning the range of scales from ≈10 days to ≈30 years. Macroweather statistics are characterized by two fundamental symmetries: scaling and the factorization of the joint space-time statistics. In the time domain, the scaling has low intermittency with the additional property that successive fluctuations tend to cancel. In space, on the contrary the scaling has high (multifractal) intermittency corresponding to the existence of different climate zones. These properties have fundamental implications for macroweather forecasting: a) the temporal scaling implies that the system has a long range memory that can be exploited for forecasting; b) the low temporal intermittency implies that mathematically well-established (Gaussian) forecasting techniques can be used; and c), the statistical factorization property implies that although spatial correlations (including teleconnections) may be large, if long enough time series are available, they are not necessarily useful in improving forecasts. Theoretically, these conditions imply the existence of stochastic predictability limits in our talk, we show that these limits apply to GCM's. Based on these statistical implications, we developed the Stochastic Seasonal and Interannual Prediction System (StocSIPS) for the prediction of temperature from regional to global scales and from one month to many years horizons. One of the main components of StocSIPS is the separation and prediction of both the internal and externally forced variabilities. In order to test the theoretical assumptions and consequences for predictability and predictions, we use 41 different CMIP5 model outputs from preindustrial control runs that have fixed external forcings: whose variability is purely internally generated. We first show that these statistical

Epileptic Seizure Prediction Using a New Similarity Index for Chaotic Signals

NASA Astrophysics Data System (ADS)

Niknazar, Hamid; Nasrabadi, Ali Motie

Epileptic seizures are generated by abnormal activity of neurons. The prediction of epileptic seizures is an important issue in the field of neurology, since it may improve the quality of life of patients suffering from drug resistant epilepsy. In this study a new similarity index based on symbolic dynamic techniques which can be used for extracting behavior of chaotic time series is presented. Using Freiburg EEG dataset, it is found that the method is able to detect the behavioral changes of the neural activity prior to epileptic seizures, so it can be used for prediction of epileptic seizure. A sensitivity of 63.75% with 0.33 false positive rate (FPR) in all 21 patients and sensitivity of 96.66% with 0.33 FPR in eight patients were achieved using the proposed method. Moreover, the method was evaluated by applying on Logistic and Tent map with different parameters to demonstrate its robustness and ability in determining similarity between two time series with the same chaotic characterization.

Low-Frequency Temporal Variability in Mira and Semiregular Variables

NASA Astrophysics Data System (ADS)

Templeton, Matthew R.; Karovska, M.; Waagen, E. O.

2012-01-01

We investigate low-frequency variability in a large sample of Mira and semiregular variables with long-term visual light curves from the AAVSO International Database. Our aim is to determine whether we can detect and measure long-timescale variable phenomena in these stars, for example photometric variations that might be associated with supergranular convection. We analyzed the long-term light curves of 522 variable stars of the Mira and SRa, b, c, and d classes. We calculated their low-frequency time-series spectra to characterize rednoise with the power density spectrum index, and then correlate this index with other observable characteristics such as spectral type and primary pulsation period. In our initial analysis of the sample, we see that the semiregular variables have a much broader range of spectral index than the Mira types, with the SRb subtype having the broadest range. Among Mira variables we see that the M- and S-type Miras have similarly wide ranges of index, while the C-types have the narrowest with generally shallower slopes. There is also a trend of steeper slope with larger amplitude, but at a given amplitude, a wide range of slopes are seen. The ultimate goal of the project is to identify stars with strong intrinsic red noise components as possible targets for resolved surface imaging with interferometry.

Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions

NASA Astrophysics Data System (ADS)

van Hooidonk, R.; Huber, M.

2012-03-01

Future widespread coral bleaching and subsequent mortality has been projected using sea surface temperature (SST) data derived from global, coupled ocean-atmosphere general circulation models (GCMs). While these models possess fidelity in reproducing many aspects of climate, they vary in their ability to correctly capture such parameters as the tropical ocean seasonal cycle and El Niño Southern Oscillation (ENSO) variability. Such weaknesses most likely reduce the accuracy of predicting coral bleaching, but little attention has been paid to the important issue of understanding potential errors and biases, the interaction of these biases with trends, and their propagation in predictions. To analyze the relative importance of various types of model errors and biases in predicting coral bleaching, various intra- and inter-annual frequency bands of observed SSTs were replaced with those frequencies from 24 GCMs 20th century simulations included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. Subsequent thermal stress was calculated and predictions of bleaching were made. These predictions were compared with observations of coral bleaching in the period 1982-2007 to calculate accuracy using an objective measure of forecast quality, the Peirce skill score (PSS). Major findings are that: (1) predictions are most sensitive to the seasonal cycle and inter-annual variability in the ENSO 24-60 months frequency band and (2) because models tend to understate the seasonal cycle at reef locations, they systematically underestimate future bleaching. The methodology we describe can be used to improve the accuracy of bleaching predictions by characterizing the errors and uncertainties involved in the predictions.

The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic.

PubMed

Serreze, Mark C; Meier, Walter N

2018-05-28

As assessed over the period of satellite observations, October 1978 to present, there are downward linear trends in Arctic sea ice extent for all months, largest at the end of the melt season in September. The ice cover is also thinning. Downward trends in extent and thickness have been accompanied by pronounced interannual and multiyear variability, forced by both the atmosphere and ocean. As the ice thins, its response to atmospheric and oceanic forcing may be changing. In support of a busier Arctic, there is a growing need to predict ice conditions on a variety of time and space scales. A major challenge to providing seasonal scale predictions is the 7-10 days limit of numerical weather prediction. While a seasonally ice-free Arctic Ocean is likely well within this century, there is much uncertainty in the timing. This reflects differences in climate model structure, the unknown evolution of anthropogenic forcing, and natural climate variability. In sharp contrast to the Arctic, Antarctic sea ice extent, while highly variable, has increased slightly over the period of satellite observations. The reasons for this different behavior remain to be resolved, but responses to changing atmospheric circulation patterns appear to play a strong role. © 2018 New York Academy of Sciences.

The seasonal predictability of blocking frequency in two seasonal prediction systems (CMCC, Met-Office) and the associated representation of low-frequency variability.

NASA Astrophysics Data System (ADS)

Athanasiadis, Panos; Gualdi, Silvio; Scaife, Adam A.; Bellucci, Alessio; Hermanson, Leon; MacLachlan, Craig; Arribas, Alberto; Materia, Stefano; Borelli, Andrea

2014-05-01

Low-frequency variability is a fundamental component of the atmospheric circulation. Extratropical teleconnections, the occurrence of blocking and the slow modulation of the jet streams and storm tracks are all different aspects of low-frequency variability. Part of the latter is attributed to the chaotic nature of the atmosphere and is inherently unpredictable. On the other hand, primarily as a response to boundary forcings, tropospheric low-frequency variability includes components that are potentially predictable. Seasonal forecasting faces the difficult task of predicting these components. Particularly referring to the extratropics, the current generation of seasonal forecasting systems seem to be approaching this target by realistically initializing most components of the climate system, using higher resolution and utilizing large ensemble sizes. Two seasonal prediction systems (Met-Office GloSea and CMCC-SPS-v1.5) are analyzed in terms of their representation of different aspects of extratropical low-frequency variability. The current operational Met-Office system achieves unprecedented high scores in predicting the winter-mean phase of the North Atlantic Oscillation (NAO, corr. 0.74 at 500 hPa) and the Pacific-N. American pattern (PNA, corr. 0.82). The CMCC system, considering its small ensemble size and course resolution, also achieves good scores (0.42 for NAO, 0.51 for PNA). Despite these positive features, both models suffer from biases in low-frequency variance, particularly in the N. Atlantic. Consequently, it is found that their intrinsic variability patterns (sectoral EOFs) differ significantly from the observed, and the known teleconnections are underrepresented. Regarding the representation of N. hemisphere blocking, after bias correction both systems exhibit a realistic climatology of blocking frequency. In this assessment, instantaneous blocking and large-scale persistent blocking events are identified using daily geopotential height fields at

Childhood and/or Adolescent Sexual Experiences: Predicting Variability in Subsequent Adjustment.

ERIC Educational Resources Information Center

Seidner, Andrea L.; And Others

There is considerable debate regarding the effects of childhood sexual abuse on an individual's subsequent adjustment. To determine which variables are most useful in predicting subsequent adjustment of individuals who were involved in sexual experiences as children or adolescents, 59 female and 17 male undergraduates who reported having had a…

Do genetic risk scores for body mass index predict risk of phobic anxiety? Evidence for a shared genetic risk factor

PubMed Central

Walter, Stefan; Glymour, M. Maria; Koenen, Karestan; Liang, Liming; Tchetgen Tchetgen, Eric J; Cornelis, Marilyn; Chang, Shun-Chiao; Rewak, Marissa; Rimm, Eric; Kawachi, Ichiro; Kubzansky, Laura D.

2015-01-01

Background Obesity and anxiety are often linked but the direction of effects is not clear. Methods Using genetic instrumental variable (IV) analyses in a sample of 5911 female participants from the Nurses´ Health Study (NHS, initiated in 1976) and 3697 male participants from the Health Professional Follow-up Study (HPFS, initiated in 1986), we aim to determine whether obesity increases symptoms of phobic anxiety. FTO, MC4R, and a genetic risk score (GRS) based on 32 single nucleotide polymorphisms that significantly predict body mass index (BMI), were used as instrumental variables. “Functional” GRS corresponding with specific biological pathways that shape BMI (adipogenesis, appetite, and cardio-pulmonary), were considered. Phobic anxiety as measured by the Crown Crisp Experimental Index (CCI) in 2004 in NHS and 2000 in HPFS was the main outcome. Results In observational analysis, a one unit higher BMI was associated with higher phobic anxiety symptoms (women NHS: beta=0.05; 95% Confidence Interval (CI): 0.030 – 0.068 and men, HPFS, beta = 0.04; 95% CI: 0.016 – 0.071). IV analyses showed that BMI instrumented by FTO was associated with higher phobic anxiety symptoms (p = 0.005) but BMI instrumented by GRS was not (p=0.256). Functional GRS scores showed heterogeneous, non-significant effects of BMI on phobic anxiety symptoms. Conclusions Our findings do not provide conclusive evidence in favor of the hypothesis that higher BMI leads to higher levels of phobic anxiety, but rather suggest that genes that influence obesity, in particular FTO, may have direct effects on phobic anxiety, i.e., that obesity and phobic anxiety may share common genetic determinants. PMID:25065638

Do genetic risk scores for body mass index predict risk of phobic anxiety? Evidence for a shared genetic risk factor.

PubMed

Walter, S; Glymour, M M; Koenen, K; Liang, L; Tchetgen Tchetgen, E J; Cornelis, M; Chang, S-C; Rewak, M; Rimm, E; Kawachi, I; Kubzansky, L D

2015-01-01

Obesity and anxiety are often linked but the direction of effects is not clear. Using genetic instrumental variable (IV) analyses in 5911 female participants from the Nurses' Health Study (NHS, initiated 1976) and 3697 male participants from the Health Professional Follow-up Study (HPFS, initiated 1986), we aimed to determine whether obesity increases symptoms of phobic anxiety. As instrumental variables we used the fat mass and obesity-associated (FTO) gene, the melanocortin 4 receptor (MC4R) gene and a genetic risk score (GRS) based on 32 single nucleotide polymorphisms (SNPs) that significantly predict body mass index (BMI). 'Functional' GRSs corresponding with specific biological pathways that shape BMI (adipogenesis, appetite and cardiopulmonary) were considered. The main outcome was phobic anxiety measured by the Crown Crisp Index (CCI) in 2004 in the NHS and in 2000 in the HPFS. In observational analysis, a 1-unit higher BMI was associated with higher phobic anxiety symptoms [women: β = 0.05, 95% confidence interval (CI) 0.030-0.068; men: β = 0.04, 95% CI 0.016-0.071). IV analyses showed that BMI was associated with higher phobic anxiety symptoms in the FTO-instrumented analysis (p = 0.005) but not in the GRS-instrumented analysis (p = 0.256). Functional GRSs showed heterogeneous, non-significant effects of BMI on phobic anxiety symptoms. Our findings do not provide conclusive evidence in favor of the hypothesis that higher BMI leads to higher levels of phobic anxiety, but rather suggest that genes that influence obesity, in particular FTO, may have direct effects on phobic anxiety, and hence that obesity and phobic anxiety may share common genetic determinants.

Multi-scale enhancement of climate prediction over land by improving the model sensitivity to vegetation variability

NASA Astrophysics Data System (ADS)

Alessandri, A.; Catalano, F.; De Felice, M.; Hurk, B. V. D.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

2017-12-01

Here we demonstrate, for the first time, that the implementation of a realistic representation of vegetation in Earth System Models (ESMs) can significantly improve climate simulation and prediction across multiple time-scales. The effective sub-grid vegetation fractional coverage vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the surface resistance to evapotranspiration, albedo, roughness lenght, and soil field capacity. To adequately represent this effect in the EC-Earth ESM, we included an exponential dependence of the vegetation cover on the Leaf Area Index.By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal (2-4 months) and weather (4 days) time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation-cover consistently correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in

On the spatial decorrelation of stochastic solar resource variability at long timescales

DOE PAGES

Perez, Marc J. R.; Fthenakis, Vasilis M.

2015-05-16

Understanding the spatial and temporal characteristics of solar resource variability is important because it helps inform the discussion surrounding the merits of geographic dispersion and subsequent electrical interconnection of photovoltaics as part of a portfolio of future solutions for coping with this variability. The unpredictable resource variability arising from the stochastic nature of meteorological phenomena (from the passage of clouds to the movement of weather systems) is of most concern for achieving high PV penetration because unlike the passage of seasons or the shift from day to night, the uncertainty makes planning a challenge. A suitable proxy for unpredictable solarmore » resource variability at any given location is the series of variations in the clearness index from one time period to the next because the clearness index is largely independent of the predictable influence of solar geometry. At timescales shorter than one day, the correlation between these variations in clearness index at pairs of distinct geographic locations decreases with spatial extent and with timescale. As the aggregate variability across N decorrelated locations decreases as 1/√N, identifying the distance required to achieve this decorrelation is critical to quantifying the expected reduction in variability from geographic dispersion.« less

New social adaptability index predicts overall mortality.

PubMed

Goldfarb-Rumyantzev, Alexander; Barenbaum, Anna; Rodrigue, James; Rout, Preeti; Isaacs, Ross; Mukamal, Kenneth

2011-08-01

Definitions of underprivileged status based on race, gender and geographic location are neither sensitive nor specific; instead we proposed and validated a composite index of social adaptability (SAI). Index of social adaptability was calculated based on employment, education, income, marital status, and substance abuse, each factor contributing from 0 to 3 points. Index of social adaptability was validated in NHANES-3 by association with all-cause and cause-specific mortality. Weighted analysis of 19,593 subjects demonstrated mean SAI of 8.29 (95% CI 8.17-8.40). Index of social adaptability was higher in Whites, followed by Mexican-Americans and then the African-American population (ANOVA, p < 0.001). The SAI was higher in subjects living in metropolitan compared to rural areas (T-test, p < 0.001), and was greater in men than in women (T-test, p < 0.001). In Cox models adjusted for age, comorbidity index, BMI, race, sex, geographic location, hemoglobin, serum creatinine, albumin, cholesterol, and glycated hemoglobin levels, SAI was inversely associated with mortality (HR 0.87 per point, 95% CI 0.84-0.90, p < 0.001). This association was confirmed in subgroups. We proposed and validated an indicator of social adaptability with a strong association with mortality, which can be used to identify underprivileged populations at risk of death.

The predictive effect of inflammatory markers and lipid accumulation product index on clinical symptoms associated with polycystic ovary syndrome in nonobese adolescents and younger aged women.

PubMed

Tola, Esra Nur; Yalcin, Serenat Eris; Dugan, Nadiye

2017-07-01

The aim of our study is to analyse the inflammatory markers and lipid accumulation product (LAP) index in nonobese adolescents and younger aged women with polycystic ovary syndrome (PCOS) compared with age and body mass index (BMI)-matched healthy controls and to determine whether the investigated parameters are potential markers for the etiopathogenesis of PCOS. We also aim to determine whether these inflammatory markers are predictive for developing some clinical implications, such as cardiovascular disease (CVD) and insulin resistance (IR), associated with PCOS. A total of 34 adolescents and younger aged females with PCOS, and 33 age and BMI-matched healthy controls were recruited for our study. All participants were nonobese (BMI<25). Neopterin (NEO), C-reactive protein (CRP) levels and complete blood parameters were assessed. LAP index and homeostasis model assessment of IR (HOMA-IR) were calculated; anthropometric, clinical and biochemical parameters were also recorded. Serum NEO, CRP levels and LAP index were significantly increased in nonobese adolescents and younger aged females with PCOS compared to healthy controls. We could not found any predictive effect of investigated inflammatory markers and LAP index on CVD risk among PCOS patients after adjustment for abdominal obesity. We also found a positive predictive effect of WBC and a negative predictive effect of lymphocytes on IR in PCOS patients after adjustment for abdominal obesity. We did not find any predictor effect of NEO on IR, but it was a positive predictive marker for an elevated HOMA-IR index. Elevated NEO, CRP levels and LAP index could have potential roles in the etiopathogenesis of PCOS in nonobese adolescents and younger aged females,NEO could be a predictive marker for elevated HOMA-IR index, and WBC and lymphocytes could be predictive for the development of IR among nonobese adolescents and younger aged females with PCOS. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluation of Multispectral Based Radiative Transfer Model Inversion to Estimate Leaf Area Index in Wheat

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is a critical variable for predicting the growth and productivity of crops. Remote sensing estimates of LAI have relied upon empirical relationships between spectral vegetation indices and ground measurements that are costly to obtain. Radiative transfer model inversion based o...

Prediction of Maximal Oxygen Uptake by Six-Minute Walk Test and Body Mass Index in Healthy Boys.

PubMed

Jalili, Majid; Nazem, Farzad; Sazvar, Akbar; Ranjbar, Kamal

2018-05-14

To develop an equation to predict maximal oxygen uptake (VO2max) based on the 6-minute walk test (6MWT) and body composition in healthy boys. Direct VO2max, 6-minute walk distance, and anthropometric characteristics were measured in 349 healthy boys (12.49 ± 2.72 years). Multiple regression analysis was used to generate VO2max prediction equations. Cross-validation of the VO2max prediction equations was assessed with predicted residual sum of squares statistics. Pearson correlation was used to assess the correlation between measured and predicted VO2max. Objectively measured VO2max had a significant correlation with demographic and 6MWT characteristics (R = 0.11-0.723, P < .01). Multiple regression analysis revealed the following VO2max prediction equation: VO2max (mL/kg/min) = 12.701 + (0.06 × 6-minute walk distance m ) - (0.732 × body mass index kg/m2 ) (R 2 = 0.79, standard error of the estimate [SEE] = 2.91 mL/kg/min, %SEE = 6.9%). There was strong correlation between measured and predicted VO2max (r = 0.875, P < .001). Cross-validation revealed minimal shrinkage (R 2 p = 0.78 and predicted residual sum of squares SEE = 2.99 mL/kg/min). This study provides a relatively accurate and convenient VO2max prediction equation based on the 6MWT and body mass index in healthy boys. This model can be used for evaluation of cardiorespiratory fitness of boys in different settings. Copyright © 2018 Elsevier Inc. All rights reserved.

[Prediction of mathematics achievement: effect of personal, socioeducational and contextual variables].

PubMed

Rosário, Pedro; Lourenço, Abílio; Paiva, Olímpia; Rodrigues, Adriana; Valle, Antonio; Tuero-Herrero, Ellián

2012-05-01

Based upon the self-regulated learning theoretical framework this study examined to what extent students' Math school achievement (fifth to ninth graders from compulsory education) can be explained by different cognitive-motivational, social, educational, and contextual variables. A sample of 571 students (10 to 15 year old) enrolled in the study. Findings suggest that Math achievement can be predicted by self-efficacy in Math, school success and self-regulated learning and that these same variables can be explained by other motivational (ej., achievement goals) and contextual variables (school disruption) stressing this way the main importance of self-regulated learning processes and the role context can play in the promotion of school success. The educational implications of the results to the school levels taken are also discussed in the present paper.

Response-Guided Community Detection: Application to Climate Index Discovery

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

Bello, Gonzalo; Angus, Michael; Pedemane, Navya

Discovering climate indices-time series that summarize spatiotemporal climate patterns-is a key task in the climate science domain. In this work, we approach this task as a problem of response-guided community detection; that is, identifying communities in a graph associated with a response variable of interest. To this end, we propose a general strategy for response-guided community detection that explicitly incorporates information of the response variable during the community detection process, and introduce a graph representation of spatiotemporal data that leverages information from multiple variables. We apply our proposed methodology to the discovery of climate indices associated with seasonal rainfall variability.more » Our results suggest that our methodology is able to capture the underlying patterns known to be associated with the response variable of interest and to improve its predictability compared to existing methodologies for data-driven climate index discovery and official forecasts.« less

Variable classifications of glycemic index determined by glucose meters.

PubMed

Lin, Meng-Hsueh Amanda; Wu, Ming-Chang; Lin, Jenshinn

2010-07-01

THE STUDY EVALUATED AND COMPARED THE DIFFERENCES OF GLUCOSE RESPONSES, INCREMENTAL AREA UNDER CURVE (IAUC), GLYCEMIC INDEX (GI) AND THE CLASSIFICATION OF GI VALUES BETWEEN MEASURED BY BIOCHEMICAL ANALYZER (FUJI AUTOMATIC BIOCHEMISTRY ANALYZER (FAA)) AND THREE GLUCOSE METERS: Accue Chek Advantage (AGM), BREEZE 2 (BGM), and Optimum Xceed (OGM). Ten healthy subjects were recruited for the study. The results showed OGM yield highest postprandial glucose responses of 119.6 +/- 1.5, followed by FAA, 118.4 +/- 1.2, BGM, 117.4 +/- 1.4 and AGM, 112.6 +/- 1.3 mg/dl respectively. FAA reached highest mean IAUC of 4156 +/- 208 mg x min/dl, followed by OGM (3835 +/- 270 mg x min/dl), BGM (3730 +/- 241 mg x min/dl) and AGM (3394 +/- 253 mg x min/dl). Among four methods, OGM produced highest mean GI value than FAA (87 +/- 5) than FAA, followed by BGM and AGM (77 +/- 1, 68 +/- 4 and 63 +/- 5, p<0.05). The results suggested that the AGM, BGM and OGM are more variable methods to determine IAUC, GI and rank GI value of food than FAA. The present result does not necessarily apply to other glucose meters. The performance of glucose meter to determine GI value of food should be evaluated and calibrated before use.

Development of model for prediction of Leachate Pollution Index (LPI) in absence of leachate parameters.

PubMed

Lothe, Anjali G; Sinha, Alok

2017-05-01

Leachate pollution index (LPI) is an environmental index which quantifies the pollution potential of leachate generated in landfill site. Calculation of Leachate pollution index (LPI) is based on concentration of 18 parameters present in leachate. However, in case of non-availability of all 18 parameters evaluation of actual values of LPI becomes difficult. In this study, a model has been developed to predict the actual values of LPI in case of partial availability of parameters. This model generates eleven equations that helps in determination of upper and lower limit of LPI. The geometric mean of these two values results in LPI value. Application of this model to three landfill site results in LPI value with an error of ±20% for ∑ i n w i ⩾0.6. Copyright © 2016 Elsevier Ltd. All rights reserved.

Discriminability measures for predicting readability of text on textured backgrounds

NASA Technical Reports Server (NTRS)

Scharff, L. F.; Hill, A. L.; Ahumada, A. J. Jr; Watson, A. B. (Principal Investigator)

2000-01-01

Several discriminability measures were examined for their ability to predict reading search times for three levels of text contrast and a range of backgrounds (plain, a periodic texture, and four spatial-frequency-filtered textures created from the periodic texture). Search times indicate that these background variations only affect readability when the text contrast is low, and that spatial frequency content of the background affects readability. These results were not well predicted by the single variables of text contrast (Spearman rank correlation = -0.64) and background RMS contrast (0.08), but a global masking index and a spatial-frequency-selective masking index led to better predictions (-0.84 and -0.81, respectively). c2000 Optical Society of America.

Experimental comparison between speech transmission index, rapid speech transmission index, and speech intelligibility index.

PubMed

Larm, Petra; Hongisto, Valtteri

2006-02-01

During the acoustical design of, e.g., auditoria or open-plan offices, it is important to know how speech can be perceived in various parts of the room. Different objective methods have been developed to measure and predict speech intelligibility, and these have been extensively used in various spaces. In this study, two such methods were compared, the speech transmission index (STI) and the speech intelligibility index (SII). Also the simplification of the STI, the room acoustics speech transmission index (RASTI), was considered. These quantities are all based on determining an apparent speech-to-noise ratio on selected frequency bands and summing them using a specific weighting. For comparison, some data were needed on the possible differences of these methods resulting from the calculation scheme and also measuring equipment. Their prediction accuracy was also of interest. Measurements were made in a laboratory having adjustable noise level and absorption, and in a real auditorium. It was found that the measurement equipment, especially the selection of the loudspeaker, can greatly affect the accuracy of the results. The prediction accuracy of the RASTI was found acceptable, if the input values for the prediction are accurately known, even though the studied space was not ideally diffuse.

PREDICTING ADHERENCE TO TREATMENT FOR METHAMPHETAMINE DEPENDENCE FROM NEUROPSYCHOLOGICAL AND DRUG USE VARIABLES*

PubMed Central

Dean, Andy C.; London, Edythe D.; Sugar, Catherine A.; Kitchen, Christina M. R.; Swanson, Aimee-Noelle; Heinzerling, Keith G.; Kalechstein, Ari D.; Shoptaw, Steven

2009-01-01

Although some individuals who abuse methamphetamine have considerable cognitive deficits, no prior studies have examined whether neurocognitive functioning is associated with outcome of treatment for methamphetamine dependence. In an outpatient clinical trial of bupropion combined with cognitive behavioral therapy and contingency management (Shoptaw et al., 2008), 60 methamphetamine-dependent adults completed three tests of reaction time and working memory at baseline. Other variables that were collected at baseline included measures of drug use, mood/psychiatric functioning, employment, social context, legal status, and medical status. We evaluated the relative predictive value of all baseline measures for treatment outcome using Classification and Regression Trees (CART; Breiman, 1984), a nonparametric statistical technique that produces easily interpretable decision rules for classifying subjects that are particularly useful in clinical settings. Outcome measures were whether or not a participant completed the trial and whether or not most urine tests showed abstinence from methamphetamine abuse. Urine-verified methamphetamine abuse at the beginning of the study was the strongest predictor of treatment outcome; two psychosocial measures (e.g., nicotine dependence and Global Assessment of Functioning) also offered some predictive value. A few reaction time and working memory variables were related to treatment outcome, but these cognitive measures did not significantly aid prediction after adjusting for methamphetamine usage at the beginning of the study. On the basis of these findings, we recommend that research groups seeking to identify new predictors of treatment outcome compare the predictors to methamphetamine usage variables to assure that unique predictive power is attained. PMID:19608354

Using a Budyko Derived Index to Evaluate the Internal Hydrological Variability of Catchments in Complex Terrain

NASA Astrophysics Data System (ADS)

Dominguez, M.

2017-12-01

Headwater catchments in complex terrain typically exhibit significant variations in microclimatic conditions across slopes. This microclimatic variability in turn, modifies land surface properties presumably altering the hydrologic dynamics of these catchments. The extent to which differences in microclimate and land cover dictate the partition of water and energy fluxes within a catchment is still poorly understood. In this study, we attempt to do an assessment of the effects of aspect, elevation and latitude (which are the principal factors that define microclimate conditions) on the hydrologic behavior of the hillslopes within catchments with complex terrain. Using a distributed hydrologic model on a number of catchments at different latitudes, where data is available for calibration and validation, we estimate the different components of the water balance to obtain the aridity index (AI = PET/P) and the evaporative index (EI = AET/P) of each slope for a number of years. We use Budyko's curve as a framework to characterize the inter-annual variability in the hydrologic response of the hillslopes in the studied catchments, developing a hydrologic sensitivity index (HSi) based on the relative change in Budyko's curve components (HSi=ΔAI/ΔEI). With this method, when the HSi values of a given hillslope are larger than 1 the hydrologic behavior of that part of the catchment is considered sensitive to changes in climatic conditions, while values approaching 0 would indicate the opposite. We use this approach as a diagnostic tool to discern the effect of aspect, elevation, and latitude on the hydrologic regime of the slopes in complex terrain catchments and to try to explain observed patterns of land cover conditions on these types of catchments.

Can transient elastography, Fib-4, Forns Index, and Lok Score predict esophageal varices in HCV-related cirrhotic patients?

PubMed

Hassan, Eman M; Omran, Dalia A; El Beshlawey, Mohamad L; Abdo, Mahmoud; El Askary, Ahmad

2014-02-01

Gastroesophageal varices are present in approximately 50% of patients with liver cirrhosis. The aim of this study was to evaluate liver stiffness measurement (LSM), Fib-4, Forns Index and Lok Score as noninvasive predictors of esophageal varices (EV). This prospective study included 65 patients with HCV-related liver cirrhosis. All patients underwent routine laboratory tests, transient elastograhy (TE) and esophagogastroduodenoscopy. FIB-4, Forns Index and Lok Score were calculated. The diagnostic performances of these methods were assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy and receiver operating characteristic curves. All predictors (LSM, FIB-4, Forns Index and Lok Score) demonstrated statistically significant correlation with the presence and the grade of EV. TE could diagnose EV at a cutoff value of 18.2kPa. Fib-4, Forns Index, and Lok Score could diagnose EV at cutoff values of 2.8, 6.61 and 0.63, respectively. For prediction of large varices (grade 2, 3), LSM showed the highest accuracy (80%) with a cutoff of 22.4kPa and AUROC of 0.801. Its sensitivity was 84%, specificity 72%, PPV 84% and NPV 72%. The diagnostic accuracies of FIB-4, Forns Index and Lok Score were 70%, 70% and76%, respectively, at cutoffs of 3.3, 6.9 and 0.7, respectively. For diagnosis of large esophageal varices, adding TE to each of the other diagnostic indices (serum fibrosis scores) increased their sensitivities with little decrease in their specificities. Moreover, this combination decreased the LR- in all tests. Noninvasive predictors can restrict endoscopic screening. This is very important as non invasiveness is now a major goal in hepatology. Copyright © 2013 Elsevier España, S.L. and AEEH y AEG. All rights reserved.

Predicting Reading Growth with Event-Related Potentials: Thinking Differently about Indexing “Responsiveness”

PubMed Central

Lemons, Christopher J.; Key, Alexandra P.F.; Fuchs, Douglas; Yoder, Paul J.; Fuchs, Lynn S.; Compton, Donald L.; Williams, Susan M.; Bouton, Bobette

2009-01-01

The purpose of this study was to determine if event-related potential (ERP) data collected during three reading-related tasks (Letter Sound Matching, Nonword Rhyming, and Nonword Reading) could be used to predict short-term reading growth on a curriculum-based measure of word identification fluency over 19 weeks in a sample of 29 first-grade children. Results indicate that ERP responses to the Letter Sound Matching task were predictive of reading change and remained so after controlling for two previously validated behavioral predictors of reading, Rapid Letter Naming and Segmenting. ERP data for the other tasks were not correlated with reading change. The potential for cognitive neuroscience to enhance current methods of indexing responsiveness in a response-to-intervention (RTI) model is discussed. PMID:20514353

Patterns and predictability in the intra-annual organic carbon variability across the boreal and hemiboreal landscape

USGS Publications Warehouse

Hytteborn, Julia K.; Temnerud, Johan; Alexander, Richard B.; Boyer, Elizabeth W.; Futter, Martyn N.; Fröberg, Mats; Dahné, Joel; Bishop, Kevin H.

2015-01-01

Factors affecting total organic carbon (TOC) concentrations in 215 watercourses across Sweden were investigated using parameter parsimonious regression approaches to explain spatial and temporal variabilities of the TOC water quality responses. We systematically quantified the effects of discharge, seasonality, and long-term trend as factors controlling intra-annual (among year) and inter-annual (within year) variabilities of TOC by evaluating the spatial variability in model coefficients and catchment characteristics (e.g. land cover, retention time, soil type).Catchment area (0.18–47,000 km2) and land cover types (forests, agriculture and alpine terrain) are typical for the boreal and hemiboreal zones across Fennoscandia. Watercourses had at least 6 years of monthly water quality observations between 1990 and 2010. Statistically significant models (p < 0.05) describing variation of TOC in streamflow were identified in 209 of 215 watercourses with a mean Nash-Sutcliffe efficiency index of 0.44. Increasing long-term trends were observed in 149 (70%) of the watercourses, and intra-annual variation in TOC far exceeded inter-annual variation. The average influences of the discharge and seasonality terms on intra-annual variations in daily TOC concentration were 1.4 and 1.3 mg l− 1 (13 and 12% of the mean annual TOC), respectively. The average increase in TOC was 0.17 mg l− 1 year− 1 (1.6% year− 1).Multivariate regression with over 90 different catchment characteristics explained 21% of the spatial variation in the linear trend coefficient, less than 20% of the variation in the discharge coefficient and 73% of the spatial variation in mean TOC. Specific discharge, water residence time, the variance of daily precipitation, and lake area, explained 45% of the spatial variation in the amplitude of the TOC seasonality.Because the main drivers of temporal variability in TOC are seasonality and discharge, first-order estimates of the influences of

Validation of the Combined Comorbidity Index of Charlson and Elixhauser to Predict 30-Day Mortality Across ICD-9 and ICD-10.

PubMed

Simard, Marc; Sirois, Caroline; Candas, Bernard

2018-05-01

To validate and compare performance of an International Classification of Diseases, tenth revision (ICD-10) version of a combined comorbidity index merging conditions of Charlson and Elixhauser measures against individual measures in the prediction of 30-day mortality. To select a weight derivation method providing optimal performance across ICD-9 and ICD-10 coding systems. Using 2 adult population-based cohorts of patients with hospital admissions in ICD-9 (2005, n=337,367) and ICD-10 (2011, n=348,820), we validated a combined comorbidity index by predicting 30-day mortality with logistic regression. To appreciate performance of the Combined index and both individual measures, factors impacting indices performance such as population characteristics and weight derivation methods were accounted for. We applied 3 scoring methods (Van Walraven, Schneeweiss, and Charlson) and determined which provides best predictive values. Combined index [c-statistics: 0.853 (95% confidence interval: CI, 0.848-0.856)] performed better than original Charlson [0.841 (95% CI, 0.835-0.844)] or Elixhauser [0.841 (95% CI, 0.837-0.844)] measures on ICD-10 cohort. All weight derivation methods provided close high discrimination results for the Combined index (Van Walraven: 0.852, Schneeweiss: 0.851, Charlson: 0.849). Results were consistent across both coding systems. The Combined index remains valid with both ICD-9 and ICD-10 coding systems and the 3 weight derivation methods evaluated provided consistent high performance across those coding systems.

Model for End-Stage Liver Disease, Model for Liver Transplantation Survival and Donor Risk Index as predictive models of survival after liver transplantation in 1,006 patients.

PubMed

Aranzana, Elisa Maria de Camargo; Coppini, Adriana Zuolo; Ribeiro, Maurício Alves; Massarollo, Paulo Celso Bosco; Szutan, Luiz Arnaldo; Ferreira, Fabio Gonçalves

2015-06-01

Liver transplantation has not increased with the number of patients requiring this treatment, increasing deaths among those on the waiting list. Models predicting post-transplantation survival, including the Model for Liver Transplantation Survival and the Donor Risk Index, have been created. Our aim was to compare the performance of the Model for End-Stage Liver Disease, the Model for Liver Transplantation Survival and the Donor Risk Index as prognostic models for survival after liver transplantation. We retrospectively analyzed the data from 1,270 patients who received a liver transplant from a deceased donor in the state of São Paulo, Brazil, between July 2006 and July 2009. All data obtained from the Health Department of the State of São Paulo at the 15 registered transplant centers were analyzed. Patients younger than 13 years of age or with acute liver failure were excluded. The majority of the recipients had Child-Pugh class B or C cirrhosis (63.5%). Among the 1,006 patients included, 274 (27%) died. Univariate survival analysis using a Cox proportional hazards model showed hazard ratios of 1.02 and 1.43 for the Model for End-Stage Liver Disease and the Model for Liver Transplantation Survival, respectively (p<0.001). The areas under the ROC curve for the Donor Risk Index were always less than 0.5, whereas those for the Model for End-Stage Liver Disease and the Model for Liver Transplantation Survival were significantly greater than 0.5 (p<0.001). The cutoff values for the Model for End-Stage Liver Disease (≥29.5; sensitivity: 39.1%; specificity: 75.4%) and the Model for Liver Transplantation Survival (≥1.9; sensitivity 63.9%, specificity 54.5%), which were calculated using data available before liver transplantation, were good predictors of survival after liver transplantation (p<0.001). The Model for Liver Transplantation Survival displayed similar death prediction performance to that of the Model for End-Stage Liver Disease. A simpler model

A threshold-free summary index of prediction accuracy for censored time to event data.

PubMed

Yuan, Yan; Zhou, Qian M; Li, Bingying; Cai, Hengrui; Chow, Eric J; Armstrong, Gregory T

2018-05-10

Prediction performance of a risk scoring system needs to be carefully assessed before its adoption in clinical practice. Clinical preventive care often uses risk scores to screen asymptomatic population. The primary clinical interest is to predict the risk of having an event by a prespecified future time t 0 . Accuracy measures such as positive predictive values have been recommended for evaluating the predictive performance. However, for commonly used continuous or ordinal risk score systems, these measures require a subjective cutoff threshold value that dichotomizes the risk scores. The need for a cutoff value created barriers for practitioners and researchers. In this paper, we propose a threshold-free summary index of positive predictive values that accommodates time-dependent event status and competing risks. We develop a nonparametric estimator and provide an inference procedure for comparing this summary measure between 2 risk scores for censored time to event data. We conduct a simulation study to examine the finite-sample performance of the proposed estimation and inference procedures. Lastly, we illustrate the use of this measure on a real data example, comparing 2 risk score systems for predicting heart failure in childhood cancer survivors. Copyright © 2018 John Wiley & Sons, Ltd.

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

DOE PAGES

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav; ...

2016-04-07

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

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

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Assessment of the fatty liver index as an indicator of hepatic steatosis for predicting incident diabetes independently of insulin resistance in a Korean population.

PubMed

Jung, C H; Lee, W J; Hwang, J Y; Yu, J H; Shin, M S; Lee, M J; Jang, J E; Leem, J; Park, J-Y; Kim, H-K

2013-04-01

Fatty liver disease, especially non-alcoholic fatty liver disease, is considered to be the hepatic manifestation of the metabolic syndrome, both closely associated with insulin resistance. Furthermore, fatty liver disease assessed by ultrasonography is known to be a predictor of the development of Type 2 diabetes mellitus. However, it remains unclear whether fatty liver disease plays a role in the pathogenesis of Type 2 diabetes independently of insulin resistance. In this study, we investigated whether fatty liver disease assessed by the fatty liver index can predict the development of Type 2 diabetes independently of systemic insulin resistance. We examined the clinical and laboratory data of 7860 subjects without diabetes who underwent general routine health evaluations at the Asan Medical Center in 2007 and had returned for follow-up examinations in 2011. Fatty liver index was calculated using an equation that considers serum triglyceride levels, γ-glutamyltransferase, waist circumference and BMI. During a 4-year period, 457 incident diabetes cases (5.8%) were identified. The odds ratios for the development of Type 2 diabetes were significantly higher in the group with a fatty liver index ≥ 60 (fatty liver index-positive) than in the group with a fatty liver index < 20 (fatty liver index-negative) after adjusting for various confounding variables including homeostasis model assessment of insulin resistance. Odds ratios were significant regardless of the insulin resistance status at baseline. Our results suggest that fatty liver index as a simple surrogate indicator of hepatic steatosis is valuable in identifying subjects at high risk for Type 2 diabetes. In addition, fatty liver disease itself contributes to the development of Type 2 diabetes independently of systemic insulin resistance. © 2012 The Authors. Diabetic Medicine © 2012 Diabetes UK.

Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing

NASA Astrophysics Data System (ADS)

Di Biagio, Claudia; Formenti, Paola; Balkanski, Yves; Caponi, Lorenzo; Cazaunau, Mathieu; Pangui, Edouard; Journet, Emilie; Nowak, Sophie; Caquineau, Sandrine; Andreae, Meinrat O.; Kandler, Konrad; Saeed, Thuraya; Piketh, Stuart; Seibert, David; Williams, Earle; Doussin, Jean-François

2017-02-01

Modeling the interaction of dust with long-wave (LW) radiation is still a challenge because of the scarcity of information on the complex refractive index of dust from different source regions. In particular, little is known about the variability of the refractive index as a function of the dust mineralogical composition, which depends on the specific emission source, and its size distribution, which is modified during transport. As a consequence, to date, climate models and remote sensing retrievals generally use a spatially invariant and time-constant value for the dust LW refractive index. In this paper, the variability of the mineral dust LW refractive index as a function of its mineralogical composition and size distribution is explored by in situ measurements in a large smog chamber. Mineral dust aerosols were generated from 19 natural soils from 8 regions: northern Africa, the Sahel, eastern Africa and the Middle East, eastern Asia, North and South America, southern Africa, and Australia. Soil samples were selected from a total of 137 available samples in order to represent the diversity of sources from arid and semi-arid areas worldwide and to account for the heterogeneity of the soil composition at the global scale. Aerosol samples generated from soils were re-suspended in the chamber, where their LW extinction spectra (3-15 µm), size distribution, and mineralogical composition were measured. The generated aerosol exhibits a realistic size distribution and mineralogy, including both the sub- and super-micron fractions, and represents in typical atmospheric proportions the main LW-active minerals, such as clays, quartz, and calcite. The complex refractive index of the aerosol is obtained by an optical inversion based upon the measured extinction spectrum and size distribution. Results from the present study show that the imaginary LW refractive index (k) of dust varies greatly both in magnitude and spectral shape from sample to sample, reflecting the

Heritability of and mortality prediction with a longevity phenotype: the healthy aging index.

PubMed

Sanders, Jason L; Minster, Ryan L; Barmada, M Michael; Matteini, Amy M; Boudreau, Robert M; Christensen, Kaare; Mayeux, Richard; Borecki, Ingrid B; Zhang, Qunyuan; Perls, Thomas; Newman, Anne B

2014-04-01

Longevity-associated genes may modulate risk for age-related diseases and survival. The Healthy Aging Index (HAI) may be a subphenotype of longevity, which can be constructed in many studies for genetic analysis. We investigated the HAI's association with survival in the Cardiovascular Health Study and heritability in the Long Life Family Study. The HAI includes systolic blood pressure, pulmonary vital capacity, creatinine, fasting glucose, and Modified Mini-Mental Status Examination score, each scored 0, 1, or 2 using approximate tertiles and summed from 0 (healthy) to 10 (unhealthy). In Cardiovascular Health Study, the association with mortality and accuracy predicting death were determined with Cox proportional hazards analysis and c-statistics, respectively. In Long Life Family Study, heritability was determined with a variance component-based family analysis using a polygenic model. Cardiovascular Health Study participants with unhealthier index scores (7-10) had 2.62-fold (95% confidence interval: 2.22, 3.10) greater mortality than participants with healthier scores (0-2). The HAI alone predicted death moderately well (c-statistic = 0.643, 95% confidence interval: 0.626, 0.661, p < .0001) and slightly worse than age alone (c-statistic = 0.700, 95% confidence interval: 0.684, 0.717, p < .0001; p < .0001 for comparison of c-statistics). Prediction increased significantly with adjustment for demographics, health behaviors, and clinical comorbidities (c-statistic = 0.780, 95% confidence interval: 0.765, 0.794, p < .0001). In Long Life Family Study, the heritability of the HAI was 0.295 (p < .0001) overall, 0.387 (p < .0001) in probands, and 0.238 (p = .0004) in offspring. The HAI should be investigated further as a candidate phenotype for uncovering longevity-associated genes in humans.

The quantitative lung index and the prediction of survival in fetuses with congenital diaphragmatic hernia.

PubMed

Illescas, Tamara; Rodó, Carlota; Arévalo, Silvia; Giné, Carles; Peiró, José L; Carreras, Elena

2016-03-01

The lung-to-head ratio (LHR) is routinely used to select the best candidates for prenatal surgery and to follow-up the fetuses with congenital diaphragmatic hernia (CDH). Since this index is gestation-dependent, the quantitative lung index (QLI) was proposed as an alternative parameter that stays constant throughout pregnancy. Our objective was to study the performance of QLI to predict survival in fetuses with CDH. Observational retrospective study of fetuses with isolated CDH, referred to our center. LHR was originally used for the prenatal surgery evaluation. We calculated the QLI and compared the performance of both indexes (QLI and LHR) to predict survival. From January-2009 to February-2015 we followed 31 fetuses with isolated CDH. The mean QLI was 0.66 (95% CI: 0.57-0.75) for survivors and 0.41 (95% CI: 0.25-0.58) for non-survivors (p<0.01) and the mean LHR was 1.38 (95% CI: 1.17-1.60) for survivors and 0.91 (95% CI: 0.57-1.25) for non-survivors (p<0.02). All operated fetuses (n=12) had a LHR <1 and a QLI <0.5 and none of them survived when the QLI was <0.32. When separately considering the prenatal surgery status, the mean values of the QLI (but not those of the LHR) were still significantly different between survivors and non-survivors. The comparative ROC curves showed a better performance of the QLI with respect to the LHR for the prediction of survival, especially in the group of operated fetuses, although differences were not statistically significant. The QLI seems to be a better predictor for survival than the LHR, especially for the group of fetuses undergoing prenatal surgery. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

Estimation of confidence limits for descriptive indexes derived from autoregressive analysis of time series: Methods and application to heart rate variability

PubMed Central

2017-01-01

The growing interest in personalized medicine requires making inferences from descriptive indexes estimated from individual recordings of physiological signals, with statistical analyses focused on individual differences between/within subjects, rather than comparing supposedly homogeneous cohorts. To this end, methods to compute confidence limits of individual estimates of descriptive indexes are needed. This study introduces numerical methods to compute such confidence limits and perform statistical comparisons between indexes derived from autoregressive (AR) modeling of individual time series. Analytical approaches are generally not viable, because the indexes are usually nonlinear functions of the AR parameters. We exploit Monte Carlo (MC) and Bootstrap (BS) methods to reproduce the sampling distribution of the AR parameters and indexes computed from them. Here, these methods are implemented for spectral and information-theoretic indexes of heart-rate variability (HRV) estimated from AR models of heart-period time series. First, the MS and BC methods are tested in a wide range of synthetic HRV time series, showing good agreement with a gold-standard approach (i.e. multiple realizations of the "true" process driving the simulation). Then, real HRV time series measured from volunteers performing cognitive tasks are considered, documenting (i) the strong variability of confidence limits' width across recordings, (ii) the diversity of individual responses to the same task, and (iii) frequent disagreement between the cohort-average response and that of many individuals. We conclude that MC and BS methods are robust in estimating confidence limits of these AR-based indexes and thus recommended for short-term HRV analysis. Moreover, the strong inter-individual differences in the response to tasks shown by AR-based indexes evidence the need of individual-by-individual assessments of HRV features. Given their generality, MC and BS methods are promising for applications

Maximum likelihood estimation for predicting the probability of obtaining variable shortleaf pine regeneration densities

Treesearch

Thomas B. Lynch; Jean Nkouka; Michael M. Huebschmann; James M. Guldin

2003-01-01

A logistic equation is the basis for a model that predicts the probability of obtaining regeneration at specified densities. The density of regeneration (trees/ha) for which an estimate of probability is desired can be specified by means of independent variables in the model. When estimating parameters, the dependent variable is set to 1 if the regeneration density (...

Predicting Eight Grade Students' Equation Solving Performances via Concepts of Variable and Equality

ERIC Educational Resources Information Center

Ertekin, Erhan

2017-01-01

This study focused on how two algebraic concepts- equality and variable- predicted 8th grade students' equation solving performance. In this study, predictive design as a correlational research design was used. Randomly selected 407 eight-grade students who were from the central districts of a city in the central region of Turkey participated in…

Speed and Cardiac Recovery Variables Predict the Probability of Elimination in Equine Endurance Events

PubMed Central

Younes, Mohamed; Robert, Céline; Cottin, François; Barrey, Eric

2015-01-01

Nearly 50% of the horses participating in endurance events are eliminated at a veterinary examination (a vet gate). Detecting unfit horses before a health problem occurs and treatment is required is a challenge for veterinarians but is essential for improving equine welfare. We hypothesized that it would be possible to detect unfit horses earlier in the event by measuring heart rate recovery variables. Hence, the objective of the present study was to compute logistic regressions of heart rate, cardiac recovery time and average speed data recorded at the previous vet gate (n-1) and thus predict the probability of elimination during successive phases (n and following) in endurance events. Speed and heart rate data were extracted from an electronic database of endurance events (80–160 km in length) organized in four countries. Overall, 39% of the horses that started an event were eliminated—mostly due to lameness (64%) or metabolic disorders (15%). For each vet gate, logistic regressions of explanatory variables (average speed, cardiac recovery time and heart rate measured at the previous vet gate) and categorical variables (age and/or event distance) were computed to estimate the probability of elimination. The predictive logistic regressions for vet gates 2 to 5 correctly classified between 62% and 86% of the eliminated horses. The robustness of these results was confirmed by high areas under the receiving operating characteristic curves (0.68–0.84). Overall, a horse has a 70% chance of being eliminated at the next gate if its cardiac recovery time is longer than 11 min at vet gate 1 or 2, or longer than 13 min at vet gates 3 or 4. Heart rate recovery and average speed variables measured at the previous vet gate(s) enabled us to predict elimination at the following vet gate. These variables should be checked at each veterinary examination, in order to detect unfit horses as early as possible. Our predictive method may help to improve equine welfare and ethical

Speed and Cardiac Recovery Variables Predict the Probability of Elimination in Equine Endurance Events.

PubMed

Younes, Mohamed; Robert, Céline; Cottin, François; Barrey, Eric

2015-01-01

Nearly 50% of the horses participating in endurance events are eliminated at a veterinary examination (a vet gate). Detecting unfit horses before a health problem occurs and treatment is required is a challenge for veterinarians but is essential for improving equine welfare. We hypothesized that it would be possible to detect unfit horses earlier in the event by measuring heart rate recovery variables. Hence, the objective of the present study was to compute logistic regressions of heart rate, cardiac recovery time and average speed data recorded at the previous vet gate (n-1) and thus predict the probability of elimination during successive phases (n and following) in endurance events. Speed and heart rate data were extracted from an electronic database of endurance events (80-160 km in length) organized in four countries. Overall, 39% of the horses that started an event were eliminated--mostly due to lameness (64%) or metabolic disorders (15%). For each vet gate, logistic regressions of explanatory variables (average speed, cardiac recovery time and heart rate measured at the previous vet gate) and categorical variables (age and/or event distance) were computed to estimate the probability of elimination. The predictive logistic regressions for vet gates 2 to 5 correctly classified between 62% and 86% of the eliminated horses. The robustness of these results was confirmed by high areas under the receiving operating characteristic curves (0.68-0.84). Overall, a horse has a 70% chance of being eliminated at the next gate if its cardiac recovery time is longer than 11 min at vet gate 1 or 2, or longer than 13 min at vet gates 3 or 4. Heart rate recovery and average speed variables measured at the previous vet gate(s) enabled us to predict elimination at the following vet gate. These variables should be checked at each veterinary examination, in order to detect unfit horses as early as possible. Our predictive method may help to improve equine welfare and ethical

Effects of hypocaloric diets with different glycemic indexes on endothelial function and glycemic variability in overweight and in obese adult patients at increased cardiovascular risk.

PubMed

Buscemi, Silvio; Cosentino, Loretta; Rosafio, Giuseppe; Morgana, Manuela; Mattina, Alessandro; Sprini, Delia; Verga, Salvatore; Rini, Giovam Battista

2013-06-01

The role of glycemic index of the diet in glucose control and cardiovascular prevention is still not clear. The aim of this study was to determine the effects of hypocaloric diets with different glycemic indexes and glycemic loads on endothelial function and glycemic variability in nondiabetic participants at increased cardiovascular risk. Forty nondiabetic obese participants were randomly assigned to a three-month treatment with either a low glycemic index (LGI; n=19) or high glycemic index (HGI; n=21) hypocaloric diet with similar macronutrient and fiber content. Endothelial function was measured as flow-mediated dilatation (FMD) of the brachial artery before and after dieting. In addition, 48-h continuous subcutaneous glucose monitoring was done before and after dieting in a subgroup of 24 participants. The amount of weight loss after dieting was similar in both groups. The glycemic index of the diet significantly influenced the FMD (P<0.005). In particular, the change of FMD was 2.3±2.6% following the LGI diet, and -0.9±3.6% after the HGI diet (P<0.005). The mean 48-h glycemia decreased significantly after dietary treatment (P<0.05), but no significant effect of the glycemic index of the diet on results was observed. The glycemic index of the diet significantly influenced the 48-h glycemic variability measured as coefficient of variability (CV%; P<0.001). The CV% decreased after the LGI diet (from 23.5 to 20.0%) and increased after the HGI diet (from 23.6 to 26.6%). The change in percentage of FMD was inversely correlated with the change in the 48-h glycemic CV% (r=-0.45; P<0.05). Endothelial function and glycemic variability ameliorate in association with the adherence to an LGI hypocaloric diet in nondiabetic obese persons. ISRCTN56834511. Copyright © 2012 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Determination of variables in the prediction of strontium distribution coefficients for selected sediments

USGS Publications Warehouse

Pace, M.N.; Rosentreter, J.J.; Bartholomay, R.C.

2001-01-01

Idaho State University and the US Geological Survey, in cooperation with the US Department of Energy, conducted a study to determine and evaluate strontium distribution coefficients (Kds) of subsurface materials at the Idaho National Engineering and Environmental Laboratory (INEEL). The Kds were determined to aid in assessing the variability of strontium Kds and their effects on chemical transport of strontium-90 in the Snake River Plain aquifer system. Data from batch experiments done to determine strontium Kds of five sediment-infill samples and six standard reference material samples were analyzed by using multiple linear regression analysis and the stepwise variable-selection method in the statistical program, Statistical Product and Service Solutions, to derive an equation of variables that can be used to predict strontium Kds of sediment-infill samples. The sediment-infill samples were from basalt vesicles and fractures from a selected core at the INEEL; strontium Kds ranged from ???201 to 356 ml g-1. The standard material samples consisted of clay minerals and calcite. The statistical analyses of the batch-experiment results showed that the amount of strontium in the initial solution, the amount of manganese oxide in the sample material, and the amount of potassium in the initial solution are the most important variables in predicting strontium Kds of sediment-infill samples.

[Criteria for assessing severely hot environments: from the WBGT index to the PHS (predicted heat strain) model].

PubMed

d'Ambrosio, Francesca Romana; Palella, B I; Riccio, G; Alfano, G

2004-01-01

The present study deals with the main methods for assessment of hot environments: i.e., WBGT, SWreq and PHS. It is stressed how the WBGT index, which is strictly empirical, although a very practical tool for the assessment of the hot environments, can only be used for a rough evaluation of heat stress, and especially for a not very high metabolic rate (M<175 W/m2). On the contrary, the SWreq method, which is based on both subject-environment heat exchange and the effect of clothing, allows a better assessment of the work situation with a general reduction of the exposure limits with respect to WBGT, especially in non-uniform environments (ta not equal to tr). However, it should be noted that application of SWreq is required by the ISO standard 7243 when the WBGT limit values are exceeded. In this study interest was extensively focused on the "Predicted Heat Strain" method, highlighting via a special software the differences in heat stress assessment related to this new approach, which will be adopted by the ISO in the next revision of standard 7933. The PHS method, unlike SWreq, allows the prediction of the time-response of the main physiological variables of interest (i.e., skin temperature, core temperature and sweat rate). Moreover thanks to better modelling of heat exchanges, the PHS method allows account to be taken of both movement and clothing effects, resulting in even more reduced exposure.

Self-Tuning of Design Variables for Generalized Predictive Control

NASA Technical Reports Server (NTRS)

Lin, Chaung; Juang, Jer-Nan

2000-01-01

Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.

Predicting moisture dynamics of fine understory fuels in a moist tropical rainforest system: results of a pilot study undertaken to identify proxy variables useful for rating fire danger.

PubMed

Ray, David; Nepstad, Dan; Brando, Paulo

2010-08-01

*The use of fire as a land management tool in the moist tropics often has the unintended consequence of degrading adjacent forest, particularly during severe droughts. Reliable models of fire danger are needed to help mitigate these impacts. *Here, we studied the moisture dynamics of fine understory fuels in the east-central Brazilian Amazon during the 2003 dry season. Drying stations established under varying amounts of canopy cover (leaf area index (LAI) = 0 - 5.3) were subjected to a range of water inputs (5-15 mm) and models were developed to forecast litter moisture content (LMC). Predictions were then compared with independent field data. *A multiple linear regression relating litter moisture content to forest structure (LAI), ambient vapor pressure deficit (VPD(M)) and an index of elapsed time since a precipitation event (d(-1)) was identified as the best-fit model (adjusted R(2) = 0.89). Relative to the independent observations, model predictions were relatively unbiased when the LMC was predicting fire danger based on forest structure and meteorological variables is promising; however, additional information to the LAI, for example forest biomass, may be required to accurately capture the influence of forest structure on understory microclimate.

Conventional heart rate variability analysis of ambulatory electrocardiographic recordings fails to predict imminent ventricular fibrillation

NASA Technical Reports Server (NTRS)

Vybiral, T.; Glaeser, D. H.; Goldberger, A. L.; Rigney, D. R.; Hess, K. R.; Mietus, J.; Skinner, J. E.; Francis, M.; Pratt, C. M.

1993-01-01

OBJECTIVES. The purpose of this report was to study heart rate variability in Holter recordings of patients who experienced ventricular fibrillation during the recording. BACKGROUND. Decreased heart rate variability is recognized as a long-term predictor of overall and arrhythmic death after myocardial infarction. It was therefore postulated that heart rate variability would be lowest when measured immediately before ventricular fibrillation. METHODS. Conventional indexes of heart rate variability were calculated from Holter recordings of 24 patients with structural heart disease who had ventricular fibrillation during monitoring. The control group consisted of 19 patients with coronary artery disease, of comparable age and left ventricular ejection fraction, who had nonsustained ventricular tachycardia but no ventricular fibrillation. RESULTS. Heart rate variability did not differ between the two groups, and no consistent trends in heart rate variability were observed before ventricular fibrillation occurred. CONCLUSIONS. Although conventional heart rate variability is an independent long-term predictor of adverse outcome after myocardial infarction, its clinical utility as a short-term predictor of life-threatening arrhythmias remains to be elucidated.

Nuclear Division Index may Predict Neoplastic Colorectal Lesions.

PubMed

Ionescu, Mirela E; Ciocirlan, Mihai; Becheanu, Gabriel; Nicolaie, Tudor; Ditescu, Cristina; Teiusanu, Adriana G; Gologan, Serban I; Arbanas, Tudor; Diculescu, Mircea M

2011-07-01

Colorectal cancer (CRC) develops by accumulation of multiple genetic damages leading to genetic instability that can be evaluated by cytogenetic methods. In the current study we used Cytokinesis-Blocked Micronucleus Assay (CBMN) technique to assess the behavior of Nuclear Division Index(NDI) in peripheral lymphocytes of patients with CRC and polyps versus patients with normal colonoscopy. Blood samples were collected from patients after informed consent. By CBMN technique we assessed the proportion of mono-nucleated, bi-nucleated, tri-nucleated and tetra-nucleated cells/500 cells, to calculate NDI. Data were statistically analyzed using the SPSS 11.0 package. 45 patients were available for analysis, 23 men and 22 women, with a mean age of 58.7±13.5. 17 had normal colonoscopy, 17 colonic polyps and 11 CRC. The mean NDI values were significantly smaller for patients with CRC or polyps than in patients with normal colonoscopy (1.57 vs 1.73, p=0.013). The difference persisted for patients with neoplastic lesions (adenomas and carcinomas) when compared with patients with normal colonoscopy or non neoplastic (hyperplastic) polyps (1.56 vs.1.71, p=0.018). The NDI cut-off value to predict the presence of adenomas or carcinomas was equal to 1.55 with a 54.2% sensitivity and 81% specificity of lower values (p=0.019). The NDI cut off value to predict the presence of advanced adenomas or cancer was 1.525 for a sensitivity of 56.3% and a specificity of 82.8% (p=0.048). NDI may be useful in screening strategies for colorectal cancer as simple, noninvasive, inexpensive cytogenetic biomarker.

Nuclear Division Index may Predict Neoplastic Colorectal Lesions

PubMed Central

IONESCU, Mirela E.; CIOCIRLAN, Mihai; BECHEANU, Gabriel; NICOLAIE, Tudor; DITESCU, Cristina; TEIUSANU, Adriana G.; GOLOGAN, Serban I.; ARBANAS, Tudor; DICULESCU, Mircea M.

2011-01-01

ABSTRACT Background: Colorectal cancer (CRC) develops by accumulation of multiple genetic damages leading to genetic instability that can be evaluated by cytogenetic methods. In the current study we used Cytokinesis-Blocked Micronucleus Assay (CBMN) technique to assess the behavior of Nuclear Division Index(NDI) in peripheral lymphocytes of patients with CRC and polyps versus patients with normal colonoscopy. Methods: Blood samples were collected from patients after informed consent. By CBMN technique we assessed the proportion of mono-nucleated, bi-nucleated, tri-nucleated and tetra-nucleated cells/500 cells, to calculate NDI. Data were statistically analyzed using the SPSS 11.0 package. Results: 45 patients were available for analysis, 23 men and 22 women, with a mean age of 58.7±13.5. 17 had normal colonoscopy, 17 colonic polyps and 11 CRC. The mean NDI values were significantly smaller for patients with CRC or polyps than in patients with normal colonoscopy (1.57 vs 1.73, p=0.013). The difference persisted for patients with neoplastic lesions (adenomas and carcinomas) when compared with patients with normal colonoscopy or non neoplastic (hyperplastic) polyps (1.56 vs.1.71, p=0.018). The NDI cut-off value to predict the presence of adenomas or carcinomas was equal to 1.55 with a 54.2% sensitivity and 81% specificity of lower values (p=0.019). The NDI cut off value to predict the presence of advanced adenomas or cancer was 1.525 for a sensitivity of 56.3% and a specificity of 82.8% (p=0.048). Conclusion: NDI may be useful in screening strategies for colorectal cancer as simple, noninvasive, inexpensive cytogenetic biomarker. PMID:22368693

Projections of the 21st Century Freezing/Thawing Index in the Northern Hemisphere

NASA Astrophysics Data System (ADS)

Frauenfeld, O. W.; Zhang, T.; Teng, H.; Etringer, A. J.

2006-12-01

Variability in the ground thermal regime in high-latitude cold regions has important ramifications for surface and subsurface hydrology, carbon exchange, the surface energy and moisture balance, and ecosystem diversity and productivity. However, assessing these variations, particularly in light of reported widespread atmospheric and terrestrial changes over recent decades, remains a challenge due to the sparse observing networks in high latitudes. The annual freezing/thawing (F/T) index can be used to predict and map permafrost and seasonally frozen ground distribution, active layer and seasonal freeze depths, and has important engineering applications, thereby providing important information on climate variability in cold regions. Reliable long-term measurements of the F/T index are thus important variables for understanding and predicting high-latitude climate processes. The F/T index is defined as the cumulative number of degree-days below/above 0°C for a given time period. However, in recent work we have established that long- term monthly air temperature measurements can be used very reliably to approximate the annual F/T index. This has enabled us to produce a 25-km gridded Northern Hemisphere annual F/T index data set for 1901-2002 (see http://nsidc.org/data/ggd649.html). In this current effort we employ model projections of surface air temperatures from the Intergovernmental Panel on Climate Change (IPPC) Fourth Assessment Report (AR4) to provide an estimate of 21st century F/T index changes. This will provide an important analog to recent work on trying to establish near-surface permafrost changes in the Arctic. We will make use of runs for the four emission scenarios ("commit," "SRESA2," "SRESA1B," and "SRESB1") and "20th Century Climate in Coupled Models" (20c3m) from all 16 available models, as well as a multi-model ensemble. We first perform a comparison between our existing historical data base of F/T indices and the overlapping period of the IPCC

Ratio index variables or ANCOVA? Fisher's cats revisited.

PubMed

Tu, Yu-Kang; Law, Graham R; Ellison, George T H; Gilthorpe, Mark S

2010-01-01

Over 60 years ago Ronald Fisher demonstrated a number of potential pitfalls with statistical analyses using ratio variables. Nonetheless, these pitfalls are largely overlooked in contemporary clinical and epidemiological research, which routinely uses ratio variables in statistical analyses. This article aims to demonstrate how very different findings can be generated as a result of less than perfect correlations among the data used to generate ratio variables. These imperfect correlations result from measurement error and random biological variation. While the former can often be reduced by improvements in measurement, random biological variation is difficult to estimate and eliminate in observational studies. Moreover, wherever the underlying biological relationships among epidemiological variables are unclear, and hence the choice of statistical model is also unclear, the different findings generated by different analytical strategies can lead to contradictory conclusions. Caution is therefore required when interpreting analyses of ratio variables whenever the underlying biological relationships among the variables involved are unspecified or unclear. (c) 2009 John Wiley & Sons, Ltd.

Development of a wound healing index for patients with chronic wounds.

PubMed

Horn, Susan D; Fife, Caroline E; Smout, Randall J; Barrett, Ryan S; Thomson, Brett

2013-01-01

Randomized controlled trials in wound care generalize poorly because they exclude patients with significant comorbid conditions. Research using real-world wound care patients is hindered by lack of validated methods to stratify patients according to severity of underlying illnesses. We developed a comprehensive stratification system for patients with wounds that predicts healing likelihood. Complete medical record data on 50,967 wounds from the United States Wound Registry were assigned a clear outcome (healed, amputated, etc.). Factors known to be associated with healing were evaluated using logistic regression models. Significant variables (p < 0.05) were determined and subsequently tested on a holdout sample of data. A different model predicted healing for each wound type. Some variables predicted significantly in nearly all models: wound size, wound age, number of wounds, evidence of bioburden, tissue type exposed (Wagner grade or stage), being nonambulatory, and requiring hospitalization during the course of care. Variables significant in some models included renal failure, renal transplant, malnutrition, autoimmune disease, and cardiovascular disease. All models validated well when applied to the holdout sample. The "Wound Healing Index" can validly predict likelihood of wound healing among real-world patients and can facilitate comparative effectiveness research to identify patients needing advanced therapeutics. © 2013 by the Wound Healing Society.

A Risk Prediction Index for Advanced Colorectal Neoplasia at Screening Colonoscopy.

PubMed

Schroy, Paul C; Wong, John B; O'Brien, Michael J; Chen, Clara A; Griffith, John L

2015-07-01

Eliciting patient preferences within the context of shared decision making has been advocated for colorectal cancer screening. Risk stratification for advanced colorectal neoplasia (ACN) might facilitate more effective shared decision making when selecting an appropriate screening option. Our objective was to develop and validate a clinical index for estimating the probability of ACN at screening colonoscopy. We conducted a cross-sectional analysis of 3,543 asymptomatic, mostly average-risk patients 50-79 years of age undergoing screening colonoscopy at two urban safety net hospitals. Predictors of ACN were identified using multiple logistic regression. Model performance was internally validated using bootstrapping methods. The final index consisted of five independent predictors of risk (age, smoking, alcohol intake, height, and a combined sex/race/ethnicity variable). Smoking was the strongest predictor (net reclassification improvement (NRI), 8.4%) and height the weakest (NRI, 1.5%). Using a simplified weighted scoring system based on 0.5 increments of the adjusted odds ratio, the risk of ACN ranged from 3.2% (95% confidence interval (CI), 2.6-3.9) for the low-risk group (score ≤2) to 8.6% (95% CI, 7.4-9.7) for the intermediate/high-risk group (score 3-11). The model had moderate to good overall discrimination (C-statistic, 0.69; 95% CI, 0.66-0.72) and good calibration (P=0.73-0.93). A simple 5-item risk index based on readily available clinical data accurately stratifies average-risk patients into low- and intermediate/high-risk categories for ACN at screening colonoscopy. Uptake into clinical practice could facilitate more effective shared decision-making for CRC screening, particularly in situations where patient and provider test preferences differ.

Comparison Between the Four-kallikrein Panel and Prostate Health Index for Predicting Prostate Cancer.

PubMed

Nordström, Tobias; Vickers, Andrew; Assel, Melissa; Lilja, Hans; Grönberg, Henrik; Eklund, Martin

2015-07-01

The four-kallikrein panel and the Prostate Health Index (PHI) have been shown to improve prediction of prostate cancer (PCa) compared with prostate-specific antigen (PSA). No comparison of the four-kallikrein panel and PHI has been presented. To compare the four-kallikrein panel and PHI for predicting PCa in an independent cohort. Participants were from a population-based cohort of PSA-tested men in Stockholm County. We included 531 men with PSA levels between 3 and 15 ng/ml undergoing first-time prostate biopsy during 2010-2012. Models were fitted to case status. We computed calibration curves, the area under the receiver-operating characteristics curve (AUC), decision curves, and percentage of saved biopsies. The four-kallikrein panel showed AUCs of 69.0 when predicting any-grade PCa and 71.8 when predicting high-grade cancer (Gleason score ≥7). Similar values were found for PHI: 70.4 and 71.1, respectively. Both models had higher AUCs than a base model with PSA value and age (p<0.0001 for both); differences between models were not significant. Sensitivity analyses including men with any PSA level or a previous biopsy did not materially affect our findings. Using 10% predicted risk of high-grade PCa by the four-kallikrein panel or PHI of 39 as cut-off for biopsy saved 29% of performed biopsies at a cost of delayed diagnosis for 10% of the men with high-grade cancers. Both models showed limited net benefit in decision analysis. The main study limitation was lack of digital rectal examination data and biopsy decision being based on PSA information. The four-kallikrein panel and PHI similarly improved discrimination when predicting PCa and high-grade PCa. Both are simple blood tests that can reduce the number of unnecessary biopsies compared with screening with total PSA, representing an important new option to reduce harm. Prostate-specific antigen screening is controversial due to limitations of the test. We found that two blood tests, the Prostate Health Index

Doppler echocardiographic myocardial stunning index predicts recovery of left ventricular systolic function after primary percutaneous coronary intervention.

PubMed

Sharif, Dawod; Matanis, Wisam; Sharif-Rasslan, Amal; Rosenschein, Uri

2016-10-01

Myocardial stunning is responsible for partially reversible left ventricular (LV) systolic dysfunction after successful primary percutaneous coronary intervention (PPCI) in patients with acute ST-elevation myocardial infarction (STEMI). To test the hypothesis that early coronary blood flow (CBF) to LV systolic function ratios, as an equivalent to LV stunning index (SI), predict recovery of LV systolic function after PPCI in patients with acute STEMI. Twenty-four patients with acute anterior STEMI who had successful PPCI were evaluated and compared to 96 control subjects. Transthoracic echocardiography with measurement of LV ejection fraction (EF), LV, and left anterior descending (LAD) coronary artery area wall-motion score index (WMSI) as well as Doppler sampling of LAD blood velocities, early after PPCI and 5 days later, were performed. SI was evaluated as the early ratio of CBF parameters in the LAD to LV systolic function parameters. Early SI-LVEF well predicted late LVEF (r=.51, P<.01) and the change in LVEF (r=.48, P<.017). Early SI-LVMSI predicted well late LVEF (r=.56, P<.006) and the change in LVEF (r=.46, P<.028). Early SI-LADWMSI predicted late LVEF (r=.44, P<.028). Other SI indices measured as other LAD-CBF to LV systolic function parameters were not predictive of late LV systolic function. LV stunning indices measured as early LAD flow to LVEF, LVWMSI, and LADWMSI ratios well predicted late LVEF and the change in LVEF. Thus, greater early coronary artery flow to LV systolic function parameter ratios predict a better improvement in late LV systolic function after PPCI. © 2016, Wiley Periodicals, Inc.

Using Baidu Search Index to Predict Dengue Outbreak in China

NASA Astrophysics Data System (ADS)

Liu, Kangkang; Wang, Tao; Yang, Zhicong; Huang, Xiaodong; Milinovich, Gabriel J.; Lu, Yi; Jing, Qinlong; Xia, Yao; Zhao, Zhengyang; Yang, Yang; Tong, Shilu; Hu, Wenbiao; Lu, Jiahai

2016-12-01

This study identified the possible threshold to predict dengue fever (DF) outbreaks using Baidu Search Index (BSI). Time-series classification and regression tree models based on BSI were used to develop a predictive model for DF outbreak in Guangzhou and Zhongshan, China. In the regression tree models, the mean autochthonous DF incidence rate increased approximately 30-fold in Guangzhou when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 382. When the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 91.8, there was approximately 9-fold increase of the mean autochthonous DF incidence rate in Zhongshan. In the classification tree models, the results showed that when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 99.3, there was 89.28% chance of DF outbreak in Guangzhou, while, in Zhongshan, when the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 68.1, the chance of DF outbreak rose up to 100%. The study indicated that less cost internet-based surveillance systems can be the valuable complement to traditional DF surveillance in China.

The Biodiversity Informatics Potential Index

PubMed Central

2011-01-01

Background Biodiversity informatics is a relatively new discipline extending computer science in the context of biodiversity data, and its development to date has not been uniform throughout the world. Digitizing effort and capacity building are costly, and ways should be found to prioritize them rationally. The proposed 'Biodiversity Informatics Potential (BIP) Index' seeks to fulfill such a prioritization role. We propose that the potential for biodiversity informatics be assessed through three concepts: (a) the intrinsic biodiversity potential (the biological richness or ecological diversity) of a country; (b) the capacity of the country to generate biodiversity data records; and (c) the availability of technical infrastructure in a country for managing and publishing such records. Methods Broadly, the techniques used to construct the BIP Index were rank correlation, multiple regression analysis, principal components analysis and optimization by linear programming. We built the BIP Index by finding a parsimonious set of country-level human, economic and environmental variables that best predicted the availability of primary biodiversity data accessible through the Global Biodiversity Information Facility (GBIF) network, and constructing an optimized model with these variables. The model was then applied to all countries for which sufficient data existed, to obtain a score for each country. Countries were ranked according to that score. Results Many of the current GBIF participants ranked highly in the BIP Index, although some of them seemed not to have realized their biodiversity informatics potential. The BIP Index attributed low ranking to most non-participant countries; however, a few of them scored highly, suggesting that these would be high-return new participants if encouraged to contribute towards the GBIF mission of free and open access to biodiversity data. Conclusions The BIP Index could potentially help in (a) identifying countries most likely to

The Heat Exposure Integrated Deprivation Index (HEIDI): A data-driven approach to quantifying neighborhood risk during extreme hot weather.

PubMed

Krstic, Nikolas; Yuchi, Weiran; Ho, Hung Chak; Walker, Blake B; Knudby, Anders J; Henderson, Sarah B

2017-12-01

Mortality attributable to extreme hot weather is a growing concern in many urban environments, and spatial heat vulnerability indexes are often used to identify areas at relatively higher and lower risk. Three indexes were developed for greater Vancouver, Canada using a pool of 20 potentially predictive variables categorized to reflect social vulnerability, population density, temperature exposure, and urban form. One variable was chosen from each category: an existing deprivation index, senior population density, apparent temperature, and road density, respectively. The three indexes were constructed from these variables using (1) unweighted, (2) weighted, and (3) data-driven Heat Exposure Integrated Deprivation Index (HEIDI) approaches. The performance of each index was assessed using mortality data from 1998-2014, and the maps were compared with respect to spatial patterns identified. The population-weighted spatial correlation between the three indexes ranged from 0.68-0.89. The HEIDI approach produced a graduated map of vulnerability, whereas the other approaches primarily identified areas of highest risk. All indexes performed best under extreme temperatures, but HEIDI was more useful at lower thresholds. Each of the indexes in isolation provides valuable information for public health protection, but combining the HEIDI approach with unweighted and weighted methods provides richer information about areas most vulnerable to heat. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Predicting Vegetation Condition from ASCAT Soil Water Index over Southwest India

NASA Astrophysics Data System (ADS)

Pfeil, Isabella Maria; Hochstöger, Simon; Amarnath, Giriraj; Pani, Peejush; Enenkel, Markus; Wagner, Wolfgang

2017-04-01

In India, extreme water scarcity events are expected to occur on average every five years. Record-breaking droughts affecting millions of human beings and livestock are common. If the south-west monsoon (summer monsoon) is delayed or brings less rainfall than expected, a season's harvest can be destroyed despite optimal farm management, leading to, in the worst case, life-threatening circumstances for a large number of farmers. Therefore, the monitoring of key drought indicators, such as the healthiness of the vegetation, and subsequent early warning is crucial. The aim of this work is to predict vegetation state from earth observation data instead of relying on models which need a lot of input data, increasing the complexity of error propagation, or seasonal forecasts, that are often too uncertain to be used as a regression component for a vegetation parameter. While precipitation is the main water supply for large parts of India's agricultural areas, vegetation datasets such as the Normalized Difference Vegetation Index (NDVI) provide reliable estimates of vegetation greenness that can be related to vegetation health. Satellite-derived soil moisture represents the missing link between a deficit in rainfall and the response of vegetation. In particular the water available in the root zone plays an important role for near-future vegetation health. Exploiting the added-value of root zone soil moisture is therefore crucial, and its use in vegetation studies presents an added value for drought analyses and decision-support. The soil water index (SWI) dataset derived from the Advanced Scatterometer (ASCAT) on board the Metop satellites represents the water content that is available in the root zone. This dataset shows a strong correlation with NDVI data obtained from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS), which is exploited in this study. A linear regression function is fit to the multi-year SWI and NDVI dataset with a temporal

A novel structural risk index for primary spontaneous pneumothorax: Ankara Numune Risk Index.

PubMed

Akkas, Yucel; Peri, Neslihan Gulay; Kocer, Bulent; Kaplan, Tevfik; Alhan, Aslihan

2017-07-01

In this study, we aimed to reveal a novel risk index as a structural risk marker for primary spontanoeus pneumothorax using body mass index and chest height, structural risk factors for pneumothorax development. Records of 86 cases admitted between February 2014 and January 2015 with or without primary spontaneous pneumothorax were analysed retrospectively. The patients were allocated to two groups as Group I and Group II. The patients were evaluated with regard to age, gender, pneumothorax side, duration of hospital stay, treatment type, recurrence, chest height and transverse diameter on posteroanterior chest graphy and body mass index. Body mass index ratio per cm of chest height was calculated by dividing body mass index with chest height. We named this risk index ratio which is defined first as 'Ankara Numune Risk Index'. Diagnostic value of Ankara Numune Risk Index value for prediction of primary spontaneous pneumothorax development was analysed with Receiver Operating Characteristics curver. Of 86 patients, 69 (80.2%) were male and 17 (19.8%) were female. Each group was composed of 43 (50%) patients. When Receiver Operating Characteristics curve analysis was done for optimal limit value 0.74 of Ankara Numune Risk Index determined for prediction of pneumothorax development risk, area under the curve was 0.925 (95% Cl, 0.872-0.977, p < 0.001). Ankara Numune Risk Index is one of the structural risk factors for prediction of primary spontaneous pneumothorax development however it is insufficient for determining recurrence. Copyright © 2015. Published by Elsevier Taiwan.

Which Variables Associated with Data-Driven Instruction Are Believed to Best Predict Urban Student Achievement?

ERIC Educational Resources Information Center

Greer, Wil

2013-01-01

This study identified the variables associated with data-driven instruction (DDI) that are perceived to best predict student achievement. Of the DDI variables discussed in the literature, 51 of them had a sufficient enough research base to warrant statistical analysis. Of them, 26 were statistically significant. Multiple regression and an…

AE Geomagnetic Index Predictability for High Speed Solar Wind Streams: A Wavelet Decomposition Technique

NASA Technical Reports Server (NTRS)

Guarnieri, Fernando L.; Tsurutani, Bruce T.; Hajra, Rajkumar; Echer, Ezequiel; Gonzalez, Walter D.; Mannucci, Anthony J.

2014-01-01

High speed solar wind streams cause geomagnetic activity at Earth. In this study we have applied a wavelet interactive filtering and reconstruction technique on the solar wind magnetic field components and AE index series to allowed us to investigate the relationship between the two. The IMF Bz component was found as the most significant solar wind parameter responsible by the control of the AE activity. Assuming magnetic reconnection associated to southward directed Bz is the main mechanism transferring energy into the magnetosphere, we adjust parameters to forecast the AE index. The adjusted routine is able to forecast AE, based only on the Bz measured at the L1 Lagrangian point. This gives a prediction approximately 30-70 minutes in advance of the actual geomagnetic activity. The correlation coefficient between the observed AE data and the forecasted series reached values higher than 0.90. In some cases the forecast reproduced particularities observed in the signal very well.The high correlation values observed and the high efficacy of the forecasting can be taken as a confirmation that reconnection is the main physical mechanism responsible for the energy transfer during HILDCAAs. The study also shows that the IMF Bz component low frequencies are most important for AE prediction.

Heritability of and Mortality Prediction With a Longevity Phenotype: The Healthy Aging Index

PubMed Central

2014-01-01

Background. Longevity-associated genes may modulate risk for age-related diseases and survival. The Healthy Aging Index (HAI) may be a subphenotype of longevity, which can be constructed in many studies for genetic analysis. We investigated the HAI’s association with survival in the Cardiovascular Health Study and heritability in the Long Life Family Study. Methods. The HAI includes systolic blood pressure, pulmonary vital capacity, creatinine, fasting glucose, and Modified Mini-Mental Status Examination score, each scored 0, 1, or 2 using approximate tertiles and summed from 0 (healthy) to 10 (unhealthy). In Cardiovascular Health Study, the association with mortality and accuracy predicting death were determined with Cox proportional hazards analysis and c-statistics, respectively. In Long Life Family Study, heritability was determined with a variance component–based family analysis using a polygenic model. Results. Cardiovascular Health Study participants with unhealthier index scores (7–10) had 2.62-fold (95% confidence interval: 2.22, 3.10) greater mortality than participants with healthier scores (0–2). The HAI alone predicted death moderately well (c-statistic = 0.643, 95% confidence interval: 0.626, 0.661, p < .0001) and slightly worse than age alone (c-statistic = 0.700, 95% confidence interval: 0.684, 0.717, p < .0001; p < .0001 for comparison of c-statistics). Prediction increased significantly with adjustment for demographics, health behaviors, and clinical comorbidities (c-statistic = 0.780, 95% confidence interval: 0.765, 0.794, p < .0001). In Long Life Family Study, the heritability of the HAI was 0.295 (p < .0001) overall, 0.387 (p < .0001) in probands, and 0.238 (p = .0004) in offspring. Conclusion. The HAI should be investigated further as a candidate phenotype for uncovering longevity-associated genes in humans. PMID:23913930

Understanding interannual, decadal level variability in paralytic shellfish poisoning toxicity in the Gulf of Maine: The HAB Index

NASA Astrophysics Data System (ADS)

Anderson, Donald M.; Couture, Darcie A.; Kleindinst, Judith L.; Keafer, Bruce A.; McGillicuddy, Dennis J., Jr.; Martin, Jennifer L.; Richlen, Mindy L.; Hickey, J. Michael; Solow, Andrew R.

2014-05-01

A major goal in harmful algal bloom (HAB) research has been to identify mechanisms underlying interannual variability in bloom magnitude and impact. Here the focus is on variability in Alexandrium fundyense blooms and paralytic shellfish poisoning (PSP) toxicity in Maine, USA, over 34 years (1978-2011). The Maine coastline was divided into two regions - eastern and western Maine, and within those two regions, three measures of PSP toxicity (the percent of stations showing detectable toxicity over the year, the cumulative amount of toxicity per station measured in all shellfish (mussel) samples during that year, and the duration of measurable toxicity) were examined for each year in the time series. These metrics were combined into a simple HAB Index that provides a single measure of annual toxin severity across each region. The three toxin metrics, as well as the HAB Index that integrates them, reveal significant variability in overall toxicity between individual years as well as long-term, decadal patterns or regimes. Based on different conceptual models of the system, we considered three trend formulations to characterize the long-term patterns in the Index - a three-phase (mean-shift) model, a linear two-phase model, and a pulse-decline model. The first represents a “regime shift” or multiple equilibria formulation as might occur with alternating periods of sustained high and low cyst abundance or favorable and unfavorable growth conditions, the second depicts a scenario of more gradual transitions in cyst abundance or growth conditions of vegetative cells, and the third characterizes a ”sawtooth” pattern in which upward shifts in toxicity are associated with major cyst recruitment events, followed by a gradual but continuous decline until the next pulse. The fitted models were compared using both residual sum of squares and Akaike's Information Criterion. There were some differences between model fits, but none consistently gave a better fit than the

Understanding interannual, decadal level variability in paralytic shellfish poisoning toxicity in the Gulf of Maine: the HAB Index

PubMed Central

Anderson, Donald M.; Couture, Darcie A.; Kleindinst, Judith L.; Keafer, Bruce A.; McGillicuddy, Dennis J.; Martin, Jennifer L.; Richlen, Mindy L.; Hickey, J. Michael; Solow, Andrew R.

2013-01-01

A major goal in harmful algal bloom (HAB) research has been to identify mechanisms underlying interannual variability in bloom magnitude and impact. Here the focus is on variability in Alexandrium fundyense blooms and paralytic shellfish poisoning (PSP) toxicity in Maine, USA, over 34 years (1978 – 2011). The Maine coastline was divided into two regions -eastern and western Maine, and within those two regions, three measures of PSP toxicity (the percent of stations showing detectable toxicity over the year, the cumulative amount of toxicity per station measured in all shellfish (mussel) samples during that year, and the duration of measurable toxicity) were examined for each year in the time series. These metrics were combined into a simple HAB Index that provides a single measure of annual toxin severity across each region. The three toxin metrics, as well as the HAB Index that integrates them, reveal significant variability in overall toxicity between individual years as well as long-term, decadal patterns or regimes. Based on different conceptual models of the system, we considered three trend formulations to characterize the long-term patterns in the Index – a three-phase (mean-shift) model, a linear two-phase model, and a pulse-decline model. The first represents a “regime shift” or multiple equilibria formulation as might occur with alternating periods of sustained high and low cyst abundance or favorable and unfavorable growth conditions, the second depicts a scenario of more gradual transitions in cyst abundance or growth conditions of vegetative cells, and the third characterizes a ”sawtooth” pattern in which upward shifts in toxicity are associated with major cyst recruitment events, followed by a gradual but continuous decline until the next pulse. The fitted models were compared using both residual sum of squares and Akaike's Information Criterion. There were some differences between model fits, but none consistently gave a better fit than

Perceived exertion is as effective as the perceptual strain index in predicting physiological strain when wearing personal protective clothing.

PubMed

Borg, David N; Costello, Joseph T; Bach, Aaron J; Stewart, Ian B

2017-02-01

The perceptual strain index (PeSI) has been shown to overcome the limitations associated with the assessment of the physiological strain index (PSI), primarily the need to obtain a core body temperature measurement. The PeSI uses the subjective scales of thermal sensation and perceived exertion (RPE) to provide surrogate measures of core temperature and heart rate, respectively. Unfortunately, thermal sensation has shown large variability in providing an estimation of core body temperature. Therefore, the primary aim of this study was to determine if thermal comfort improved the ability of the PeSI to predict the PSI during exertional-heat stress. Eighteen healthy males (age: 23.5years; body mass: 79.4kg; maximal aerobic capacity: 57.2ml·kg -1 ·min -1 ) wore four different chemical/biological protective garments while walking on treadmill at a low (<325W) or moderate (326-499W) metabolic workload in environmental conditions equivalent to wet bulb globe temperatures 21, 30 or 37°C. Trials were terminated when heart rate exceeded 90% of maximum, when core body temperature reached 39°C, at 120min or due to volitional fatigue. Core body temperature, heart rate, thermal sensation, thermal comfort and RPE were recorded at 15min intervals and at termination. Multiple statistical methods were used to determine the most accurate perceptual predictor. Significant moderate relationships were observed between the PeSI (r=0.74; p<0.001), the modified PeSI (r=0.73; p<0.001) and unexpectedly RPE (r=0.71; p<0.001) with the PSI, respectively. The PeSI (mean bias: -0.8±1.5 based on a 0-10 scale; area under the curve: 0.887), modified PeSI (mean bias: -0.5±1.4 based on 0-10 scale; area under the curve: 0.886) and RPE (mean bias: -0.7±1.4 based on a 0-10 scale; area under the curve: 0.883) displayed similar predictive performance when participants experienced high-to-very high levels of physiological strain. Modifying the PeSI did not improve the subjective prediction of

Canonical Measure of Correlation (CMC) and Canonical Measure of Distance (CMD) between sets of data. Part 3. Variable selection in classification.

PubMed

Ballabio, Davide; Consonni, Viviana; Mauri, Andrea; Todeschini, Roberto

2010-01-11

In multivariate regression and classification issues variable selection is an important procedure used to select an optimal subset of variables with the aim of producing more parsimonious and eventually more predictive models. Variable selection is often necessary when dealing with methodologies that produce thousands of variables, such as Quantitative Structure-Activity Relationships (QSARs) and highly dimensional analytical procedures. In this paper a novel method for variable selection for classification purposes is introduced. This method exploits the recently proposed Canonical Measure of Correlation between two sets of variables (CMC index). The CMC index is in this case calculated for two specific sets of variables, the former being comprised of the independent variables and the latter of the unfolded class matrix. The CMC values, calculated by considering one variable at a time, can be sorted and a ranking of the variables on the basis of their class discrimination capabilities results. Alternatively, CMC index can be calculated for all the possible combinations of variables and the variable subset with the maximal CMC can be selected, but this procedure is computationally more demanding and classification performance of the selected subset is not always the best one. The effectiveness of the CMC index in selecting variables with discriminative ability was compared with that of other well-known strategies for variable selection, such as the Wilks' Lambda, the VIP index based on the Partial Least Squares-Discriminant Analysis, and the selection provided by classification trees. A variable Forward Selection based on the CMC index was finally used in conjunction of Linear Discriminant Analysis. This approach was tested on several chemical data sets. Obtained results were encouraging.

Effect of predictive sign of acceleration on heart rate variability in passive translation situation: preliminary evidence using visual and vestibular stimuli in VR environment

PubMed Central

Watanabe, Hiroshi; Teramoto, Wataru; Umemura, Hiroyuki

2007-01-01

Objective We studied the effects of the presentation of a visual sign that warned subjects of acceleration around the yaw and pitch axes in virtual reality (VR) on their heart rate variability. Methods Synchronization of the immersive virtual reality equipment (CAVE) and motion base system generated a driving scene and provided subjects with dynamic and wide-ranging depth information and vestibular input. The heart rate variability of 21 subjects was measured while the subjects observed a simulated driving scene for 16 minutes under three different conditions. Results When the predictive sign of the acceleration appeared 3500 ms before the acceleration, the index of the activity of the autonomic nervous system (low/high frequency ratio; LF/HF ratio) of subjects did not change much, whereas when no sign appeared the LF/HF ratio increased over the observation time. When the predictive sign of the acceleration appeared 750 ms before the acceleration, no systematic change occurred. Conclusion The visual sign which informed subjects of the acceleration affected the activity of the autonomic nervous system when it appeared long enough before the acceleration. Also, our results showed the importance of the interval between the sign and the event and the relationship between the gradual representation of events and their quantity. PMID:17903267

[Frail-VIG index: Design and evaluation of a new frailty index based on the Comprehensive Geriatric Assessment].

PubMed

Amblàs-Novellas, Jordi; Martori, Joan Carles; Molist Brunet, Núria; Oller, Ramon; Gómez-Batiste, Xavier; Espaulella Panicot, Joan

Frailty is closely linked to health results. Frailty indexes (FI) and the Comprehensive Geriatric Assessment (CGA) are multidimensional tools. FI serve to quantitatively measure frailty levels. They have shown to have an excellent correlation with mortality. However, they are infrequently used in clinical practice. Given the need for new, more concise, and pragmatic FI, a new FI is proposed based on a CGA (Frail-VIG Index). A prospective, observational, longitudinal study was conducted, with cohort follow up at 12 months or death. Participants were patients admitted in the Geriatric Unit of the University Hospital of Vic (Barcelona, Spain) during 2014. Contrast of hypothesis log-rank for survival curves according to Frail-VIG index, and analysis of ROC curves were performed to assess prognostic capacity. A total of 590 patients were included (mean age=86.39). Mortality rate at 12 months was 46.4%. The comparative analysis showed statistically significant differences (P<.05) for almost all variables included in the Frail-VIG index. Survival curves also show significant differences (X 2 =445, P<.001) for the different Frail-VIG index scores. The area under the ROC curve at 12 months was 0.9 (0.88-0.92). An administration time of the Index is estimated at less than 10minutes. Results endorse the Frail-VIG index as a simple (as for contents), rapid (for administration time) tool, with discriminative (for situational diagnosis) and predictive capacity (high correlation with mortality). Copyright © 2016 SEGG. Publicado por Elsevier España, S.L.U. All rights reserved.

Protein construct storage: Bayesian variable selection and prediction with mixtures.

PubMed

Clyde, M A; Parmigiani, G

1998-07-01

Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.

Comment on "The Predicted Size of Cycle 23 Based on the Inferred three-cycle Quasiperiodicity of the Planetary Index Ap"

NASA Technical Reports Server (NTRS)

Wilson, Robert M.; Hathaway, David H.

1999-01-01

Recently, Ahluwalia reviewed the solar and geomagnetic data for the last 6 decades and remarked that these data "indicate the existence of a three-solar-activity-cycle quasiperiodicity in them." Furthermore, on the basis of this inferred quasiperiodicity, he asserted that cycle 23 represents the initial cycle in a new three-cycle string, implying that it "will be more modest (a la cycle 17) with an annual mean sunspot number count of 119.3 +/- 30 at the maximum", a prediction that is considerably below the consensus prediction of 160 +/- 30 by Joselin et al. and of similar predictions by others based on a variety of predictive techniques. Several major sticking points of Ahluwalia's presentation, however, must be readdressed, and these issues form the basis of this comment. First, Ahluwalia appears to have based his analysis on a data set of Ap index values that is erroneous. For example, he depicts for the interval of 1932-1997 the variation of the Ap index in terms of annual averages, contrasting them against annual averages of sunspot number (SSN), and he lists for cycles 17-23 the minimum and maximum value of each, as well as the years in which they occur and a quantity which he calls "Amplitude" (defined as the numeric difference between the maximum and minimum values). In particular, he identifies the minimum Ap index (i.e., the minimum value of the Ap index in the vicinity of sunspot cycle minimum, which usually occurs in the year following sunspot minimum and which will be called hereafter, simply, Ap min) and the year in which it occur for cycles 17 - 23 respectively.

Prediction of Elderly Anthropometric Dimension Based On Age, Gender, Origin, and Body Mass Index

NASA Astrophysics Data System (ADS)

Indah, P.; Sari, A. D.; Suryoputro, M. R.; Purnomo, H.

2016-01-01

Introduction: Studies have indicated that elderly anthropometric dimensions will different for each person. To determine whether there are differences in the anthropometric data of Javanese elderly, this study will analyze whether the variables of age, gender, origin, and body mass index (BMI) have been associated with elderly anthropometric dimensions. Age will be divided into elderly and old categories, gender will divide into male and female, origins were divided into Yogyakarta and Central Java, and for BMI only use the normal category. Method: Anthropometric studies were carried out on 45 elderly subjects in Sleman,Yogyakarta. Results and Discussion: The results showed that some elderly anthropometric dimensions were influenced by age, origin, and body mass index but gender doesn't significantly affect the elderly anthropometric dimensions that exist in the area of Sleman. The analysis has provided important aid when designing products that intended to the Javanese elderly Population.

KSC Construction Cost Index

NASA Technical Reports Server (NTRS)

Brown, J. A.

1983-01-01

Kennedy Space Center cost Index aids in conceptual design cost estimates. Report discusses development of KSC Cost Index since January 1974. Index since January 1974. Index provides management, design engineers, and estimators an up-to-data reference for local labor and material process. Also provides mount and rate of change in these costs used to predict future construction costs.

Development of the statistical ARIMA model: an application for predicting the upcoming of MJO index

NASA Astrophysics Data System (ADS)

Hermawan, Eddy; Nurani Ruchjana, Budi; Setiawan Abdullah, Atje; Gede Nyoman Mindra Jaya, I.; Berliana Sipayung, Sinta; Rustiana, Shailla

2017-10-01

This study is mainly concerned in development one of the most important equatorial atmospheric phenomena that we call as the Madden Julian Oscillation (MJO) which having strong impacts to the extreme rainfall anomalies over the Indonesian Maritime Continent (IMC). In this study, we focused to the big floods over Jakarta and surrounded area that suspecting caused by the impacts of MJO. We concentrated to develop the MJO index using the statistical model that we call as Box-Jenkis (ARIMA) ini 1996, 2002, and 2007, respectively. They are the RMM (Real Multivariate MJO) index as represented by RMM1 and RMM2, respectively. There are some steps to develop that model, starting from identification of data, estimated, determined model, before finally we applied that model for investigation some big floods that occurred at Jakarta in 1996, 2002, and 2007 respectively. We found the best of estimated model for the RMM1 and RMM2 prediction is ARIMA (2,1,2). Detailed steps how that model can be extracted and applying to predict the rainfall anomalies over Jakarta for 3 to 6 months later is discussed at this paper.

Independent variable complexity for regional regression of the flow duration curve in ungauged basins

NASA Astrophysics Data System (ADS)

Fouad, Geoffrey; Skupin, André; Hope, Allen

2016-04-01

The flow duration curve (FDC) is one of the most widely used tools to quantify streamflow. Its percentile flows are often required for water resource applications, but these values must be predicted for ungauged basins with insufficient or no streamflow data. Regional regression is a commonly used approach for predicting percentile flows that involves identifying hydrologic regions and calibrating regression models to each region. The independent variables used to describe the physiographic and climatic setting of the basins are a critical component of regional regression, yet few studies have investigated their effect on resulting predictions. In this study, the complexity of the independent variables needed for regional regression is investigated. Different levels of variable complexity are applied for a regional regression consisting of 918 basins in the US. Both the hydrologic regions and regression models are determined according to the different sets of variables, and the accuracy of resulting predictions is assessed. The different sets of variables include (1) a simple set of three variables strongly tied to the FDC (mean annual precipitation, potential evapotranspiration, and baseflow index), (2) a traditional set of variables describing the average physiographic and climatic conditions of the basins, and (3) a more complex set of variables extending the traditional variables to include statistics describing the distribution of physiographic data and temporal components of climatic data. The latter set of variables is not typically used in regional regression, and is evaluated for its potential to predict percentile flows. The simplest set of only three variables performed similarly to the other more complex sets of variables. Traditional variables used to describe climate, topography, and soil offered little more to the predictions, and the experimental set of variables describing the distribution of basin data in more detail did not improve predictions

Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks.

PubMed

Rudi, Gabrielle; Bailly, Jean-Stéphane; Vinatier, Fabrice

2018-01-01

To optimize ecosystem services provided by agricultural drainage networks (ditches) in headwater catchments, we need to manage the spatial distribution of plant species living in these networks. Geomorphological variables have been shown to be important predictors of plant distribution in other ecosystems because they control the water regime, the sediment deposition rates and the sun exposure in the ditches. Whether such variables may be used to predict plant distribution in agricultural drainage networks is unknown. We collected presence and absence data for 10 herbaceous plant species in a subset of a network of drainage ditches (35 km long) within a Mediterranean agricultural catchment. We simulated their spatial distribution with GLM and Maxent model using geomorphological variables and distance to natural lands and roads. Models were validated using k-fold cross-validation. We then compared the mean Area Under the Curve (AUC) values obtained for each model and other metrics issued from the confusion matrices between observed and predicted variables. Based on the results of all metrics, the models were efficient at predicting the distribution of seven species out of ten, confirming the relevance of geomorphological variables and distance to natural lands and roads to explain the occurrence of plant species in this Mediterranean catchment. In particular, the importance of the landscape geomorphological variables, ie the importance of the geomorphological features encompassing a broad environment around the ditch, has been highlighted. This suggests that agro-ecological measures for managing ecosystem services provided by ditch plants should focus on the control of the hydrological and sedimentological connectivity at the catchment scale. For example, the density of the ditch network could be modified or the spatial distribution of vegetative filter strips used for sediment trapping could be optimized. In addition, the vegetative filter strips could constitute

External validation of the endometriosis fertility index (EFI) staging system for predicting non-ART pregnancy after endometriosis surgery.

PubMed

Tomassetti, C; Geysenbergh, B; Meuleman, C; Timmerman, D; Fieuws, S; D'Hooghe, T

2013-05-01

. Subjects were censored when they were lost to follow-up, had subsequent surgery for endometriosis, started ovarian suppression or underwent ART. As K-M estimates might overestimate the actual event rate, cumulative incidence estimates treating ART as competing event were also calculated. Cox regression analysis was used to assess the performance of EFI and constituting variables. Performance of the score (prediction, discrimination) was quantified with the following methods: mean squared error of prediction (Brier score), areas under the receiver-operating curve and global concordance index C(τ). There was a highly significant relationship between the EFI and the time to non-ART pregnancy (cumulative overall pregnancy rate, P = 0.0004), with the K-M estimate of cumulative overall pregnancy rate at 12 months after surgery equal to 45.5% [95% confidence interval (CI) 39.47-49.87]-ranging from 16.67% (95% CI 5.01-47.65) for EFI scores 0-3, to 62.55% (95% CI 55.18-69.94) for EFI scores 9-10. For each increase of 1 point in the EFI score, the relative risk of becoming pregnant increased by 31% (95% CI 16-47%; i.e. hazard ratio 1.31). The 'least function score'-which assesses the tubal/ovarian function at conclusion of surgery-was found to be the most important contributor to the total EFI score among all the other variables (age, duration of infertility, prior pregnancy, AFS endometriosis lesion and total score). The EFI score had a moderate performance in the prediction of the pregnancy rate. Indeed, the decrease in prediction error was rather small, as shown by the decrease in Brier score from 0.213 to 0.198, and low estimates for R² (13%) and C(τ) (0.629). As the EFI was validated externally in our own European population after initial testing by Adamson and Pasta (Endometriosis fertility index: the new, validated endometriosis staging system. Fertil Steril 2010;94:1609-1615) in an American population, it appears that the EFI can be used clinically to counsel

Application of a functional mathematical index (FMI) for predicting effects of the composition of jujube fruit on nutritional quality and health

USDA-ARS?s Scientific Manuscript database

In the present study, we extend the concept of a Functional Mathematical Index (FMI) for the assessment and prediction of food quality and safety of jujube fruit, a medicinal food widely consumed in Asian countries. In this study the index has been applied to one field-grown jujube fruit harvested a...

Intraindividual variability in reaction time predicts cognitive outcomes 5 years later.

PubMed

Bielak, Allison A M; Hultsch, David F; Strauss, Esther; Macdonald, Stuart W S; Hunter, Michael A

2010-11-01

Building on results suggesting that intraindividual variability in reaction time (inconsistency) is highly sensitive to even subtle changes in cognitive ability, this study addressed the capacity of inconsistency to predict change in cognitive status (i.e., cognitive impairment, no dementia [CIND] classification) and attrition 5 years later. Two hundred twelve community-dwelling older adults, initially aged 64-92 years, remained in the study after 5 years. Inconsistency was calculated from baseline reaction time performance. Participants were assigned to groups on the basis of their fluctuations in CIND classification over time. Logistic and Cox regressions were used. Baseline inconsistency significantly distinguished among those who remained or transitioned into CIND over the 5 years and those who were consistently intact (e.g., stable intact vs. stable CIND, Wald (1) = 7.91, p < .01, Exp(β) = 1.49). Average level of inconsistency over time was also predictive of study attrition, for example, Wald (1) = 11.31, p < .01, Exp(β) = 1.24. For both outcomes, greater inconsistency was associated with a greater likelihood of being in a maladaptive group 5 years later. Variability based on moderately cognitively challenging tasks appeared to be particularly sensitive to longitudinal changes in cognitive ability. Mean rate of responding was a comparable predictor of change in most instances, but individuals were at greater relative risk of being in a maladaptive outcome group if they were more inconsistent rather than if they were slower in responding. Implications for the potential utility of intraindividual variability in reaction time as an early marker of cognitive decline are discussed. (c) 2010 APA, all rights reserved

Predictive variables for mortality after acute ischemic stroke.

PubMed

Carter, Angela M; Catto, Andrew J; Mansfield, Michael W; Bamford, John M; Grant, Peter J

2007-06-01

Stroke is a major healthcare issue worldwide with an incidence comparable to coronary events, highlighting the importance of understanding risk factors for stroke and subsequent mortality. In the present study, we determined long-term (all-cause) mortality in 545 patients with ischemic stroke compared with a cohort of 330 age-matched healthy control subjects followed up for a median of 7.4 years. We assessed the effect of selected demographic, clinical, biochemical, hematologic, and hemostatic factors on mortality in patients with ischemic stroke. Stroke subtype was classified according to the Oxfordshire Community Stroke Project criteria. Patients who died 30 days or less after the acute event (n=32) were excluded from analyses because this outcome is considered to be directly attributable to the acute event. Patients with ischemic stroke were at more than 3-fold increased risk of death compared with the age-matched control cohort. In multivariate analyses, age, stroke subtype, atrial fibrillation, and previous stroke/transient ischemic attack were predictive of mortality in patients with ischemic stroke. Albumin and creatinine and the hemostatic factors von Willebrand factor and beta-thromboglobulin were also predictive of mortality in patients with ischemic stroke after accounting for demographic and clinical variables. The results indicate that subjects with acute ischemic stroke are at increased risk of all-cause mortality. Advancing age, large-vessel stroke, atrial fibrillation, and previous stroke/transient ischemic attack predict mortality; and analysis of albumin, creatinine, von Willebrand factor, and beta-thromboglobulin will aid in the identification of patients at increased risk of death after stroke.

The Niño1+2 region and the Niño4 region predictability.

NASA Astrophysics Data System (ADS)

Miguel, Tasambay-Salazar; Jose, Ortizbevia Maria; Francisco Jose, Alvarez-Garcia; Antonio, Ruizdeelvira

2016-04-01

The El Niño-Southern Oscillation variability is monitored basically by the the Niño3.4 Index. In addition, the Niño1+2 and the Niño4 Indexes are also used to characterise ENSO variability, by reason of their relationships with some of the variability of the neighboring regions, like the air temperature in South America or Australia. However, with the increased length of the available instrumental ENSO records, the need of considering the two different ENSO types identified, Eastern Pacific (EP) or Central Pacific (CP), has become more evident. (Yu and Kim 2013). While the Nino3.4 Index is used to monitor the EP events, the CP events are currently identified by removing from the Niño4 Index the variability associated to the Niño1+2 Index (Kao and Yu 2009). Therefore there is a renewed interest on the predictability of both Indexes. In this study we focus on the predictability of the Niño1+2 region variability and those of the Niño4 region, in the recent post-satellital period. We develop a methodology to identify potential predictors among climate modes, represented by their respective indexes. Among the tropical predictors tested we include the most commonly used,like the Southern Oscillation Index or the Warm Water Volume in the equatorial Pacific (WWV) Index, but also some whose part in the ENSO generation and evolution has been pointed only recently, like the Pacific Meridional Mode (PMM) Index or the North Tropical Zonal Gradient and South Tropical Zonal Gradient Indexes.We also include in our study some other tropical Indexes outside the Pacific basin, like the Tropical North Atlantic, the Tropical South Atlantic and the Indian Ocean Dipole Indexes. We use a seasonal approach, based in a linear statistical relationship and focus on leads going from one season to one year. In the case of the Niño1+2 Index, the number of potential predictors is much higher in spring, followed by winter and summer and last of all autumn. The potential predictor most

Variable-Domain Displacement Transfer Functions for Converting Surface Strains into Deflections for Structural Deformed Shape Predictions

NASA Technical Reports Server (NTRS)

Ko, William L.; Fleischer, Van Tran

2015-01-01

Variable-Domain Displacement Transfer Functions were formulated for shape predictions of complex wing structures, for which surface strain-sensing stations must be properly distributed to avoid jointed junctures, and must be increased in the high strain gradient region. Each embedded beam (depth-wise cross section of structure along a surface strain-sensing line) was discretized into small variable domains. Thus, the surface strain distribution can be described with a piecewise linear or a piecewise nonlinear function. Through discretization, the embedded beam curvature equation can be piece-wisely integrated to obtain the Variable-Domain Displacement Transfer Functions (for each embedded beam), which are expressed in terms of geometrical parameters of the embedded beam and the surface strains along the strain-sensing line. By inputting the surface strain data into the Displacement Transfer Functions, slopes and deflections along each embedded beam can be calculated for mapping out overall structural deformed shapes. A long tapered cantilever tubular beam was chosen for shape prediction analysis. The input surface strains were analytically generated from finite-element analysis. The shape prediction accuracies of the Variable- Domain Displacement Transfer Functions were then determined in light of the finite-element generated slopes and deflections, and were fofound to be comparable to the accuracies of the constant-domain Displacement Transfer Functions

Prediction of coal grindability from exploration data

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

Gomez, M.; Hazen, K.

1970-08-01

A general prediction model for the Hardgrove grindability index was constructed from 735 coal samples using the proximate analysis, heating value, and sulfur content. The coals used to develop the general model ranged in volatile matter from 12.8 to 49.2 percent, dry basis, and had grindability indexes ranging from 35 to 121. A restricted model applicable to bituminous coals having grindabilities in the 40 to 110 range was developed from the proximate analysis and the petrographic composition of the coal. The prediction of coal grindability within a single seam was also investigated. The results reported support the belief that mechanicalmore » properties of the coal are related to both chemical and petrographic factors of the coal. The mechanical properties coal may be forecast in advance of mining, because the variables used as input to the prediction models can be measured from drill core samples collected during exploration.« less

Sources of Uncertainty in the Prediction of LAI / fPAR from MODIS

NASA Technical Reports Server (NTRS)

Dungan, Jennifer L.; Ganapol, Barry D.; Brass, James A. (Technical Monitor)

2002-01-01

To explicate the sources of uncertainty in the prediction of biophysical variables over space, consider the general equation: where z is a variable with values on some nominal, ordinal, interval or ratio scale; y is a vector of input variables; u is the spatial support of y and z ; x and u are the spatial locations of y and z , respectively; f is a model and B is the vector of the parameters of this model. Any y or z has a value and a spatial extent which is called its support. Viewed in this way, categories of uncertainty are from variable (e.g. measurement), parameter, positional. support and model (e.g. structural) sources. The prediction of Leaf Area Index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) are examples of z variables predicted using model(s) as a function of y variables and spatially constant parameters. The MOD15 algorithm is an example of f, called f(sub 1), with parameters including those defined by one of six biome types and solar and view angles. The Leaf Canopy Model (LCM)2, a nested model that combines leaf radiative transfer with a full canopy reflectance model through the phase function, is a simpler though similar radiative transfer approach to f(sub 1). In a previous study, MOD15 and LCM2 gave similar results for the broadleaf forest biome. Differences between these two models can be used to consider the structural uncertainty in prediction results. In an effort to quantify each of the five sources of uncertainty and rank their relative importance for the LAI/fPAR prediction problem, we used recent data for an EOS Core Validation Site in the broadleaf biome with coincident surface reflectance, vegetation index, fPAR and LAI products from the Moderate Resolution Imaging Spectrometer (MODIS). Uncertainty due to support on the input reflectance variable was characterized using Landsat ETM+ data. Input uncertainties were propagated through the LCM2 model and compared with published uncertainties from the MOD15

Performance index for virtual reality phacoemulsification surgery

NASA Astrophysics Data System (ADS)

Söderberg, Per; Laurell, Carl-Gustaf; Simawi, Wamidh; Skarman, Eva; Nordqvist, Per; Nordh, Leif

2007-02-01

We have developed a virtual reality (VR) simulator for phacoemulsification (phaco) surgery. The current work aimed at developing a performance index that characterizes the performance of an individual trainee. We recorded measurements of 28 response variables during three iterated surgical sessions in 9 subjects naive to cataract surgery and 6 experienced cataract surgeons, separately for the sculpting phase and the evacuation phase of phacoemulsification surgery. We further defined a specific performance index for a specific measurement variable and a total performance index for a specific trainee. The distribution function for the total performance index was relatively evenly distributed both for the sculpting and the evacuation phase indicating that parametric statistics can be used for comparison of total average performance indices for different groups in the future. The current total performance index for an individual considers all measurement variables included with the same weight. It is possible that a future development of the system will indicate that a better characterization of a trainee can be obtained if the various measurements variables are given specific weights. The currently developed total performance index for a trainee is statistically an independent observation of that particular trainee.

Heat and Humidity in the City: Neighborhood Heat Index Variability in a Mid-Sized City in the Southeastern United States

PubMed Central

Hass, Alisa L.; Ellis, Kelsey N.; Reyes Mason, Lisa; Hathaway, Jon M.; Howe, David A.

2016-01-01

Daily weather conditions for an entire city are usually represented by a single weather station, often located at a nearby airport. This resolution of atmospheric data fails to recognize the microscale climatic variability associated with land use decisions across and within urban neighborhoods. This study uses heat index, a measure of the combined effects of temperature and humidity, to assess the variability of heat exposure from ten weather stations across four urban neighborhoods and two control locations (downtown and in a nearby nature center) in Knoxville, Tennessee, USA. Results suggest that trees may negate a portion of excess urban heat, but are also associated with greater humidity. As a result, the heat index of locations with more trees is significantly higher than downtown and areas with fewer trees. Trees may also reduce heat stress by shading individuals from incoming radiation, though this is not considered in this study. Greater amounts of impervious surfaces correspond with reduced evapotranspiration and greater runoff, in terms of overall mass balance, leading to a higher temperature, but lower relative humidity. Heat index and relative humidity were found to significantly vary between locations with different tree cover and neighborhood characteristics for the full study time period as well as for the top 10% of heat index days. This work demonstrates the need for high-resolution climate data and the use of additional measures beyond temperature to understand urban neighborhood exposure to extreme heat, and expresses the importance of considering vulnerability differences among residents when analyzing neighborhood-scale impacts. PMID:26761021

Heat and Humidity in the City: Neighborhood Heat Index Variability in a Mid-Sized City in the Southeastern United States.

PubMed

Hass, Alisa L; Ellis, Kelsey N; Reyes Mason, Lisa; Hathaway, Jon M; Howe, David A

2016-01-11

Daily weather conditions for an entire city are usually represented by a single weather station, often located at a nearby airport. This resolution of atmospheric data fails to recognize the microscale climatic variability associated with land use decisions across and within urban neighborhoods. This study uses heat index, a measure of the combined effects of temperature and humidity, to assess the variability of heat exposure from ten weather stations across four urban neighborhoods and two control locations (downtown and in a nearby nature center) in Knoxville, Tennessee, USA. Results suggest that trees may negate a portion of excess urban heat, but are also associated with greater humidity. As a result, the heat index of locations with more trees is significantly higher than downtown and areas with fewer trees. Trees may also reduce heat stress by shading individuals from incoming radiation, though this is not considered in this study. Greater amounts of impervious surfaces correspond with reduced evapotranspiration and greater runoff, in terms of overall mass balance, leading to a higher temperature, but lower relative humidity. Heat index and relative humidity were found to significantly vary between locations with different tree cover and neighborhood characteristics for the full study time period as well as for the top 10% of heat index days. This work demonstrates the need for high-resolution climate data and the use of additional measures beyond temperature to understand urban neighborhood exposure to extreme heat, and expresses the importance of considering vulnerability differences among residents when analyzing neighborhood-scale impacts.

Who will have Sustainable Employment After a Back Injury? The Development of a Clinical Prediction Model in a Cohort of Injured Workers.

PubMed

Shearer, Heather M; Côté, Pierre; Boyle, Eleanor; Hayden, Jill A; Frank, John; Johnson, William G

2017-09-01

Purpose Our objective was to develop a clinical prediction model to identify workers with sustainable employment following an episode of work-related low back pain (LBP). Methods We used data from a cohort study of injured workers with incident LBP claims in the USA to predict employment patterns 1 and 6 months following a workers' compensation claim. We developed three sequential models to determine the contribution of three domains of variables: (1) basic demographic/clinical variables; (2) health-related variables; and (3) work-related factors. Multivariable logistic regression was used to develop the predictive models. We constructed receiver operator curves and used the c-index to measure predictive accuracy. Results Seventy-nine percent and 77 % of workers had sustainable employment at 1 and 6 months, respectively. Sustainable employment at 1 month was predicted by initial back pain intensity, mental health-related quality of life, claim litigation and employer type (c-index = 0.77). At 6 months, sustainable employment was predicted by physical and mental health-related quality of life, claim litigation and employer type (c-index = 0.77). Adding health-related and work-related variables to models improved predictive accuracy by 8.5 and 10 % at 1 and 6 months respectively. Conclusion We developed clinically-relevant models to predict sustainable employment in injured workers who made a workers' compensation claim for LBP. Inquiring about back pain intensity, physical and mental health-related quality of life, claim litigation and employer type may be beneficial in developing programs of care. Our models need to be validated in other populations.

Database and new models based on a group contribution method to predict the refractive index of ionic liquids.

PubMed

Wang, Xinxin; Lu, Xingmei; Zhou, Qing; Zhao, Yongsheng; Li, Xiaoqian; Zhang, Suojiang

2017-08-02

Refractive index is one of the important physical properties, which is widely used in separation and purification. In this study, the refractive index data of ILs were collected to establish a comprehensive database, which included about 2138 pieces of data from 1996 to 2014. The Group Contribution-Artificial Neural Network (GC-ANN) model and Group Contribution (GC) method were employed to predict the refractive index of ILs at different temperatures from 283.15 K to 368.15 K. Average absolute relative deviations (AARD) of the GC-ANN model and the GC method were 0.179% and 0.628%, respectively. The results showed that a GC-ANN model provided an effective way to estimate the refractive index of ILs, whereas the GC method was simple and extensive. In summary, both of the models were accurate and efficient approaches for estimating refractive indices of ILs.

Can we predict diatoms herbicide sensitivities with phylogeny? Influence of intraspecific and interspecific variability.

PubMed

Esteves, Sara M; Keck, François; Almeida, Salomé F P; Figueira, Etelvina; Bouchez, Agnès; Rimet, Frédéric

2017-10-01