Habitat and Vegetation Variables Are Not Enough When Predicting Tick Populations in the Southeastern United States

PubMed Central

Trout Fryxell, R. T.; Moore, J. E.; Collins, M. D.; Kwon, Y.; Jean-Philippe, S. R.; Schaeffer, S. M.; Odoi, A.; Kennedy, M.; Houston, A. E.

2015-01-01

Two tick-borne diseases with expanding case and vector distributions are ehrlichiosis (transmitted by Amblyomma americanum) and rickettiosis (transmitted by A. maculatum and Dermacentor variabilis). There is a critical need to identify the specific habitats where each of these species is likely to be encountered to classify and pinpoint risk areas. Consequently, an in-depth tick prevalence study was conducted on the dominant ticks in the southeast. Vegetation, soil, and remote sensing data were used to test the hypothesis that habitat and vegetation variables can predict tick abundances. No variables were significant predictors of A. americanum adult and nymph tick abundance, and no clustering was evident because this species was found throughout the study area. For A. maculatum adult tick abundance was predicted by NDVI and by the interaction between habitat type and plant diversity; two significant population clusters were identified in a heterogeneous area suitable for quail habitat. For D. variabilis no environmental variables were significant predictors of adult abundance; however, D. variabilis collections clustered in three significant areas best described as agriculture areas with defined edges. This study identified few landscape and vegetation variables associated with tick presence. While some variables were significantly associated with tick populations, the amount of explained variation was not useful for predicting reliably where ticks occur; consequently, additional research that includes multiple sampling seasons and locations throughout the southeast are warranted. This low amount of explained variation may also be due to the use of hosts for dispersal, and potentially to other abiotic and biotic variables. Host species play a large role in the establishment, maintenance, and dispersal of a tick species, as well as the maintenance of disease cycles, dispersal to new areas, and identification of risk areas. PMID:26656122

Computational methods using genome-wide association studies to predict radiotherapy complications and to identify correlative molecular processes

NASA Astrophysics Data System (ADS)

Oh, Jung Hun; Kerns, Sarah; Ostrer, Harry; Powell, Simon N.; Rosenstein, Barry; Deasy, Joseph O.

2017-02-01

The biological cause of clinically observed variability of normal tissue damage following radiotherapy is poorly understood. We hypothesized that machine/statistical learning methods using single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) would identify groups of patients of differing complication risk, and furthermore could be used to identify key biological sources of variability. We developed a novel learning algorithm, called pre-conditioned random forest regression (PRFR), to construct polygenic risk models using hundreds of SNPs, thereby capturing genomic features that confer small differential risk. Predictive models were trained and validated on a cohort of 368 prostate cancer patients for two post-radiotherapy clinical endpoints: late rectal bleeding and erectile dysfunction. The proposed method results in better predictive performance compared with existing computational methods. Gene ontology enrichment analysis and protein-protein interaction network analysis are used to identify key biological processes and proteins that were plausible based on other published studies. In conclusion, we confirm that novel machine learning methods can produce large predictive models (hundreds of SNPs), yielding clinically useful risk stratification models, as well as identifying important underlying biological processes in the radiation damage and tissue repair process. The methods are generally applicable to GWAS data and are not specific to radiotherapy endpoints.

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

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

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.

Identifying dyslexia in adults: an iterative method using the predictive value of item scores and self-report questions.

PubMed

Tamboer, Peter; Vorst, Harrie C M; Oort, Frans J

2014-04-01

Methods for identifying dyslexia in adults vary widely between studies. Researchers have to decide how many tests to use, which tests are considered to be the most reliable, and how to determine cut-off scores. The aim of this study was to develop an objective and powerful method for diagnosing dyslexia. We took various methodological measures, most of which are new compared to previous methods. We used a large sample of Dutch first-year psychology students, we considered several options for exclusion and inclusion criteria, we collected as many cognitive tests as possible, we used six independent sources of biographical information for a criterion of dyslexia, we compared the predictive power of discriminant analyses and logistic regression analyses, we used both sum scores and item scores as predictor variables, we used self-report questions as predictor variables, and we retested the reliability of predictions with repeated prediction analyses using an adjusted criterion. We were able to identify 74 dyslexic and 369 non-dyslexic students. For 37 students, various predictions were too inconsistent for a final classification. The most reliable predictions were acquired with item scores and self-report questions. The main conclusion is that it is possible to identify dyslexia with a high reliability, although the exact nature of dyslexia is still unknown. We therefore believe that this study yielded valuable information for future methods of identifying dyslexia in Dutch as well as in other languages, and that this would be beneficial for comparing studies across countries.

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.

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.

Subarachnoid hemorrhage admissions retrospectively identified using a prediction model

PubMed Central

McIntyre, Lauralyn; Fergusson, Dean; Turgeon, Alexis; dos Santos, Marlise P.; Lum, Cheemun; Chassé, Michaël; Sinclair, John; Forster, Alan; van Walraven, Carl

2016-01-01

Objective: To create an accurate prediction model using variables collected in widely available health administrative data records to identify hospitalizations for primary subarachnoid hemorrhage (SAH). Methods: A previously established complete cohort of consecutive primary SAH patients was combined with a random sample of control hospitalizations. Chi-square recursive partitioning was used to derive and internally validate a model to predict the probability that a patient had primary SAH (due to aneurysm or arteriovenous malformation) using health administrative data. Results: A total of 10,322 hospitalizations with 631 having primary SAH (6.1%) were included in the study (5,122 derivation, 5,200 validation). In the validation patients, our recursive partitioning algorithm had a sensitivity of 96.5% (95% confidence interval [CI] 93.9–98.0), a specificity of 99.8% (95% CI 99.6–99.9), and a positive likelihood ratio of 483 (95% CI 254–879). In this population, patients meeting criteria for the algorithm had a probability of 45% of truly having primary SAH. Conclusions: Routinely collected health administrative data can be used to accurately identify hospitalized patients with a high probability of having a primary SAH. This algorithm may allow, upon validation, an easy and accurate method to create validated cohorts of primary SAH from either ruptured aneurysm or arteriovenous malformation. PMID:27629096

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...

Drivers and potential predictability of summer time North Atlantic polar front jet variability

NASA Astrophysics Data System (ADS)

Hall, Richard J.; Jones, Julie M.; Hanna, Edward; Scaife, Adam A.; Erdélyi, Róbert

2017-06-01

The variability of the North Atlantic polar front jet stream is crucial in determining summer weather around the North Atlantic basin. Recent extreme summers in western Europe and North America have highlighted the need for greater understanding of this variability, in order to aid seasonal forecasting and mitigate societal, environmental and economic impacts. Here we find that simple linear regression and composite models based on a few predictable factors are able to explain up to 35 % of summertime jet stream speed and latitude variability from 1955 onwards. Sea surface temperature forcings impact predominantly on jet speed, whereas solar and cryospheric forcings appear to influence jet latitude. The cryospheric associations come from the previous autumn, suggesting the survival of an ice-induced signal through the winter season, whereas solar influences lead jet variability by a few years. Regression models covering the earlier part of the twentieth century are much less effective, presumably due to decreased availability of data, and increased uncertainty in observational reanalyses. Wavelet coherence analysis identifies that associations fluctuate over the study period but it is not clear whether this is just internal variability or genuine non-stationarity. Finally we identify areas for future research.

A Python Analytical Pipeline to Identify Prohormone Precursors and Predict Prohormone Cleavage Sites

PubMed Central

Southey, Bruce R.; Sweedler, Jonathan V.; Rodriguez-Zas, Sandra L.

2008-01-01

Neuropeptides and hormones are signaling molecules that support cell–cell communication in the central nervous system. Experimentally characterizing neuropeptides requires significant efforts because of the complex and variable processing of prohormone precursor proteins into neuropeptides and hormones. We demonstrate the power and flexibility of the Python language to develop components of an bioinformatic analytical pipeline to identify precursors from genomic data and to predict cleavage as these precursors are en route to the final bioactive peptides. We identified 75 precursors in the rhesus genome, predicted cleavage sites using support vector machines and compared the rhesus predictions to putative assignments based on homology to human sequences. The correct classification rate of cleavage using the support vector machines was over 97% for both human and rhesus data sets. The functionality of Python has been important to develop and maintain NeuroPred (http://neuroproteomics.scs.uiuc.edu/neuropred.html), a user-centered web application for the neuroscience community that provides cleavage site prediction from a wide range of models, precision and accuracy statistics, post-translational modifications, and the molecular mass of potential peptides. The combined results illustrate the suitability of the Python language to implement an all-inclusive bioinformatics approach to predict neuropeptides that encompasses a large number of interdependent steps, from scanning genomes for precursor genes to identification of potential bioactive neuropeptides. PMID:19169350

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.

Identifying the bleeding trauma patient: predictive factors for massive transfusion in an Australasian trauma population.

PubMed

Hsu, Jeremy Ming; Hitos, Kerry; Fletcher, John P

2013-09-01

Military and civilian data would suggest that hemostatic resuscitation results in improved outcomes for exsanguinating patients. However, identification of those patients who are at risk of significant hemorrhage is not clearly defined. We attempted to identify factors that would predict the need for massive transfusion (MT) in an Australasian trauma population, by comparing those trauma patients who did receive massive transfusion with those who did not. Between 1985 and 2010, 1,686 trauma patients receiving at least 1 U of packed red blood cells were identified from our prospectively maintained trauma registry. Demographic, physiologic, laboratory, injury, and outcome variables were reviewed. Univariate analysis determined significant factors between those who received MT and those who did not. A predictive multivariate logistic regression model with backward conditional stepwise elimination was used for MT risk. Statistical analysis was performed using SPSS PASW. MT patients had a higher pulse rate, lower Glasgow Coma Scale (GCS) score, lower systolic blood pressure, lower hemoglobin level, higher Injury Severity Score (ISS), higher international normalized ratio (INR), and longer stay. Initial logistic regression identified base deficit (BD), INR, and hemoperitoneum at laparotomy as independent predictive variables. After assigning cutoff points of BD being greater than 5 and an INR of 1.5 or greater, a further model was created. A BD greater than 5 and either INR of 1.5 or greater or hemoperitoneum was associated with 51 times increase in MT risk (odds ratio, 51.6; 95% confidence interval, 24.9-95.8). The area under the receiver operating characteristic curve for the model was 0.859. From this study, a combination of BD, INR, and hemoperitoneum has demonstrated good predictability for MT. This tool may assist in the determination of those patients who might benefit from hemostatic resuscitation. Prognostic study, level III.

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.

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...

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

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.

Identifying student difficulties with basic scientific reasoning skills: An example from control of variables

NASA Astrophysics Data System (ADS)

Boudreaux, Andrew

2006-05-01

Current national and local standards for the science learning of K-12 students emphasize both basic concepts (such as density) and fundamental reasoning skills (such as proportional reasoning, the interpretation of graphs, and the use of control of variables). At Western Washington University (WWU) and the University of Washington (UW), an effort is underway to examine the ability of university students to apply these same concepts and skills. Populations include students in liberal arts physics courses, introductory calculus-based physics courses, and special courses for the preparation of teachers. One focus of the research has been on the idea of control of variables. This topic is studied by students at all levels, from the primary grades, in which the notion of a ``fair test,'' is sometimes used, to university courses. This talk will discuss research tasks in which students are expected to infer from experimental data whether a particular variable influences (i.e., affects) or by itself determines (i.e., predicts) a given result. Student responses will be presented to identify specific difficulties.

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

Predicting the Dominant Patterns of Subseasonal Variability of Wintertime Surface Air Temperature in Extratropical Northern Hemisphere

NASA Astrophysics Data System (ADS)

Lin, Hai

2018-05-01

Skillfully predicting persistent extreme temperature anomalies more than 10 days in advance remains a challenge although it is of great value to the society. Here the two leading modes of subseasonal variability of surface air temperature over the extratropical Northern Hemisphere in boreal winter are identified with pentad (5 days) averaged data. They are well separated geographically, dominating temperature variability in North America and Eurasia, respectively. There exists a two-pentad lagged correlation between these two modes, implying an intercontinental link of temperature variability. Forecast skill of these two modes is evaluated based on three operational subseasonal prediction models. The results show that useful forecasts of the Eurasian mode (EOF2) can be achieved four pentads in advance, which is more skillful than the North American mode (EOF1). EOF2 is found to benefit from the Madden-Julian Oscillation signal in the initial condition.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

PubMed

Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

On the predictability of land surface fluxes from meteorological variables

NASA Astrophysics Data System (ADS)

Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.

2018-01-01

Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

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.

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

Accounting for Rainfall Spatial Variability in Prediction of Flash Floods

NASA Astrophysics Data System (ADS)

Saharia, M.; Kirstetter, P. E.; Gourley, J. J.; Hong, Y.; Vergara, H. J.

2016-12-01

Flash floods are a particularly damaging natural hazard worldwide in terms of both fatalities and property damage. In the United States, the lack of a comprehensive database that catalogues information related to flash flood timing, location, causative rainfall, and basin geomorphology has hindered broad characterization studies. First a representative and long archive of more than 20,000 flooding events during 2002-2011 is used to analyze the spatial and temporal variability of flash floods. We also derive large number of spatially distributed geomorphological and climatological parameters such as basin area, mean annual precipitation, basin slope etc. to identify static basin characteristics that influence flood response. For the same period, the National Severe Storms Laboratory (NSSL) has produced a decadal archive of Multi-Radar/Multi-Sensor (MRMS) radar-only precipitation rates at 1-km spatial resolution with 5-min temporal resolution. This provides an unprecedented opportunity to analyze the impact of event-level precipitation variability on flooding using a big data approach. To analyze the impact of sub-basin scale rainfall spatial variability on flooding, certain indices such as the first and second scaled moment of rainfall, horizontal gap, vertical gap etc. are computed from the MRMS dataset. Finally, flooding characteristics such as rise time, lag time, and peak discharge are linked to derived geomorphologic, climatologic, and rainfall indices to identify basin characteristics that drive flash floods. Next the model is used to predict flash flooding characteristics all over the continental U.S., specifically over regions poorly covered by hydrological observations. So far studies involving rainfall variability indices have only been performed on a case study basis, and a large scale approach is expected to provide a deeper insight into how sub-basin scale precipitation variability affects flooding. Finally, these findings are validated using the

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.

Marine heatwaves off eastern Tasmania: Trends, interannual variability, and predictability

NASA Astrophysics Data System (ADS)

Oliver, Eric C. J.; Lago, Véronique; Hobday, Alistair J.; Holbrook, Neil J.; Ling, Scott D.; Mundy, Craig N.

2018-02-01

Surface waters off eastern Tasmania are a global warming hotspot. Here, mean temperatures have been rising over several decades at nearly four times the global average rate, with concomitant changes in extreme temperatures - marine heatwaves. These changes have recently caused the marine biodiversity, fisheries and aquaculture industries off Tasmania's east coast to come under stress. In this study we quantify the long-term trends, variability and predictability of marine heatwaves off eastern Tasmania. We use a high-resolution ocean model for Tasmania's eastern continental shelf. The ocean state over the 1993-2015 period is hindcast, providing daily estimates of the three-dimensional temperature and circulation fields. Marine heatwaves are identified at the surface and subsurface from ocean temperature time series using a consistent definition. Trends in marine heatwave frequency are positive nearly everywhere and annual marine heatwave days and penetration depths indicate significant positive changes, particularly off southeastern Tasmania. A decomposition into modes of variability indicates that the East Australian Current is the dominant driver of marine heatwaves across the domain. Self-organising maps are used to identify 12 marine heatwave types, each with its own regionality, seasonality, and associated large-scale oceanic and atmospheric circulation patterns. The implications of this work for marine ecosystems and their management were revealed through review of past impacts and stakeholder discussions regarding use of these data.

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.

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

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.

Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.

PubMed

Roelen, Corné A M; Stapelfeldt, Christina M; Heymans, Martijn W; van Rhenen, Willem; Labriola, Merete; Nielsen, Claus V; Bültmann, Ute; Jensen, Chris

2015-06-01

To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated. 2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI). 1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model. The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.

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.

What variables are important in predicting bovine viral diarrhea virus? A random forest approach.

PubMed

Machado, Gustavo; Mendoza, Mariana Recamonde; Corbellini, Luis Gustavo

2015-07-24

Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.

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.

[Prediction of the nutritional status by anthropometrical variables and food safety at homes of pregnant women from Caracas, Venezuela].

PubMed

Pérez Guillén, A; Bernal Rivas, J

2006-01-01

The objective of this research is to analyze the nutritional status and household food security of a sample of healthy pregnant women who attend to external medicine service at Concepcion Palacios Maternity located in Caracas, Venezuela, and identify variables, which could predict the nutritional status of the evaluated group. This cross sectional, descriptive, comparative study evaluates a sample of 89 pregnant women, between 14 and 44 years of age. Economical, social, demographic and alimentary consumption variables and nutritional conditions were studied. On the way, anthropometrics like weight, height, and middle-arm circumference and Household food security scale were obtained. In order to perform the descriptive statistic, bivariate, and multiple linear regression analysis required during the investigation, the software SPSS, version 12, was used. The predictive variables considered for the evaluation of the actual nutritional status in pregnant women were: right middle-arm circumference, household food security level and the supplementation with vitamins and/or minerals. These variables explain 78.2% of the actual nutritional status variation in this sample. Therefore, this investigation highlights the importance of the research on simple variables, as a good prediction of the actual nutritional status in pregnant women, with acceptable precision values and without requiring high-trained personnel to perform it. Under these findings, is very important the study of more predictive variables to evaluate the nutritional and alimentary conditions, with practical and easy mechanisms that can be applied by non-technical personnel. It is recommended to go deep into the study of methods, which evaluate the nutrition in an easy and practical way, applied by non-technical personnel, besides continuing the validation process of the variable combinations determined as predictive of the nutritional status.

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.

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

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

Identifying black swans in NextGen: predicting human performance in off-nominal conditions.

PubMed

Wickens, Christopher D; Hooey, Becky L; Gore, Brian F; Sebok, Angelia; Koenicke, Corey S

2009-10-01

The objective is to validate a computational model of visual attention against empirical data--derived from a meta-analysis--of pilots' failure to notice safety-critical unexpected events. Many aircraft accidents have resulted, in part, because of failure to notice nonsalient unexpected events outside of foveal vision, illustrating the phenomenon of change blindness. A model of visual noticing, N-SEEV (noticing-salience, expectancy, effort, and value), was developed to predict these failures. First, 25 studies that reported objective data on miss rate for unexpected events in high-fidelity cockpit simulations were identified, and their miss rate data pooled across five variables (phase of flight, event expectancy, event location, presence of a head-up display, and presence of a highway-in-the-sky display). Second, the parameters of the N-SEEV model were tailored to mimic these dichotomies. The N-SEEV model output predicted variance in the obtained miss rate (r = .73). The individual miss rates of all six dichotomous conditions were predicted within 14%, and four of these were predicted within 7%. The N-SEEV model, developed on the basis of an independent data set, was able to successfully predict variance in this safety-critical measure of pilot response to abnormal circumstances, as collected from the literature. As new technology and procedures are envisioned for the future airspace, it is important to predict if these may compromise safety in terms of pilots' failing to notice unexpected events. Computational models such as N-SEEV support cost-effective means of making such predictions.

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.

Forced Expiratory Volume in 1 Second Variability Helps Identify Patients with Cystic Fibrosis at Risk of Greater Loss of Lung Function.

PubMed

Morgan, Wayne J; VanDevanter, Donald R; Pasta, David J; Foreman, Aimee J; Wagener, Jeffrey S; Konstan, Michael W

2016-02-01

To evaluate several alternative measures of forced expiratory volume in 1 second percent predicted (FEV1 %pred) variability as potential predictors of future FEV1 %pred decline in patients with cystic fibrosis. We included 13,827 patients age ≥6 years from the Epidemiologic Study of Cystic Fibrosis 1994-2002 with ≥4 FEV1 %pred measurements spanning ≥366 days in both a 2-year baseline period and a 2-year follow-up period. We predicted change from best baseline FEV1 %pred to best follow-up FEV1 %pred and change from baseline to best in the second follow-up year by using multivariable regression stratified by 4 lung-disease stages. We assessed 5 measures of variability (some as deviations from the best and some as deviations from the trend line) both alone and after controlling for demographic and clinical factors and for the slope and level of FEV1 %pred. All 5 measures of FEV1 %pred variability were predictive, but the strongest predictor was median deviation from the best FEV1 %pred in the baseline period. The contribution to explanatory power (R(2)) was substantial and exceeded the total contribution of all other factors excluding the FEV1 %pred rate of decline. Adding the other variability measures provided minimal additional value. Median deviation from the best FEV1 %pred is a simple metric that markedly improves prediction of FEV1 %pred decline even after the inclusion of demographic and clinical characteristics and the FEV1 %pred rate of decline. The routine calculation of this variability measure could allow clinicians to better identify patients at risk and therefore in need of increased intervention. Copyright © 2016 Elsevier Inc. All rights reserved.

Differentially Variable Component Analysis (dVCA): Identifying Multiple Evoked Components using Trial-to-Trial Variability

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.; Shah, Ankoor S.; Truccolo, Wilson; Ding, Ming-Zhou; Bressler, Steven L.; Schroeder, Charles E.

2003-01-01

Electric potentials and magnetic fields generated by ensembles of synchronously active neurons in response to external stimuli provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult as each detector records signals simultaneously generated by various regions throughout the brain. We introduce the differentially Variable Component Analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we evaluate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. Finally, we evaluate the technique using visually evoked field potentials recorded at incremental depths across the layers of cortical area VI, in an awake, behaving macaque monkey.

Generating temporal model using climate variables for the prediction of dengue cases in Subang Jaya, Malaysia

PubMed Central

Dom, Nazri Che; Hassan, A Abu; Latif, Z Abd; Ismail, Rodziah

2013-01-01

Objective To develop a forecasting model for the incidence of dengue cases in Subang Jaya using time series analysis. Methods The model was performed using the Autoregressive Integrated Moving Average (ARIMA) based on data collected from 2005 to 2010. The fitted model was then used to predict dengue incidence for the year 2010 by extrapolating dengue patterns using three different approaches (i.e. 52, 13 and 4 weeks ahead). Finally cross correlation between dengue incidence and climate variable was computed over a range of lags in order to identify significant variables to be included as external regressor. Results The result of this study revealed that the ARIMA (2,0,0) (0,0,1)52 model developed, closely described the trends of dengue incidence and confirmed the existence of dengue fever cases in Subang Jaya for the year 2005 to 2010. The prediction per period of 4 weeks ahead for ARIMA (2,0,0)(0,0,1)52 was found to be best fit and consistent with the observed dengue incidence based on the training data from 2005 to 2010 (Root Mean Square Error=0.61). The predictive power of ARIMA (2,0,0) (0,0,1)52 is enhanced by the inclusion of climate variables as external regressor to forecast the dengue cases for the year 2010. Conclusions The ARIMA model with weekly variation is a useful tool for disease control and prevention program as it is able to effectively predict the number of dengue cases in Malaysia.

Field potential soil variability index to identify precision agriculture opportunity

USDA-ARS?s Scientific Manuscript database

Precision agriculture (PA) technologies used for identifying and managing within-field variability are not widely used despite decades of advancement. Technological innovations in agronomic tools, such as canopy reflectance or electrical conductivity sensors, have created opportunities to achieve a ...

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

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.

Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction

PubMed Central

Shen, Li; Qi, Yuan; Kim, Sungeun; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Saykin, Andrew J.

2010-01-01

We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimer’s disease (AD) classification to make accurate prediction and identify critical imaging markers relevant to AD at the same time. ARD is one of the most successful Bayesian feature selection methods. PARD is a powerful Bayesian feature selection method, and provides sparse models that is easy to interpret. PARD selects the model with the best estimate of the predictive performance instead of choosing the one with the largest marginal model likelihood. Comparative study with support vector machine (SVM) shows that ARD/PARD in general outperform SVM in terms of prediction accuracy. Additional comparison with surface-based general linear model (GLM) analysis shows that regions with strongest signals are identified by both GLM and ARD/PARD. While GLM P-map returns significant regions all over the cortex, ARD/PARD provide a small number of relevant and meaningful imaging markers with predictive power, including both cortical and subcortical measures. PMID:20879451

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

Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns.

PubMed

Liu, Jin; Liao, Xuhong; Xia, Mingrui; He, Yong

2018-02-01

The human brain is a large, interacting dynamic network, and its architecture of coupling among brain regions varies across time (termed the "chronnectome"). However, very little is known about whether and how the dynamic properties of the chronnectome can characterize individual uniqueness, such as identifying individuals as a "fingerprint" of the brain. Here, we employed multiband resting-state functional magnetic resonance imaging data from the Human Connectome Project (N = 105) and a sliding time-window dynamic network analysis approach to systematically examine individual time-varying properties of the chronnectome. We revealed stable and remarkable individual variability in three dynamic characteristics of brain connectivity (i.e., strength, stability, and variability), which was mainly distributed in three higher order cognitive systems (i.e., default mode, dorsal attention, and fronto-parietal) and in two primary systems (i.e., visual and sensorimotor). Intriguingly, the spatial patterns of these dynamic characteristics of brain connectivity could successfully identify individuals with high accuracy and could further significantly predict individual higher cognitive performance (e.g., fluid intelligence and executive function), which was primarily contributed by the higher order cognitive systems. Together, our findings highlight that the chronnectome captures inherent functional dynamics of individual brain networks and provides implications for individualized characterization of health and disease. © 2017 Wiley Periodicals, Inc.

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and

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

NASA Astrophysics Data System (ADS)

Van Hooidonk, R. J.

2011-12-01

Future widespread coral bleaching and subsequent mortality has been projected with sea surface temperature (SST) data 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. These model weaknesses likely reduce the skill of coral bleaching predictions, 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 on coral reef bleaching predictive skill, various intra- and inter-annual frequency bands of observed SSTs were replaced with those frequencies from GCMs 20th century simulations to be included in the Intergovernmental Panel on Climate Change (IPCC) 5th 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 skill using an objective measure of forecast quality, the Peirce Skill Score (PSS). This methodology will identify frequency bands that are important to predicting coral bleaching and it will highlight deficiencies in these bands in models. The methodology we describe can be used to improve future climate model derived predictions of coral reef bleaching and it can be used to better characterize the errors and uncertainty in predictions.

Optical Spectra of Four Objects Identified with Variable Radio Sources

NASA Astrophysics Data System (ADS)

Chavushyan, V.; Mujica, R.; Gorshkov, A. G.; Konnikova, V. K.; Mingaliev, M. G.

2000-06-01

We obtained optical spectra of four objects identified with variable radio sources. Three objects (0029+0554, 0400+0550, 2245+0500) were found to be quasars with redshifts of 1.314, 0.761, and 1.091. One object (2349+0534) has a continuum spectrum characteristic of BL Lac objects. We analyze spectra of the radio sources in the range 0.97-21.7 GHz for the epoch 1997 and in the range 3.9-11.1 GHz for the epoch 1990, as well as the pattern of variability of their flux densities on time scales of 1.5 and 7 years.

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.

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.

Surgeon and type of anesthesia predict variability in surgical procedure times.

PubMed

Strum, D P; Sampson, A R; May, J H; Vargas, L G

2000-05-01

Variability in surgical procedure times increases the cost of healthcare delivery by increasing both the underutilization and overutilization of expensive surgical resources. To reduce variability in surgical procedure times, we must identify and study its sources. Our data set consisted of all surgeries performed over a 7-yr period at a large teaching hospital, resulting in 46,322 surgical cases. To study factors associated with variability in surgical procedure times, data mining techniques were used to segment and focus the data so that the analyses would be both technically and intellectually feasible. The data were subdivided into 40 representative segments of manageable size and variability based on headers adopted from the common procedural terminology classification. Each data segment was then analyzed using a main-effects linear model to identify and quantify specific sources of variability in surgical procedure times. The single most important source of variability in surgical procedure times was surgeon effect. Type of anesthesia, age, gender, and American Society of Anesthesiologists risk class were additional sources of variability. Intrinsic case-specific variability, unexplained by any of the preceding factors, was found to be highest for shorter surgeries relative to longer procedures. Variability in procedure times among surgeons was a multiplicative function (proportionate to time) of surgical time and total procedure time, such that as procedure times increased, variability in surgeons' surgical time increased proportionately. Surgeon-specific variability should be considered when building scheduling heuristics for longer surgeries. Results concerning variability in surgical procedure times due to factors such as type of anesthesia, age, gender, and American Society of Anesthesiologists risk class may be extrapolated to scheduling in other institutions, although specifics on individual surgeons may not. This research identifies factors associated

Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions.

PubMed

Teklehaimanot, Hailay D; Schwartz, Joel; Teklehaimanot, Awash; Lipsitch, Marc

2004-11-19

Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy. Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4th-degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts. The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10-25% more cases at a given sensitivity in cold districts than in hot ones. The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems.

Sequence-based predictive modeling to identify cancerlectins

PubMed Central

Lai, Hong-Yan; Chen, Xin-Xin; Chen, Wei; Tang, Hua; Lin, Hao

2017-01-01

Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer. However, the predictive accuracies of most of these techniques are very limited. In this work, by constructing a benchmark dataset based on the CancerLectinDB database, a new amino acid sequence-based strategy for feature description was developed, and then the binomial distribution was applied to screen the optimal feature set. Ultimately, an SVM-based predictor was performed to distinguish cancerlectins from non-cancerlectins, and achieved an accuracy of 77.48% with AUC of 85.52% in jackknife cross-validation. The results revealed that our prediction model could perform better comparing with published predictive tools. PMID:28423655

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

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.

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.

Diagnostic Value of Selected Echocardiographic Variables to Identify Pulmonary Hypertension in Dogs with Myxomatous Mitral Valve Disease.

PubMed

Tidholm, A; Höglund, K; Häggström, J; Ljungvall, I

2015-01-01

Pulmonary hypertension (PH) is commonly associated with myxomatous mitral valve disease (MMVD). Because dogs with PH present without measureable tricuspid regurgitation (TR), it would be useful to investigate echocardiographic variables that can identify PH. To investigate associations between estimated systolic TR pressure gradient (TRPG) and dog characteristics and selected echocardiographic variables. 156 privately owned dogs. Prospective observational study comparing the estimations of TRPG with dog characteristics and selected echocardiographic variables in dogs with MMVD and measureable TR. Tricuspid regurgitation pressure gradient was significantly (P < .05) associated with body weight corrected right (RVIDDn) and left (LVIDDn) ventricular end-diastolic and systolic (LVIDSn) internal diameters, pulmonary arterial (PA) acceleration to deceleration time ratio (AT/DT), heart rate, left atrial to aortic root ratio (LA/Ao), and the presence of congestive heart failure. Four variables remained significant in the multiple regression analysis with TRPG as a dependent variable: modeled as linear variables LA/Ao (P < .0001) and RVIDDn (P = .041), modeled as second order polynomial variables: AT/DT (P = .0039) and LVIDDn (P < .0001) The adjusted R(2) -value for the final model was 0.45 and receiver operating characteristic curve analysis suggested the model's performance to predict PH, defined as 36, 45, and 55 mmHg as fair (area under the curve [AUC] = 0.80), good (AUC = 0.86), and excellent (AUC = 0.92), respectively. In dogs with MMVD, the presence of PH might be suspected with the combination of decreased PA AT/DT, increased RVIDDn and LA/Ao, and a small or great LVIDDn. Copyright © 2015 The Authors Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

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.

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.

Spontaneous Fluctuations in Sensory Processing Predict Within-Subject Reaction Time Variability.

PubMed

Ribeiro, Maria J; Paiva, Joana S; Castelo-Branco, Miguel

2016-01-01

When engaged in a repetitive task our performance fluctuates from trial-to-trial. In particular, inter-trial reaction time variability has been the subject of considerable research. It has been claimed to be a strong biomarker of attention deficits, increases with frontal dysfunction, and predicts age-related cognitive decline. Thus, rather than being just a consequence of noise in the system, it appears to be under the control of a mechanism that breaks down under certain pathological conditions. Although the underlying mechanism is still an open question, consensual hypotheses are emerging regarding the neural correlates of reaction time inter-trial intra-individual variability. Sensory processing, in particular, has been shown to covary with reaction time, yet the spatio-temporal profile of the moment-to-moment variability in sensory processing is still poorly characterized. The goal of this study was to characterize the intra-individual variability in the time course of single-trial visual evoked potentials and its relationship with inter-trial reaction time variability. For this, we chose to take advantage of the high temporal resolution of the electroencephalogram (EEG) acquired while participants were engaged in a 2-choice reaction time task. We studied the link between single trial event-related potentials (ERPs) and reaction time using two different analyses: (1) time point by time point correlation analyses thereby identifying time windows of interest; and (2) correlation analyses between single trial measures of peak latency and amplitude and reaction time. To improve extraction of single trial ERP measures related with activation of the visual cortex, we used an independent component analysis (ICA) procedure. Our ERP analysis revealed a relationship between the N1 visual evoked potential and reaction time. The earliest time point presenting a significant correlation of its respective amplitude with reaction time occurred 175 ms after stimulus onset

Spontaneous Fluctuations in Sensory Processing Predict Within-Subject Reaction Time Variability

PubMed Central

Ribeiro, Maria J.; Paiva, Joana S.; Castelo-Branco, Miguel

2016-01-01

When engaged in a repetitive task our performance fluctuates from trial-to-trial. In particular, inter-trial reaction time variability has been the subject of considerable research. It has been claimed to be a strong biomarker of attention deficits, increases with frontal dysfunction, and predicts age-related cognitive decline. Thus, rather than being just a consequence of noise in the system, it appears to be under the control of a mechanism that breaks down under certain pathological conditions. Although the underlying mechanism is still an open question, consensual hypotheses are emerging regarding the neural correlates of reaction time inter-trial intra-individual variability. Sensory processing, in particular, has been shown to covary with reaction time, yet the spatio-temporal profile of the moment-to-moment variability in sensory processing is still poorly characterized. The goal of this study was to characterize the intra-individual variability in the time course of single-trial visual evoked potentials and its relationship with inter-trial reaction time variability. For this, we chose to take advantage of the high temporal resolution of the electroencephalogram (EEG) acquired while participants were engaged in a 2-choice reaction time task. We studied the link between single trial event-related potentials (ERPs) and reaction time using two different analyses: (1) time point by time point correlation analyses thereby identifying time windows of interest; and (2) correlation analyses between single trial measures of peak latency and amplitude and reaction time. To improve extraction of single trial ERP measures related with activation of the visual cortex, we used an independent component analysis (ICA) procedure. Our ERP analysis revealed a relationship between the N1 visual evoked potential and reaction time. The earliest time point presenting a significant correlation of its respective amplitude with reaction time occurred 175 ms after stimulus onset

A tree-based statistical classification algorithm (CHAID) for identifying variables responsible for the occurrence of faecal indicator bacteria during waterworks operations

NASA Astrophysics Data System (ADS)

Bichler, Andrea; Neumaier, Arnold; Hofmann, Thilo

2014-11-01

Microbial contamination of groundwater used for drinking water can affect public health and is of major concern to local water authorities and water suppliers. Potential hazards need to be identified in order to protect raw water resources. We propose a non-parametric data mining technique for exploring the presence of total coliforms (TC) in a groundwater abstraction well and its relationship to readily available, continuous time series of hydrometric monitoring parameters (seven year records of precipitation, river water levels, and groundwater heads). The original monitoring parameters were used to create an extensive generic dataset of explanatory variables by considering different accumulation or averaging periods, as well as temporal offsets of the explanatory variables. A classification tree based on the Chi-Squared Automatic Interaction Detection (CHAID) recursive partitioning algorithm revealed statistically significant relationships between precipitation and the presence of TC in both a production well and a nearby monitoring well. Different secondary explanatory variables were identified for the two wells. Elevated water levels and short-term water table fluctuations in the nearby river were found to be associated with TC in the observation well. The presence of TC in the production well was found to relate to elevated groundwater heads and fluctuations in groundwater levels. The generic variables created proved useful for increasing significance levels. The tree-based model was used to predict the occurrence of TC on the basis of hydrometric variables.

A SEARCH FOR SUB-SECOND RADIO VARIABILITY PREDICTED TO ARISE TOWARD 3C 84 FROM INTERGALACTIC DISPERSION

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

Hales, C. A.; Max-Moerbeck, W.; Roshi, D. A.

2016-06-01

We empirically evaluate the scheme proposed by Lieu and Duan in which the light curve of a time-steady radio source is predicted to exhibit increased variability on a characteristic timescale set by the sightline’s electron column density. Application to extragalactic sources is of significant appeal, as it would enable a unique and reliable probe of cosmic baryons. We examine temporal power spectra for 3C 84, observed at 1.7 GHz with the Karl G. Jansky Very Large Array and the Robert C. Byrd Green Bank Telescope. These data constrain the ratio between standard deviation and mean intensity for 3C 84 tomore » less than 0.05% at temporal frequencies ranging between 0.1 and 200 Hz. This limit is 3 orders of magnitude below the variability predicted by Lieu and Duan and is in accord with theoretical arguments presented by Hirata and McQuinn rebutting electron density dependence. We identify other spectral features in the data consistent with the slow solar wind, a coronal mass ejection, and the ionosphere.« less

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.

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.

A model for estimating pathogen variability in shellfish and predicting minimum depuration times.

PubMed

McMenemy, Paul; Kleczkowski, Adam; Lees, David N; Lowther, James; Taylor, Nick

2018-01-01

Norovirus is a major cause of viral gastroenteritis, with shellfish consumption being identified as one potential norovirus entry point into the human population. Minimising shellfish norovirus levels is therefore important for both the consumer's protection and the shellfish industry's reputation. One method used to reduce microbiological risks in shellfish is depuration; however, this process also presents additional costs to industry. Providing a mechanism to estimate norovirus levels during depuration would therefore be useful to stakeholders. This paper presents a mathematical model of the depuration process and its impact on norovirus levels found in shellfish. Two fundamental stages of norovirus depuration are considered: (i) the initial distribution of norovirus loads within a shellfish population and (ii) the way in which the initial norovirus loads evolve during depuration. Realistic assumptions are made about the dynamics of norovirus during depuration, and mathematical descriptions of both stages are derived and combined into a single model. Parameters to describe the depuration effect and norovirus load values are derived from existing norovirus data obtained from U.K. harvest sites. However, obtaining population estimates of norovirus variability is time-consuming and expensive; this model addresses the issue by assuming a 'worst case scenario' for variability of pathogens, which is independent of mean pathogen levels. The model is then used to predict minimum depuration times required to achieve norovirus levels which fall within possible risk management levels, as well as predictions of minimum depuration times for other water-borne pathogens found in shellfish. Times for Escherichia coli predicted by the model all fall within the minimum 42 hours required for class B harvest sites, whereas minimum depuration times for norovirus and FRNA+ bacteriophage are substantially longer. Thus this study provides relevant information and tools to assist

Identifying, characterizing and predicting spatial patterns of lacustrine groundwater discharge

NASA Astrophysics Data System (ADS)

Tecklenburg, Christina; Blume, Theresa

2017-10-01

Lacustrine groundwater discharge (LGD) can significantly affect lake water balances and lake water quality. However, quantifying LGD and its spatial patterns is challenging because of the large spatial extent of the aquifer-lake interface and pronounced spatial variability. This is the first experimental study to specifically study these larger-scale patterns with sufficient spatial resolution to systematically investigate how landscape and local characteristics affect the spatial variability in LGD. We measured vertical temperature profiles around a 0.49 km2 lake in northeastern Germany with a needle thermistor, which has the advantage of allowing for rapid (manual) measurements and thus, when used in a survey, high spatial coverage and resolution. Groundwater inflow rates were then estimated using the heat transport equation. These near-shore temperature profiles were complemented with sediment temperature measurements with a fibre-optic cable along six transects from shoreline to shoreline and radon measurements of lake water samples to qualitatively identify LGD patterns in the offshore part of the lake. As the hydrogeology of the catchment is sufficiently homogeneous (sandy sediments of a glacial outwash plain; no bedrock control) to avoid patterns being dominated by geological discontinuities, we were able to test the common assumptions that spatial patterns of LGD are mainly controlled by sediment characteristics and the groundwater flow field. We also tested the assumption that topographic gradients can be used as a proxy for gradients of the groundwater flow field. Thanks to the extensive data set, these tests could be carried out in a nested design, considering both small- and large-scale variability in LGD. We found that LGD was concentrated in the near-shore area, but alongshore variability was high, with specific regions of higher rates and higher spatial variability. Median inflow rates were 44 L m-2 d-1 with maximum rates in certain locations going

Interannual variability and predictability over the Arabian Penuinsula Winter monsoon region

NASA Astrophysics Data System (ADS)

Adnan Abid, Muhammad; Kucharski, Fred; Almazroui, Mansour; Kang, In-Sik

2016-04-01

Interannual winter rainfall variability and its predictability are analysed over the Arabian Peninsula region by using observed and hindcast datasets from the state-of-the-art European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal prediction System 4 for the period 1981-2010. An Arabian winter monsoon index (AWMI) is defined to highlight the Arabian Peninsula as the most representative region for the Northern Hemispheric winter dominating the summer rainfall. The observations show that the rainfall variability is relatively large over the northeast of the Arabian Peninsula. The correlation coefficient between the Nino3.4 index and rainfall in this region is 0.33, suggesting potentially some modest predictability, and indicating that El Nino increases and La Nina decreases the rainfall. Regression analysis shows that upper-level cyclonic circulation anomalies that are forced by El Nino Southern Oscillation (ENSO) are responsible for the winter rainfall anomalies over the Arabian region. The stronger (weaker) mean transient-eddy activity related to the upper-level trough induced by the warm (cold) sea-surface temperatures during El Nino (La Nina) tends to increase (decrease) the rainfall in the region. The model hindcast dataset reproduces the ENSO-rainfall connection. The seasonal mean predictability of the northeast Arabian rainfall index is 0.35. It is shown that the noise variance is larger than the signal over the Arabian Peninsula region, which tends to limit the prediction skill. The potential predictability is generally increased in ENSO years and is, in particular, larger during La Nina compared to El Nino years in the region. Furthermore, central Pacific ENSO events and ENSO events with weak signals in the Indian Ocean tend to increase predictability over the Arabian region.

Examining Preservice Science Teachers' Skills of Formulating Hypotheses and Identifying Variables

ERIC Educational Resources Information Center

Aydogdu, Bülent

2015-01-01

The aim of this study is to examine preservice science teachers' skills of formulating hypotheses and identifying variables. The research has a phenomenological research design. The data was gathered qualitatively. In this study, preservice science teachers were first given two scenarios (Scenario-1 & Scenario-2) containing two different…

Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions.

PubMed

Krishnamurthy, Dilip; Sumaria, Vaidish; Viswanathan, Venkatasubramanian

2018-02-01

Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔG opt . We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.

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…

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

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).

Strategies to design clinical studies to identify predictive biomarkers in cancer research.

PubMed

Perez-Gracia, Jose Luis; Sanmamed, Miguel F; Bosch, Ana; Patiño-Garcia, Ana; Schalper, Kurt A; Segura, Victor; Bellmunt, Joaquim; Tabernero, Josep; Sweeney, Christopher J; Choueiri, Toni K; Martín, Miguel; Fusco, Juan Pablo; Rodriguez-Ruiz, Maria Esperanza; Calvo, Alfonso; Prior, Celia; Paz-Ares, Luis; Pio, Ruben; Gonzalez-Billalabeitia, Enrique; Gonzalez Hernandez, Alvaro; Páez, David; Piulats, Jose María; Gurpide, Alfonso; Andueza, Mapi; de Velasco, Guillermo; Pazo, Roberto; Grande, Enrique; Nicolas, Pilar; Abad-Santos, Francisco; Garcia-Donas, Jesus; Castellano, Daniel; Pajares, María J; Suarez, Cristina; Colomer, Ramon; Montuenga, Luis M; Melero, Ignacio

2017-02-01

The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework-the DESIGN guidelines-to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

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.

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.

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.

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

Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects.

PubMed

Melillo, Paolo; Jovic, Alan; De Luca, Nicola; Pecchia, Leandro

2015-08-01

Accidental falls are a major problem of later life. Different technologies to predict falls have been investigated, but with limited success, mainly because of low specificity due to a high false positive rate. This Letter presents an automatic classifier based on heart rate variability (HRV) analysis with the goal to identify fallers automatically. HRV was used in this study as it is considered a good estimator of autonomic nervous system (ANS) states, which are responsible, among other things, for human balance control. Nominal 24 h electrocardiogram recordings from 168 cardiac patients (age 72 ± 8 years, 60 female), of which 47 were fallers, were investigated. Linear and nonlinear HRV properties were analysed in 30 min excerpts. Different data mining approaches were adopted and their performances were compared with a subject-based receiver operating characteristic analysis. The best performance was achieved by a hybrid algorithm, RUSBoost, integrated with feature selection method based on principal component analysis, which achieved satisfactory specificity and accuracy (80 and 72%, respectively), but low sensitivity (51%). These results suggested that ANS states causing falls could be reliably detected, but also that not all the falls were due to ANS states.

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.

Identifying variably saturated water-flow patterns in a steep hillslope under intermittent heavy rainfall

USGS Publications Warehouse

El-Kadi, A. I.; Torikai, J.D.

2001-01-01

The objective of this paper is to identify water-flow patterns in part of an active landslide, through the use of numerical simulations and data obtained during a field study. The approaches adopted include measuring rainfall events and pore-pressure responses in both saturated and unsaturated soils at the site. To account for soil variability, the Richards equation is solved within deterministic and stochastic frameworks. The deterministic simulations considered average water-retention data, adjusted retention data to account for stones or cobbles, retention functions for a heterogeneous pore structure, and continuous retention functions for preferential flow. The stochastic simulations applied the Monte Carlo approach which considers statistical distribution and autocorrelation of the saturated conductivity and its cross correlation with the retention function. Although none of the models is capable of accurately predicting field measurements, appreciable improvement in accuracy was attained using stochastic, preferential flow, and heterogeneous pore-structure models. For the current study, continuum-flow models provide reasonable accuracy for practical purposes, although they are expected to be less accurate than multi-domain preferential flow models.

Evaluation of the Predictive Validity of Thermography in Identifying Extravasation With Intravenous Chemotherapy Infusions.

PubMed

Matsui, Yuko; Murayama, Ryoko; Tanabe, Hidenori; Oe, Makoto; Motoo, Yoshiharu; Wagatsuma, Takanori; Michibuchi, Michiko; Kinoshita, Sachiko; Sakai, Keiko; Konya, Chizuko; Sugama, Junko; Sanada, Hiromi

Early detection of extravasation is important, but conventional methods of detection lack objectivity and reliability. This study evaluated the predictive validity of thermography for identifying extravasation during intravenous antineoplastic therapy. Of 257 patients who received chemotherapy through peripheral veins, extravasation was identified in 26. Thermography was performed every 15 to 30 minutes during the infusions. Sensitivity, specificity, positive predictive value, and negative predictive value using thermography were 84.6%, 94.8%, 64.7%, and 98.2%, respectively. This study showed that thermography offers an accurate prediction of extravasation.

Evaluation of the Predictive Validity of Thermography in Identifying Extravasation With Intravenous Chemotherapy Infusions

PubMed Central

Murayama, Ryoko; Tanabe, Hidenori; Oe, Makoto; Motoo, Yoshiharu; Wagatsuma, Takanori; Michibuchi, Michiko; Kinoshita, Sachiko; Sakai, Keiko; Konya, Chizuko; Sugama, Junko; Sanada, Hiromi

2017-01-01

Early detection of extravasation is important, but conventional methods of detection lack objectivity and reliability. This study evaluated the predictive validity of thermography for identifying extravasation during intravenous antineoplastic therapy. Of 257 patients who received chemotherapy through peripheral veins, extravasation was identified in 26. Thermography was performed every 15 to 30 minutes during the infusions. Sensitivity, specificity, positive predictive value, and negative predictive value using thermography were 84.6%, 94.8%, 64.7%, and 98.2%, respectively. This study showed that thermography offers an accurate prediction of extravasation. PMID:29112585

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 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

Female anthropometric variability and their effects on predicted thermoregulatory responses to work in the heat

NASA Astrophysics Data System (ADS)

Yokota, Miyo; Berglund, Larry G.; Bathalon, Gaston P.

2012-03-01

The use of thermoregulatory models for assessing physiological responses of workers in thermally stressful situations has been increasing because of the risks and costs related to human studies. In a previous study (Yokota et al. Eur J Appl Physiol 104:297-302, 2008), the effects of anthropometric variability on predicted physiological responses to heat stress in U.S. Army male soldiers were evaluated. Five somatotypes were identified in U.S. Army male multivariate anthropometric distribution. The simulated heat responses, using a thermoregulatory model, were different between somatotypes. The present study further extends this line of research to female soldiers. Anthropometric somatotypes were identified using multivariate analysis [height, weight, percent body fat (%BF)] and the predicted physiological responses to simulated exercise and heat stress using a thermoregulatory model were evaluated. The simulated conditions included walking at ~3 mph (4.8 km/h) for 300 min and wearing battle dress uniform and body armor in a 30°C, 25% relative humidity (RH) environment without solar radiation. Five major somatotypes (tall-fat, tall-lean, average, short-lean, and short-fat), identified through multivariate analysis of anthropometric distributions, showed different tolerance levels to simulated heat stress: lean women were predicted to maintain their core temperatures (Tc) lower than short-fat or tall-fat women. The measured Tc of female subjects obtained from two heat studies (data1: 30°C, 32% RH, protective garments, ~225 w·m-2 walk for 90 min; data2: 32°C, 75% RH, hot weather battle dress uniform, ~378 ± 32 w·m-2 for 30 min walk/30 min rest cycles for 120 min) were utilized for validation. Validation results agreed with the findings in this study: fat subjects tended to have higher core temperatures than medium individuals (data2) and lean subjects maintained lower core temperatures than medium subjects (data1).

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.

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…

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

The impacts of oceanic deep temperature perturbations in the North Atlantic on decadal climate variability and predictability

NASA Astrophysics Data System (ADS)

Germe, Agathe; Sévellec, Florian; Mignot, Juliette; Fedorov, Alexey; Nguyen, Sébastien; Swingedouw, Didier

2017-12-01

Decadal climate predictability in the North Atlantic is largely related to ocean low frequency variability, whose sensitivity to initial conditions is not very well understood. Recently, three-dimensional oceanic temperature anomalies optimally perturbing the North Atlantic Mean Temperature (NAMT) have been computed via an optimization procedure using a linear adjoint to a realistic ocean general circulation model. The spatial pattern of the identified perturbations, localized in the North Atlantic, has the largest magnitude between 1000 and 4000 m depth. In the present study, the impacts of these perturbations on NAMT, on the Atlantic meridional overturning circulation (AMOC), and on climate in general are investigated in a global coupled model that uses the same ocean model as was used to compute the three-dimensional optimal perturbations. In the coupled model, these perturbations induce AMOC and NAMT anomalies peaking after 5 and 10 years, respectively, generally consistent with the ocean-only linear predictions. To further understand their impact, their magnitude was varied in a broad range. For initial perturbations with a magnitude comparable to the internal variability of the coupled model, the model response exhibits a strong signature in sea surface temperature and precipitation over North America and the Sahel region. The existence and impacts of these ocean perturbations have important implications for decadal prediction: they can be seen either as a source of predictability or uncertainty, depending on whether the current observing system can detect them or not. In fact, comparing the magnitude of the imposed perturbations with the uncertainty of available ocean observations such as Argo data or ocean state estimates suggests that only the largest perturbations used in this study could be detectable. This highlights the importance for decadal climate prediction of accurate ocean density initialisation in the North Atlantic at intermediate and greater

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.

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.

Identifying Useful Auxiliary Variables for Incomplete Data Analyses: A Note on a Group Difference Examination Approach

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2014-01-01

This research note contributes to the discussion of methods that can be used to identify useful auxiliary variables for analyses of incomplete data sets. A latent variable approach is discussed, which is helpful in finding auxiliary variables with the property that if included in subsequent maximum likelihood analyses they may enhance considerably…

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…

Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables.

PubMed

Horiuchi, Yu; Tanimoto, Shuzou; Latif, A H M Mahbub; Urayama, Kevin Y; Aoki, Jiro; Yahagi, Kazuyuki; Okuno, Taishi; Sato, Yu; Tanaka, Tetsu; Koseki, Keita; Komiyama, Kota; Nakajima, Hiroyoshi; Hara, Kazuhiro; Tanabe, Kengo

2018-07-01

Acute heart failure (AHF) is a heterogeneous disease caused by various cardiovascular (CV) pathophysiology and multiple non-CV comorbidities. We aimed to identify clinically important subgroups to improve our understanding of the pathophysiology of AHF and inform clinical decision-making. We evaluated detailed clinical data of 345 consecutive AHF patients using non-hierarchical cluster analysis of 77 variables, including age, sex, HF etiology, comorbidities, physical findings, laboratory data, electrocardiogram, echocardiogram and treatment during hospitalization. Cox proportional hazards regression analysis was performed to estimate the association between the clusters and clinical outcomes. Three clusters were identified. Cluster 1 (n=108) represented "vascular failure". This cluster had the highest average systolic blood pressure at admission and lung congestion with type 2 respiratory failure. Cluster 2 (n=89) represented "cardiac and renal failure". They had the lowest ejection fraction (EF) and worst renal function. Cluster 3 (n=148) comprised mostly older patients and had the highest prevalence of atrial fibrillation and preserved EF. Death or HF hospitalization within 12-month occurred in 23% of Cluster 1, 36% of Cluster 2 and 36% of Cluster 3 (p=0.034). Compared with Cluster 1, risk of death or HF hospitalization was 1.74 (95% CI, 1.03-2.95, p=0.037) for Cluster 2 and 1.82 (95% CI, 1.13-2.93, p=0.014) for Cluster 3. Cluster analysis may be effective in producing clinically relevant categories of AHF, and may suggest underlying pathophysiology and potential utility in predicting clinical outcomes. Copyright © 2018 Elsevier B.V. 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.

Identifying Variability in Mental Models Within and Between Disciplines Caring for the Cardiac Surgical Patient.

PubMed

Brown, Evans K H; Harder, Kathleen A; Apostolidou, Ioanna; Wahr, Joyce A; Shook, Douglas C; Farivar, R Saeid; Perry, Tjorvi E; Konia, Mojca R

2017-07-01

The cardiac operating room is a complex environment requiring efficient and effective communication between multiple disciplines. The objectives of this study were to identify and rank critical time points during the perioperative care of cardiac surgical patients, and to assess variability in responses, as a correlate of a shared mental model, regarding the importance of these time points between and within disciplines. Using Delphi technique methodology, panelists from 3 institutions were tasked with developing a list of critical time points, which were subsequently assigned to pause point (PP) categories. Panelists then rated these PPs on a 100-point visual analog scale. Descriptive statistics were expressed as percentages, medians, and interquartile ranges (IQRs). We defined low response variability between panelists as an IQR ≤ 20, moderate response variability as an IQR > 20 and ≤ 40, and high response variability as an IQR > 40. Panelists identified a total of 12 PPs. The PPs identified by the highest number of panelists were (1) before surgical incision, (2) before aortic cannulation, (3) before cardiopulmonary bypass (CPB) initiation, (4) before CPB separation, and (5) at time of transfer of care from operating room (OR) to intensive care unit (ICU) staff. There was low variability among panelists' ratings of the PP "before surgical incision," moderate response variability for the PPs "before separation from CPB," "before transfer from OR table to bed," and "at time of transfer of care from OR to ICU staff," and high response variability for the remaining 8 PPs. In addition, the perceived importance of each of these PPs varies between disciplines and between institutions. Cardiac surgical providers recognize distinct critical time points during cardiac surgery. However, there is a high degree of variability within and between disciplines as to the importance of these times, suggesting an absence of a shared mental model among disciplines caring for

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.

Temporal effects in trend prediction: identifying the most popular nodes in the future.

PubMed

Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

2015-01-01

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.

Temporal Effects in Trend Prediction: Identifying the Most Popular Nodes in the Future

PubMed Central

Zhou, Yanbo; Zeng, An; Wang, Wei-Hong

2015-01-01

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail. PMID:25806810

Development and Validation of a Predictive Model to Identify Individuals Likely to Have Undiagnosed Chronic Obstructive Pulmonary Disease Using an Administrative Claims Database.

PubMed

Moretz, Chad; Zhou, Yunping; Dhamane, Amol D; Burslem, Kate; Saverno, Kim; Jain, Gagan; Devercelli, Giovanna; Kaila, Shuchita; Ellis, Jeffrey J; Hernandez, Gemzel; Renda, Andrew

2015-12-01

Despite the importance of early detection, delayed diagnosis of chronic obstructive pulmonary disease (COPD) is relatively common. Approximately 12 million people in the United States have undiagnosed COPD. Diagnosis of COPD is essential for the timely implementation of interventions, such as smoking cessation programs, drug therapies, and pulmonary rehabilitation, which are aimed at improving outcomes and slowing disease progression. To develop and validate a predictive model to identify patients likely to have undiagnosed COPD using administrative claims data. A predictive model was developed and validated utilizing a retro-spective cohort of patients with and without a COPD diagnosis (cases and controls), aged 40-89, with a minimum of 24 months of continuous health plan enrollment (Medicare Advantage Prescription Drug [MAPD] and commercial plans), and identified between January 1, 2009, and December 31, 2012, using Humana's claims database. Stratified random sampling based on plan type (commercial or MAPD) and index year was performed to ensure that cases and controls had a similar distribution of these variables. Cases and controls were compared to identify demographic, clinical, and health care resource utilization (HCRU) characteristics associated with a COPD diagnosis. Stepwise logistic regression (SLR), neural networking, and decision trees were used to develop a series of models. The models were trained, validated, and tested on randomly partitioned subsets of the sample (Training, Validation, and Test data subsets). Measures used to evaluate and compare the models included area under the curve (AUC); index of the receiver operating characteristics (ROC) curve; sensitivity, specificity, positive predictive value (PPV); and negative predictive value (NPV). The optimal model was selected based on AUC index on the Test data subset. A total of 50,880 cases and 50,880 controls were included, with MAPD patients comprising 92% of the study population. Compared

Identifying Decadal to Multi-decadal Variability in the Pacific by Empirical Mode Decomposition

NASA Astrophysics Data System (ADS)

Sommers, L. A.; Hamlington, B.; Cheon, S. H.

2016-12-01

Large scale climate variability in the Pacific Ocean like that associated with ENSO and the Pacific Decadal Oscillation (PDO) has been shown to have a significant impact on climate and sea level across a range of timescales. The changes related to these climate signals have worldwide impacts on fisheries, weather, and precipitation patterns among others. Understanding these inter-annual to multi-decadal oscillations is imperative to longer term climate forecasts and understanding how climate will behave, and its effect on changes in sea level. With a 110-year reconstruction of sea level, we examine decadal to multi-decadal variability seen in the sea level fluctuations in the Pacific Ocean. Using empirical mode decomposition (EMD), we break down regional sea level into a series of intrinsic mode functions (IMFs) and attempt attribution of these IMFs to specific climate modes of variability. In particular, and not unexpectedly, we identify IMFs associated with the PDO, finding correlations between the PDO Index and IMFs in the Pacific Ocean upwards of 0.6-0.8 over the 110-year reconstructed record. Perhaps more significantly, we also find evidence of a longer multi-decadal signal ( 50-60 years) in the higher order IMFs. This lower frequency variability has been suggested in previous literature as influencing GMSL, but here we find a regional pattern associated with this multi-decadal signal. By identifying and separating these periodic climate signals, we can gain a better understanding of how the sea level variability associated with these modes can impact sea level on short timescales and serve to exacerbate the effects of long-term sea level change.

Why significant variables aren't automatically good predictors.

PubMed

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

2015-11-10

Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%.

A new approach to hazardous materials transportation risk analysis: decision modeling to identify critical variables.

PubMed

Clark, Renee M; Besterfield-Sacre, Mary E

2009-03-01

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity, and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.

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

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.

Time Scales and Sources of European Temperature Variability

NASA Astrophysics Data System (ADS)

Årthun, Marius; Kolstad, Erik W.; Eldevik, Tor; Keenlyside, Noel S.

2018-04-01

Skillful predictions of continental climate would be of great practical benefit for society and stakeholders. It nevertheless remains fundamentally unresolved to what extent climate is predictable, for what features, at what time scales, and by which mechanisms. Here we identify the dominant time scales and sources of European surface air temperature (SAT) variability during the cold season using a coupled climate reanalysis, and a statistical method that estimates SAT variability due to atmospheric circulation anomalies. We find that eastern Europe is dominated by subdecadal SAT variability associated with the North Atlantic Oscillation, whereas interdecadal and multidecadal SAT variability over northern and southern Europe are thermodynamically driven by ocean temperature anomalies. Our results provide evidence that temperature anomalies in the North Atlantic Ocean are advected over land by the mean westerly winds and, hence, provide a mechanism through which ocean temperature controls the variability and provides predictability of European SAT.

Identifying dissolved oxygen variability and stress in tidal freshwater streams of northern New Zealand.

PubMed

Wilding, Thomas K; Brown, Edmund; Collier, Kevin J

2012-10-01

Tidal streams are ecologically important components of lotic network, and we identify dissolved oxygen (DO) depletion as a potentially important stressor in freshwater tidal streams of northern New Zealand. Other studies have examined temporal DO variability within rivers and we build on this by examining variability between streams as a basis for regional-scale predictors of risk for DO stress. Diel DO variability in these streams was driven by: (1) photosynthesis by aquatic plants and community respiration which produced DO maxima in the afternoon and minima early morning (range, 0.6-4.7 g/m(3)) as a product of the solar cycle and (2) tidal variability as a product of the lunar cycle, including saline intrusions with variable DO concentrations plus a small residual effect on freshwater DO for low-velocity streams. The lowest DO concentrations were observed during March (early autumn) when water temperatures and macrophyte biomass were high. Spatial comparisons indicated that low-gradient tidal streams were at greater risk of DO depletions harmful to aquatic life. Tidal influence was stronger in low-gradient streams, which typically drain more developed catchments, have lower reaeration potential and offer conditions more suitable for aquatic plant proliferation. Combined, these characteristics supported a simple method based on the extent of low-gradient channel for identifying coastal streams at risk of DO depletion. High-risk streams can then be targeted for riparian planting, nutrient limits and water allocation controls to reduce potential ecological stress.

An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

PubMed

Peng, Bin; Zhu, Dianwen; Ander, Bradley P; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.

Effect of Adding McKenzie Syndrome, Centralization, Directional Preference, and Psychosocial Classification Variables to a Risk-Adjusted Model Predicting Functional Status Outcomes for Patients With Lumbar Impairments.

PubMed

Werneke, Mark W; Edmond, Susan; Deutscher, Daniel; Ward, Jason; Grigsby, David; Young, Michelle; McGill, Troy; McClenahan, Brian; Weinberg, Jon; Davidow, Amy L

2016-09-01

the baseline model. Conclusion The small added prognostic capabilities identified when combining McKenzie or pain-pattern classifications with the SCL BPPM classification did not significantly improve prediction of FS outcomes in this study. Additional research is warranted to investigate the importance of classification variables compared with those used in the baseline model to maximize predictive power. Level of Evidence Prognosis, level 4. J Orthop Sports Phys Ther 2016;46(9):726-741. Epub 31 Jul 2016. doi:10.2519/jospt.2016.6266.

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.

Female anthropometric variability and their effects on predicted thermoregulatory responses to work in the heat.

PubMed

Yokota, Miyo; Berglund, Larry G; Bathalon, Gaston P

2012-03-01

The use of thermoregulatory models for assessing physiological responses of workers in thermally stressful situations has been increasing because of the risks and costs related to human studies. In a previous study (Yokota et al. Eur J Appl Physiol 104:297-302, 2008), the effects of anthropometric variability on predicted physiological responses to heat stress in U.S. Army male soldiers were evaluated. Five somatotypes were identified in U.S. Army male multivariate anthropometric distribution. The simulated heat responses, using a thermoregulatory model, were different between somatotypes. The present study further extends this line of research to female soldiers. Anthropometric somatotypes were identified using multivariate analysis [height, weight, percent body fat (%BF)] and the predicted physiological responses to simulated exercise and heat stress using a thermoregulatory model were evaluated. The simulated conditions included walking at ~3 mph (4.8 km/h) for 300 min and wearing battle dress uniform and body armor in a 30°C, 25% relative humidity (RH) environment without solar radiation. Five major somatotypes (tall-fat, tall-lean, average, short-lean, and short-fat), identified through multivariate analysis of anthropometric distributions, showed different tolerance levels to simulated heat stress: lean women were predicted to maintain their core temperatures (T(c)) lower than short-fat or tall-fat women. The measured T(c) of female subjects obtained from two heat studies (data1: 30°C, 32% RH, protective garments, ~225 w·m(-2) walk for 90 min; data2: 32°C, 75% RH, hot weather battle dress uniform, ~378 ± 32 w·m(-2) for 30 min walk/30 min rest cycles for 120 min) were utilized for validation. Validation results agreed with the findings in this study: fat subjects tended to have higher core temperatures than medium individuals (data2) and lean subjects maintained lower core temperatures than medium subjects (data1).

Accurate structure prediction of peptide–MHC complexes for identifying highly immunogenic antigens

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

Park, Min-Sun; Park, Sung Yong; Miller, Keith R.

2013-11-01

Designing an optimal HIV-1 vaccine faces the challenge of identifying antigens that induce a broad immune capacity. One factor to control the breadth of T cell responses is the surface morphology of a peptide–MHC complex. Here, we present an in silico protocol for predicting peptide–MHC structure. A robust signature of a conformational transition was identified during all-atom molecular dynamics, which results in a model with high accuracy. A large test set was used in constructing our protocol and we went another step further using a blind test with a wild-type peptide and two highly immunogenic mutants, which predicted substantial conformationalmore » changes in both mutants. The center residues at position five of the analogs were configured to be accessible to solvent, forming a prominent surface, while the residue of the wild-type peptide was to point laterally toward the side of the binding cleft. We then experimentally determined the structures of the blind test set, using high resolution of X-ray crystallography, which verified predicted conformational changes. Our observation strongly supports a positive association of the surface morphology of a peptide–MHC complex to its immunogenicity. Our study offers the prospect of enhancing immunogenicity of vaccines by identifying MHC binding immunogens.« less

Spatiotemporal variability and predictability of Normalized Difference Vegetation Index (NDVI) in Alberta, Canada.

PubMed

Jiang, Rengui; Xie, Jiancang; He, Hailong; Kuo, Chun-Chao; Zhu, Jiwei; Yang, Mingxiang

2016-09-01

As one of the most popular vegetation indices to monitor terrestrial vegetation productivity, Normalized Difference Vegetation Index (NDVI) has been widely used to study the plant growth and vegetation productivity around the world, especially the dynamic response of vegetation to climate change in terms of precipitation and temperature. Alberta is the most important agricultural and forestry province and with the best climatic observation systems in Canada. However, few studies pertaining to climate change and vegetation productivity are found. The objectives of this paper therefore were to better understand impacts of climate change on vegetation productivity in Alberta using the NDVI and provide reference for policy makers and stakeholders. We investigated the following: (1) the variations of Alberta's smoothed NDVI (sNDVI, eliminated noise compared to NDVI) and two climatic variables (precipitation and temperature) using non-parametric Mann-Kendall monotonic test and Thiel-Sen's slope; (2) the relationships between sNDVI and climatic variables, and the potential predictability of sNDVI using climatic variables as predictors based on two predicted models; and (3) the use of a linear regression model and an artificial neural network calibrated by the genetic algorithm (ANN-GA) to estimate Alberta's sNDVI using precipitation and temperature as predictors. The results showed that (1) the monthly sNDVI has increased during the past 30 years and a lengthened growing season was detected; (2) vegetation productivity in northern Alberta was mainly temperature driven and the vegetation in southern Alberta was predominantly precipitation driven for the period of 1982-2011; and (3) better performances of the sNDVI-climate relationships were obtained by nonlinear model (ANN-GA) than using linear (regression) model. Similar results detected in both monthly and summer sNDVI prediction using climatic variables as predictors revealed the applicability of two models for

Seasonal Variability Study of the Tropospheric Zenithal Delay in the South America using regional Numerical Weather Prediction model

NASA Astrophysics Data System (ADS)

Sapucci, L. F.; Monico, J. G.; Machado, L. T.

2007-05-01

events like: the penetration the cold front from Antarctic pole into the continent and occurrence of humidity convergence zones. In South America there are two main convergence zones that has strong influence in the troposphere variability, the ITCZ (Inter Tropical Convergence Zone) and the SACZ (South Atlantic Convergence Zone) zones. These convergence zones are characterized by an extensive precipitation band and high nebulosity almost stationary. The physical processes associated with these convergence zones present strong impacts in the variability of ZWD values. This work aims to contribute with ZTD modeling over South America continent using NWP to identify where and when the ZTD values present lower predictability in this region, and consequently, minimizing the error in the GNSS positioning that apply this technique.

PREDICT-PD: An online approach to prospectively identify risk indicators of Parkinson's disease.

PubMed

Noyce, Alastair J; R'Bibo, Lea; Peress, Luisa; Bestwick, Jonathan P; Adams-Carr, Kerala L; Mencacci, Niccolo E; Hawkes, Christopher H; Masters, Joseph M; Wood, Nicholas; Hardy, John; Giovannoni, Gavin; Lees, Andrew J; Schrag, Anette

2017-02-01

A number of early features can precede the diagnosis of Parkinson's disease (PD). To test an online, evidence-based algorithm to identify risk indicators of PD in the UK population. Participants aged 60 to 80 years without PD completed an online survey and keyboard-tapping task annually over 3 years, and underwent smell tests and genotyping for glucocerebrosidase (GBA) and leucine-rich repeat kinase 2 (LRRK2) mutations. Risk scores were calculated based on the results of a systematic review of risk factors and early features of PD, and individuals were grouped into higher (above 15th centile), medium, and lower risk groups (below 85th centile). Previously defined indicators of increased risk of PD ("intermediate markers"), including smell loss, rapid eye movement-sleep behavior disorder, and finger-tapping speed, and incident PD were used as outcomes. The correlation of risk scores with intermediate markers and movement of individuals between risk groups was assessed each year and prospectively. Exploratory Cox regression analyses with incident PD as the dependent variable were performed. A total of 1323 participants were recruited at baseline and >79% completed assessments each year. Annual risk scores were correlated with intermediate markers of PD each year and baseline scores were correlated with intermediate markers during follow-up (all P values < 0.001). Incident PD diagnoses during follow-up were significantly associated with baseline risk score (hazard ratio = 4.39, P = .045). GBA variants or G2019S LRRK2 mutations were found in 47 participants, and the predictive power for incident PD was improved by the addition of genetic variants to risk scores. The online PREDICT-PD algorithm is a unique and simple method to identify indicators of PD risk. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder

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

Melancholic depression prediction by identifying representative features in metabolic and microarray profiles with missing values.

PubMed

Nie, Zhi; Yang, Tao; Liu, Yashu; Li, Qingyang; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

2015-01-01

Recent studies have revealed that melancholic depression, one major subtype of depression, is closely associated with the concentration of some metabolites and biological functions of certain genes and pathways. Meanwhile, recent advances in biotechnologies have allowed us to collect a large amount of genomic data, e.g., metabolites and microarray gene expression. With such a huge amount of information available, one approach that can give us new insights into the understanding of the fundamental biology underlying melancholic depression is to build disease status prediction models using classification or regression methods. However, the existence of strong empirical correlations, e.g., those exhibited by genes sharing the same biological pathway in microarray profiles, tremendously limits the performance of these methods. Furthermore, the occurrence of missing values which are ubiquitous in biomedical applications further complicates the problem. In this paper, we hypothesize that the problem of missing values might in some way benefit from the correlation between the variables and propose a method to learn a compressed set of representative features through an adapted version of sparse coding which is capable of identifying correlated variables and addressing the issue of missing values simultaneously. An efficient algorithm is also developed to solve the proposed formulation. We apply the proposed method on metabolic and microarray profiles collected from a group of subjects consisting of both patients with melancholic depression and healthy controls. Results show that the proposed method can not only produce meaningful clusters of variables but also generate a set of representative features that achieve superior classification performance over those generated by traditional clustering and data imputation techniques. In particular, on both datasets, we found that in comparison with the competing algorithms, the representative features learned by the proposed

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

An Integrative Framework for Bayesian Variable Selection with Informative Priors for Identifying Genes and Pathways

PubMed Central

Ander, Bradley P.; Zhang, Xiaoshuai; Xue, Fuzhong; Sharp, Frank R.; Yang, Xiaowei

2013-01-01

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with ‘large p, small n’ problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed. PMID:23844055

In Search of Black Swans: Identifying Students at Risk of Failing Licensing Examinations.

PubMed

Barber, Cassandra; Hammond, Robert; Gula, Lorne; Tithecott, Gary; Chahine, Saad

2018-03-01

To determine which admissions variables and curricular outcomes are predictive of being at risk of failing the Medical Council of Canada Qualifying Examination Part 1 (MCCQE1), how quickly student risk of failure can be predicted, and to what extent predictive modeling is possible and accurate in estimating future student risk. Data from five graduating cohorts (2011-2015), Schulich School of Medicine & Dentistry, Western University, were collected and analyzed using hierarchical generalized linear models (HGLMs). Area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of predictive models and determine whether they could be used to predict future risk, using the 2016 graduating cohort. Four predictive models were developed to predict student risk of failure at admissions, year 1, year 2, and pre-MCCQE1. The HGLM analyses identified gender, MCAT verbal reasoning score, two preclerkship course mean grades, and the year 4 summative objective structured clinical examination score as significant predictors of student risk. The predictive accuracy of the models varied. The pre-MCCQE1 model was the most accurate at predicting a student's risk of failing (AUC 0.66-0.93), while the admissions model was not predictive (AUC 0.25-0.47). Key variables predictive of students at risk were found. The predictive models developed suggest, while it is not possible to identify student risk at admission, we can begin to identify and monitor students within the first year. Using such models, programs may be able to identify and monitor students at risk quantitatively and develop tailored intervention strategies.

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.

External forcing as a metronome for Atlantic multidecadal variability

NASA Astrophysics Data System (ADS)

Otterå, Odd Helge; Bentsen, Mats; Drange, Helge; Suo, Lingling

2010-10-01

Instrumental records, proxy data and climate modelling show that multidecadal variability is a dominant feature of North Atlantic sea-surface temperature variations, with potential impacts on regional climate. To understand the observed variability and to gauge any potential for climate predictions it is essential to identify the physical mechanisms that lead to this variability, and to explore the spatial and temporal characteristics of multidecadal variability modes. Here we use a coupled ocean-atmosphere general circulation model to show that the phasing of the multidecadal fluctuations in the North Atlantic during the past 600 years is, to a large degree, governed by changes in the external solar and volcanic forcings. We find that volcanoes play a particularly important part in the phasing of the multidecadal variability through their direct influence on tropical sea-surface temperatures, on the leading mode of northern-hemisphere atmosphere circulation and on the Atlantic thermohaline circulation. We suggest that the implications of our findings for decadal climate prediction are twofold: because volcanic eruptions cannot be predicted a decade in advance, longer-term climate predictability may prove challenging, whereas the systematic post-eruption changes in ocean and atmosphere may hold promise for shorter-term climate prediction.

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

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

Diatoms are used as indicators of freshwater ecosystems integrity. Developing diatom-based tools to assess impact of herbicide pollution is expected by water managers. But, defining sensitivities of all species to multiple herbicides would be unattainable. The existence of a phylogenetic signal of herbicide sensitivity was shown among diatoms and should enable prediction of new species sensitivity. However, diatoms present a cryptic diversity that may lead to variation in their sensitivity to herbicides that would need to be taken into account. Using bioassays, the sensitivity to four herbicides (Atrazine, Terbutryn, Diuron, Isoproturon) was evaluated for 11 freshwater diatom taxa and intraspecific variability was assessed for two of them (Nitzschia palea and Achnanthidium spp.). Intraspecific variability of herbicide sensitivity was always smaller than interspecific variability, but intraspecific variability was more important in N. palea than in Achnanthidium spp. Indeed, one species showed no intraspecific phylogenetic signal (N. palea) whereas the other did (Achnanthidium spp.). On one hand, species boundaries are not set properly for Achnanthidium spp. which encompass several taxa. On the other hand, there is a higher phenotypic plasticity for N. palea. Finally, a phylogenetic signal of herbicide sensitivity was measured at the interspecific level, opening up prospects for setting up reliable biomonitoring tools based on sensitivity prediction, insofar as species boundaries are correctly defined.

Predicting fifth-grade students' understanding of ecological science concepts with motivational and cognitive variables

NASA Astrophysics Data System (ADS)

Alao, Solomon

The need to identify factors that contribute to students' understanding of ecological concepts has been widely expressed in recent literature. The purpose of this study was to investigate the relationship between fifth grade students' prior knowledge, learning strategies, interest, and learning goals and their conceptual understanding of ecological science concepts. Subject were 72 students from three fifth grade classrooms located in a metropolitan area of the eastern United States. Students completed the goal commitment, interest, and strategy use questionnaire (GISQ), and a knowledge test designed to assess their prior knowledge and conceptual understanding of ecological science concepts. The learning goals scale assessed intentions to try to learn and understand ecological concepts. The interest scale assessed the feeling and value-related valences that students ascribed to science and ecological science concepts. The strategy use scale assessed the use of two cognitive strategies (monitoring and elaboration). The knowledge test assessed students' understanding of ecological concepts (the relationship between living organisms and their environment). Scores on all measures were examined for gender differences; no significant gender differences were observed. The motivational and cognitive variables contributed to students' understanding of ecological concepts. After accounting for interest, learning goals, and strategy use, prior knowledge accounted for 28% of the total variance in conceptual understanding. After accounting for prior knowledge, interest, learning goals, and strategy use explained 7%, 6%, and 4% of the total variance in conceptual understanding, respectively. More importantly, these variables were interrelated to each other and to conceptual understanding. After controlling for prior knowledge, learning goals, and strategy use, interest did not predict the variance in conceptual understanding. After controlling for prior knowledge, interest, and

Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils.

PubMed

Terra, Luciana A; Filgueiras, Paulo R; Tose, Lílian V; Romão, Wanderson; de Souza, Douglas D; de Castro, Eustáquio V R; de Oliveira, Mirela S L; Dias, Júlio C M; Poppi, Ronei J

2014-10-07

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.

Identified state-space prediction model for aero-optical wavefronts

NASA Astrophysics Data System (ADS)

Faghihi, Azin; Tesch, Jonathan; Gibson, Steve

2013-07-01

A state-space disturbance model and associated prediction filter for aero-optical wavefronts are described. The model is computed by system identification from a sequence of wavefronts measured in an airborne laboratory. Estimates of the statistics and flow velocity of the wavefront data are shown and can be computed from the matrices in the state-space model without returning to the original data. Numerical results compare velocity values and power spectra computed from the identified state-space model with those computed from the aero-optical data.

Identifying the independent effect of HbA1c variability on adverse health outcomes in patients with Type 2 diabetes.

PubMed

Prentice, J C; Pizer, S D; Conlin, P R

2016-12-01

To characterize the relationship between HbA 1c variability and adverse health outcomes among US military veterans with Type 2 diabetes. This retrospective cohort study used Veterans Affairs and Medicare claims for veterans with Type 2 diabetes taking metformin who initiated a second diabetes medication (n = 50 861). The main exposure of interest was HbA 1c variability during a 3-year baseline period. HbA 1c variability, categorized into quartiles, was defined as standard deviation, coefficient of variation and adjusted standard deviation, which accounted for the number and mean number of days between HbA 1c tests. Cox proportional hazard models predicted mortality, hospitalization for ambulatory care-sensitive conditions, and myocardial infarction or stroke and were controlled for mean HbA 1c levels and the direction of change in HbA 1c levels during the baseline period. Over a mean 3.3 years of follow-up, all HbA 1c variability measures significantly predicted each outcome. Using the adjusted standard deviation measure for HbA 1c variability, the hazard ratios for the third and fourth quartile predicting mortality were 1.14 (95% CI 1.04, 1.25) and 1.42 (95% CI 1.28, 1.58), for myocardial infarction and stroke they were 1.25 (95% CI 1.10, 1.41) and 1.23 (95% CI 1.07, 1.42) and for ambulatory-care sensitive condition hospitalization they were 1.10 (95% CI 1.03, 1.18) and 1.11 (95% CI 1.03, 1.20). Higher baseline HbA 1c levels independently predicted the likelihood of each outcome. In veterans with Type 2 diabetes, greater HbA 1c variability was associated with an increased risk of adverse long-term outcomes, independently of HbA 1c levels and direction of change. Limiting HbA 1c fluctuations over time may reduce complications. © 2016 Diabetes UK.

Meridional Propagation of the MJO/ISO and Prediction of Off-equatorial Monsoon Variability

NASA Technical Reports Server (NTRS)

Wu, Man Li C.; Schubert, S.; Suarez, M.; Pegion, P.; Waliser, D.

2003-01-01

This study was examine the links between tropical heating, the Madden Julian Oscillation (MJO)/Intraseasonal Oscillation (ISO), and the off-equatorial monsoon development. We examine both observations and idealized "MJO heating" experiments employing the NASA Seasonal-Interannual Prediction Project (NSIPP) atmospheric general circulation model (AGCM). In the simulations, the model is forced by climatological SST and an idealized eastward propagating heating profile that is meant 'to mimic the canonical heating associated with the MJO in the Indian Ocean and western Pacific. The observational analysis highlights the strong link between the Indian summer monsoon and the tropical ISO/MJO activity and heating. Here we focus on the potential for skillful predictions of the monsoon on sub-seasonal time scales associated with the meridional propagation of the ISO/MJO. In particular, we show that the variability of the Indian summer monsoon lags behind the variability of tropical ISO/MJO heating by about 15 days when the tropical heating is around 60E and 90E. This feature of the ISO/MJO is reproduced in the AGCM experiments with the idealized eastward propagating MJO-like heating, suggesting that models with realistic ISO/MJO variability should provide useful skill of monsoon breaks and surges on sub-seasonal time scales.

Meridional Propagation of the MJO/ISO and Prediction of Off-equatorial Monsoon Variability

NASA Technical Reports Server (NTRS)

Wu, Man Li C.; Schubert, S.; Suarez, M.; Pegion, P.; Bacmeister, J.; Waliser, D.

2004-01-01

In this study we examine the links between tropical heating, the Madden Julian Oscillation (MJO)/Intraseasonal Oscillation (ISO), and the off-equatorial monsoon development. We examine both observations and idealized "MJO heating" experiments employing the NASA Seasonal-Interannual Prediction Project (NSIPP) atmospheric general circulation model (AGCM). In the simulations, the model is forced by climatological SST and an idealized eastward propagating heating profile that is meant to mimic the canonical heating associated with the MJO in the Indian Ocean and western Pacific. The observational analysis highlights the strong link between the Indian summer monsoon and the tropical ISO/MJO activity and heating. Here we focus on the potential for skillful predictions of the monsoon on subseasonal time scales associated with the meridional propagation of the ISOMJO. In particular, we show that the variability of the Indian summer monsoon lags behind the variability of tropical ISOMJO heating by about 15 days when the tropical heating is around 60E and 90E. This feature of the ISOMJO is reproduced in the AGCM experiments with the idealized eastward propagating MJO-like heating, suggesting that models with realistic ISOM0 variability should provide useful skill of monsoon breaks and surges on subseasonal time scales.

Improved prediction of disturbed flow via hemodynamically-inspired geometric variables.

PubMed

Bijari, Payam B; Antiga, Luca; Gallo, Diego; Wasserman, Bruce A; Steinman, David A

2012-06-01

Arterial geometry has long been considered as a pragmatic alternative for inferring arterial flow disturbances, and their impact on the natural history and treatment of vascular diseases. Traditionally, definition of geometric variables is based on convenient shape descriptors, with only superficial consideration of their influence on flow and wall shear stress patterns. In the present study we demonstrate that a more studied consideration of the actual (cf. nominal) local hemodynamics can lead to substantial improvements in the prediction of disturbed flow by geometry. Starting from a well-characterized computational fluid dynamics (CFD) dataset of 50 normal carotid bifurcations, we observed that disturbed flow tended to be confined proximal to the flow divider, whereas geometric variables previously shown to be significant predictors of disturbed flow included features distal to the flow divider in their definitions. Flaring of the bifurcation leading to flow separation was redefined as the maximum relative expansion of the common carotid artery (CCA), proximal to the flow divider. The beneficial effect of primary curvature on flow inertia, via suppression of flow separation, was characterized by the in-plane tortuosity of CCA as it enters the flare region. Multiple linear regressions of these redefined geometric variables against various metrics of disturbed flow revealed R(2) values approaching 0.6, better than the roughly 0.3 achieved using the conventional shape-based variables, while maintaining their demonstrated real-world reproducibility. Such a hemodynamically-inspired approach to the definition of geometric variables may reap benefits for other applications where geometry is used as a surrogate marker of local hemodynamics. Copyright © 2012 Elsevier Ltd. All rights reserved.

Applying psychological theories to evidence-based clinical practice: identifying factors predictive of placing preventive fissure sealants.

PubMed

Bonetti, Debbie; Johnston, Marie; Clarkson, Jan E; Grimshaw, Jeremy; Pitts, Nigel B; Eccles, Martin; Steen, Nick; Thomas, Ruth; Maclennan, Graeme; Glidewell, Liz; Walker, Anne

2010-04-08

Psychological models are used to understand and predict behaviour in a wide range of settings, but have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. This study explored the usefulness of a range of models to predict an evidence-based behaviour -- the placing of fissure sealants. Measures were collected by postal questionnaire from a random sample of general dental practitioners (GDPs) in Scotland. Outcomes were behavioural simulation (scenario decision-making), and behavioural intention. Predictor variables were from the Theory of Planned Behaviour (TPB), Social Cognitive Theory (SCT), Common Sense Self-regulation Model (CS-SRM), Operant Learning Theory (OLT), Implementation Intention (II), Stage Model, and knowledge (a non-theoretical construct). Multiple regression analysis was used to examine the predictive value of each theoretical model individually. Significant constructs from all theories were then entered into a 'cross theory' stepwise regression analysis to investigate their combined predictive value. Behavioural simulation - theory level variance explained was: TPB 31%; SCT 29%; II 7%; OLT 30%. Neither CS-SRM nor stage explained significant variance. In the cross theory analysis, habit (OLT), timeline acute (CS-SRM), and outcome expectancy (SCT) entered the equation, together explaining 38% of the variance. Behavioural intention - theory level variance explained was: TPB 30%; SCT 24%; OLT 58%, CS-SRM 27%. GDPs in the action stage had significantly higher intention to place fissure sealants. In the cross theory analysis, habit (OLT) and attitude (TPB) entered the equation, together explaining 68% of the variance in intention. The study provides evidence that psychological models can be useful in understanding and predicting clinical behaviour. Taking a theory-based approach enables the creation of a replicable methodology for identifying factors that may predict clinical behaviour

Variability in Cadence During Forced Cycling Predicts Motor Improvement in Individuals With Parkinson’s Disease

PubMed Central

Ridgel, Angela L.; Abdar, Hassan Mohammadi; Alberts, Jay L.; Discenzo, Fred M.; Loparo, Kenneth A.

2014-01-01

Variability in severity and progression of Parkinson’s disease symptoms makes it challenging to design therapy interventions that provide maximal benefit. Previous studies showed that forced cycling, at greater pedaling rates, results in greater improvements in motor function than voluntary cycling. The precise mechanism for differences in function following exercise is unknown. We examined the complexity of biomechanical and physiological features of forced and voluntary cycling and correlated these features to improvements in motor function as measured by the Unified Parkinson’s Disease Rating Scale (UPDRS). Heart rate, cadence, and power were analyzed using entropy signal processing techniques. Pattern variability in heart rate and power were greater in the voluntary group when compared to forced group. In contrast, variability in cadence was higher during forced cycling. UPDRS Motor III scores predicted from the pattern variability data were highly correlated to measured scores in the forced group. This study shows how time series analysis methods of biomechanical and physiological parameters of exercise can be used to predict improvements in motor function. This knowledge will be important in the development of optimal exercise-based rehabilitation programs for Parkinson’s disease. PMID:23144045

A STATE-VARIABLE APPROACH FOR PREDICTING THE TIME REQUIRED FOR 50% RECRYSTALLIZATION

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

M. STOUT; ET AL

2000-08-01

It is important to be able to model the recrystallization kinetics in aluminum alloys during hot deformation. The industrial relevant process of hot rolling is an example of where the knowledge of whether or not a material recrystallizes is critical to making a product with the correct properties. Classically, the equations that describe the kinetics of recrystallization predict the time to 50% recrystallization. These equations are largely empirical; they are based on the free energy for recrystallization, a Zener-Holloman parameter, and have several adjustable exponents to fit the equation to engineering data. We have modified this form of classical theorymore » replacing the Zener-Hollomon parameter with a deformation energy increment, a free energy available to drive recrystallization. The advantage of this formulation is that the deformation energy increment is calculated based on the previously determined temperature and strain-rate sensitivity of the constitutive response. We modeled the constitutive response of the AA5182 aluminum using a state variable approach, the value of the state variable is a function of the temperature and strain-rate history of deformation. Thus, the recrystallization kinetics is a function of only the state variable and free energy for recrystallization. There are no adjustable exponents as in classical theory. Using this approach combined with engineering recrystallization data we have been able to predict the kinetics of recrystallization in AA5182 as a function of deformation strain rate and temperature.« less

Amniotic fluid index predicts the relief of variable decelerations after amnioinfusion bolus.

PubMed

Spong, C Y; McKindsey, F; Ross, M G

1996-10-01

Our purpose was to determine whether intrapartum amniotic fluid index before amnioinfusion can be used to predict response to therapeutic amnioinfusion. Intrapartum patients (n = 85) with repetitive variable decelerations in fetal heart rate that necessitated amnioinfusion (10 ml/min for 60 minutes) underwent determination of amniotic fluid index before and after bolus amnioinfusion. The fetal heart tracing was scored (scorer blinded to amniotic fluid index values) for number and characteristics of variable decelerations before and 1 hour after initiation of amnioinfusion. The amnioinfusion was considered successful if it resulted in a decrease of > or = 50% in total number of variable decelerations or a decrease of > or = 50% in the rate of atypical or severe variable decelerations after administration of the bolus. Spontaneous vaginal births before completion of administration of the bolus (n = 18) were excluded from analysis. The probability of success of amnioinfusion in relation to amniotic fluid index was analyzed with the chi(2) test for progressive sequence. The mean amniotic fluid index before amnioinfusion was 6.2 +/- 3.3 cm. An amniotic fluid index of < or = 5 cm was present in 40% of patients (27/67), and an amniotic fluid index of < or = 8 cm was present in 72% of patients (48/67). The probability of success of amnioinfusion decreased with increasing amniotic fluid index before amnioinfusion (76% [16/21] when initial amniotic fluid index was 0 to 4 cm, 63% [17/27] when initial amniotic fluid index was 4 to 8 cm, 44% [7/16] when initial amniotic fluid index was 8 to 12 cm, and 33% [1/3] when initial amniotic fluid index was > 12 cm, p = 0.03). The incidence of nuchal cords or true umbilical cord knots increased in relation to amniotic fluid index before amnioinfusion. Amniotic fluid index before amnioinfusion can be used to predict the success of amnioinfusion for relief of variable decelerations in fetal heart rate. Failure of amnioinfusion at a high

Portfolio theory of optimal isometric force production: Variability predictions and nonequilibrium fluctuation dissipation theorem

NASA Astrophysics Data System (ADS)

Frank, T. D.; Patanarapeelert, K.; Beek, P. J.

2008-05-01

We derive a fundamental relationship between the mean and the variability of isometric force. The relationship arises from an optimal collection of active motor units such that the force variability assumes a minimum (optimal isometric force). The relationship is shown to be independent of the explicit motor unit properties and of the dynamical features of isometric force production. A constant coefficient of variation in the asymptotic regime and a nonequilibrium fluctuation-dissipation theorem for optimal isometric force are predicted.

Prediction of endocrine stress reactions by means of personality variables.

PubMed

de Leeuwe, J N; Hentschel, U; Tavenier, R; Edelbroek, P

1992-06-01

The study examined the predictability of endocrine stress indicators on the basis of personality measures. The subjects were 83 computer operators (63 men, 20 women; mean age 28 years) who by means of an experimental situation were confronted with a mild stressor (a cognitive two-channel task with a high information load). Using scores on personality questionnaires (comprising scales for defense mechanisms, neuroticism, and 2 achievement motivation variables), subjects were classified into extreme groups of stress-resistant (17 subjects) versus nonstress-resistant (13 subjects). Immediately after the experiment blood samples were taken to assay the norepinephrine metabolites plasma-free 3-methoxy-4-hydroxy-phenylglycol (MHPG) and MHPG sulfate (MHPG.SO4), which formed the dependent variables. Personality measures and endocrine stress indicators were until the final analysis of the data kept apart by a double-blind strategy. A significant difference was noted in the MHPG level between the stress-resistant and the nonstress-resistant group. The value and applicability of these results for stress prevention is discussed.

Functional Analysis of Problem Behavior: A Systematic Approach for Identifying Idiosyncratic Variables

PubMed Central

Roscoe, Eileen M.; Schlichenmeyer, Kevin J.; Dube, William V.

2015-01-01

When inconclusive functional analysis (FA) outcomes occur, a number of modifications have been made to enhance the putative establishing operation or consequence associated with behavioral maintenance. However, a systematic method for identifying relevant events to test during modified FAs has not been evaluated. The purpose of this study was to develop and evaluate a technology for systematically identifying events to test in a modified FA after an initial FA led to inconclusive outcomes. Six individuals whose initial FA showed little or no responding or high levels only in the control condition participated. An indirect assessment (IA) questionnaire developed for identifying idiosyncratic variables was administered, and a descriptive analysis (DA) was conducted. Results from the IA only or a combination of the IA and DA were used to inform modified FA test and control conditions. Conclusive FA outcomes were obtained with five of the six participants during the modified FA phase. PMID:25930176

Variability and predictability of finals times of elite rowers.

PubMed

Smith, Tiaki Brett; Hopkins, Will G

2011-11-01

Little is known about the competitive performance characteristics of elite rowers. We report here analyses of performance times for finalists in world-class regattas from 1999 to 2009. The data were official race times for the 10 men's and 7 women's single and crewed boat classes, each with ∼ 200-300 different boats competing in 1-33 of the 46 regattas at 18 venues. A linear mixed model of race times for each boat class provided estimates of variability as coefficients of variation after adjustment for means of calendar year, level of competition (Olympics, world championship, World Cup), venue, and level of final (A, B, C, …). Mean performance was substantially slower between consecutive levels of competition (1.5%, 2.7%) and consecutive levels of finals (∼ 1%-2%). Differences in the effects of venue and of environmental conditions, estimated as variability in mean race time between venues and finals, were extremely large (∼ 3.0%). Within-boat race-to-race variability for A finalists was 1.1% for single sculls and 0.9% for crewed boats, with little difference between men and women and only a small increase in lower-level finalists. Predictability of performance, expressed as intraclass correlation coefficients, showed considerable differences between boat classes, but the mean was high (∼ 0.63), with little difference between crewed and single boats, between men and women, and between within and between years. The race-to-race variability of boat times of ∼ 1.0% is similar to that in comparable endurance sports performed against water or air resistance. Estimates of the smallest important performance enhancement (∼ 0.3%) and the effects of level of competition, level of final, venue, environment, and boat class will help inform investigations of factors affecting elite competitive rowing performance.

Investigation of adolescent accident predictive variables in hilly regions.

PubMed

Mohanty, Malaya; Gupta, Ankit

2016-09-01

The study aims to determine the significant personal and environmental factors in predicting the adolescent accidents in the hilly regions taking into account two cities Hamirpur and Dharamshala, which lie at an average elevation of 700--1000 metres above the mean sea level (MSL). Detailed comparisons between the results of 2 cities are also studied. The results are analyzed to provide the list of most significant factors responsible for adolescent accidents. Data were collected from different schools and colleges of the city with the help of a questionnaire survey. Around 690 responses from Hamirpur and 460 responses from Dharamshala were taken for study and analysis. Standard deviations (SD) of various factors affecting accidents were calculated and factors with relatively very low SD were discarded and other variables were considered for correlations. Correlation was developed using Kendall's-tau and chi-square tests and factors those were found significant were used for modelling. They were - the victim's age, the character of road, the speed of vehicle, and the use of helmet for Hamirpur and for Dharamshala, the kind of vehicle involved was an added variable found responsible for adolescent accidents. A logistic regression was performed to know the effect of each category present in a variable on the occurrence of accidents. Though the age and the speed of vehicle were considered to be important factors for accident occurrence according to Indian accident data records, even the use of helmet comes out as a major concern. The age group of 15-18 and 18-21 years were found to be more susceptible to accidents than the higher age groups. Due to the presence of hilly area, the character of road becomes a major concern for cause of accidents and the topography of the area makes the kind of vehicle involved as a major variable for determining the severity of accidents.

Using geomorphological variables to predict the spatial distribution of plant species in agricultural drainage networks

PubMed Central

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

Predicting missing links and identifying spurious links via likelihood analysis

NASA Astrophysics Data System (ADS)

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-03-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.

Predicting missing links and identifying spurious links via likelihood analysis

PubMed Central

Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun

2016-01-01

Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms. PMID:26961965

US Climate Variability and Predictability (CLIVAR) Project- Final Report

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

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

What weather variables are important in predicting heat-related mortality? A new application of statistical learning methods

PubMed Central

Zhang, Kai; Li, Yun; Schwartz, Joel D.; O'Neill, Marie S.

2014-01-01

Hot weather increases risk of mortality. Previous studies used different sets of weather variables to characterize heat stress, resulting in variation in heat-mortality- associations depending on the metric used. We employed a statistical learning method – random forests – to examine which of various weather variables had the greatest impact on heat-related mortality. We compiled a summertime daily weather and mortality counts dataset from four U.S. cities (Chicago, IL; Detroit, MI; Philadelphia, PA; and Phoenix, AZ) from 1998 to 2006. A variety of weather variables were ranked in predicting deviation from typical daily all-cause and cause-specific death counts. Ranks of weather variables varied with city and health outcome. Apparent temperature appeared to be the most important predictor of heat-related mortality for all-cause mortality. Absolute humidity was, on average, most frequently selected one of the top variables for all-cause mortality and seven cause-specific mortality categories. Our analysis affirms that apparent temperature is a reasonable variable for activating heat alerts and warnings, which are commonly based on predictions of total mortality in next few days. Additionally, absolute humidity should be included in future heat-health studies. Finally, random forests can be used to guide choice of weather variables in heat epidemiology studies. PMID:24834832

The balanced mind: the variability of task-unrelated thoughts predicts error monitoring

PubMed Central

Allen, Micah; Smallwood, Jonathan; Christensen, Joanna; Gramm, Daniel; Rasmussen, Beinta; Jensen, Christian Gaden; Roepstorff, Andreas; Lutz, Antoine

2013-01-01

Self-generated thoughts unrelated to ongoing activities, also known as “mind-wandering,” make up a substantial portion of our daily lives. Reports of such task-unrelated thoughts (TUTs) predict both poor performance on demanding cognitive tasks and blood-oxygen-level-dependent (BOLD) activity in the default mode network (DMN). However, recent findings suggest that TUTs and the DMN can also facilitate metacognitive abilities and related behaviors. To further understand these relationships, we examined the influence of subjective intensity, ruminative quality, and variability of mind-wandering on response inhibition and monitoring, using the Error Awareness Task (EAT). We expected to replicate links between TUT and reduced inhibition, and explored whether variance in TUT would predict improved error monitoring, reflecting a capacity to balance between internal and external cognition. By analyzing BOLD responses to subjective probes and the EAT, we dissociated contributions of the DMN, executive, and salience networks to task performance. While both response inhibition and online TUT ratings modulated BOLD activity in the medial prefrontal cortex (mPFC) of the DMN, the former recruited a more dorsal area implying functional segregation. We further found that individual differences in mean TUTs strongly predicted EAT stop accuracy, while TUT variability specifically predicted levels of error awareness. Interestingly, we also observed co-activation of salience and default mode regions during error awareness, supporting a link between monitoring and TUTs. Altogether our results suggest that although TUT is detrimental to task performance, fluctuations in attention between self-generated and external task-related thought is a characteristic of individuals with greater metacognitive monitoring capacity. Achieving a balance between internally and externally oriented thought may thus aid individuals in optimizing their task performance. PMID:24223545

Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability

PubMed Central

Sang, Shaowei; Yin, Wenwu; Bi, Peng; Zhang, Honglong; Wang, Chenggang; Liu, Xiaobo; Chen, Bin; Yang, Weizhong; Liu, Qiyong

2014-01-01

Introduction Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose. Methodology and Principal Findings Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time

Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability.

PubMed

Sang, Shaowei; Yin, Wenwu; Bi, Peng; Zhang, Honglong; Wang, Chenggang; Liu, Xiaobo; Chen, Bin; Yang, Weizhong; Liu, Qiyong

2014-01-01

Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue's control and prevention purpose. Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags. Imported DF cases and mosquito density play a

Identifying pollution sources and predicting urban air quality using ensemble learning methods

NASA Astrophysics Data System (ADS)

Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

2013-12-01

In this study, principal components analysis (PCA) was performed to identify air pollution sources and tree based ensemble learning models were constructed to predict the urban air quality of Lucknow (India) using the air quality and meteorological databases pertaining to a period of five years. PCA identified vehicular emissions and fuel combustion as major air pollution sources. The air quality indices revealed the air quality unhealthy during the summer and winter. Ensemble models were constructed to discriminate between the seasonal air qualities, factors responsible for discrimination, and to predict the air quality indices. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) were constructed and their generalization and predictive performance was evaluated in terms of several statistical parameters and compared with conventional machine learning benchmark, support vector machines (SVM). The DT and SVM models discriminated the seasonal air quality rendering misclassification rate (MR) of 8.32% (SDT); 4.12% (DTF); 5.62% (DTB), and 6.18% (SVM), respectively in complete data. The AQI and CAQI regression models yielded a correlation between measured and predicted values and root mean squared error of 0.901, 6.67 and 0.825, 9.45 (SDT); 0.951, 4.85 and 0.922, 6.56 (DTF); 0.959, 4.38 and 0.929, 6.30 (DTB); 0.890, 7.00 and 0.836, 9.16 (SVR) in complete data. The DTF and DTB models outperformed the SVM both in classification and regression which could be attributed to the incorporation of the bagging and boosting algorithms in these models. The proposed ensemble models successfully predicted the urban ambient air quality and can be used as effective tools for its management.

The Predictive Role of Values and Perceived Social Support Variables in Marital Adjustment

ERIC Educational Resources Information Center

Mert, Abdullah

2018-01-01

The aim of this study was to examine the predictive role of values and perceived social support variables in marital adjustment level among married individuals. A total of 422 (211 pairs) married individuals who agreed to participate voluntarily were included. The study was conducted in accordance with the relational screening model. "Dyadic…

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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 Geosphere-Biosphere Programme (IGBP), and the U.S. Global Change Research Program (USGCRP). The CVP program sits within the Earth System Science (ESS) Division at NOAA's Climate Program Office. Dynamics of the Madden-Julian Oscillation (DYNAMO): The Indian Ocean is one of Earth's most sensitive regions because the interactions between ocean and atmosphere there have a discernable effect on global climate patterns. The tropical weather that brews in that region can move eastward along the equator and reverberate around the globe, shaping weather and climate in far-off places. The vehicle for this variability is a phenomenon called the Madden-Julian Oscillation, or MJO. The MJO, which originates over the Indian Ocean roughly every 30 to 90 days, is known to influence the Asian and Australian monsoons. It can also enhance hurricane activity in the northeast Pacific and Gulf of Mexico, trigger torrential rainfall along the west coast of North America, and affect the onset of El Niño. CVP-funded scientists participated in the DYNAMO field campaign in 2011-12. Results from this international campaign are expected to improve researcher's insights into this influential phenomenon. A better understanding of the processes governing MJO is an essential step toward

Prediction of thoracic injury severity in frontal impacts by selected anatomical morphomic variables through model-averaged logistic regression approach.

PubMed

Zhang, Peng; Parenteau, Chantal; Wang, Lu; Holcombe, Sven; Kohoyda-Inglis, Carla; Sullivan, June; Wang, Stewart

2013-11-01

This study resulted in a model-averaging methodology that predicts crash injury risk using vehicle, demographic, and morphomic variables and assesses the importance of individual predictors. The effectiveness of this methodology was illustrated through analysis of occupant chest injuries in frontal vehicle crashes. The crash data were obtained from the International Center for Automotive Medicine (ICAM) database for calendar year 1996 to 2012. The morphomic data are quantitative measurements of variations in human body 3-dimensional anatomy. Morphomics are obtained from imaging records. In this study, morphomics were obtained from chest, abdomen, and spine CT using novel patented algorithms. A NASS-trained crash investigator with over thirty years of experience collected the in-depth crash data. There were 226 cases available with occupants involved in frontal crashes and morphomic measurements. Only cases with complete recorded data were retained for statistical analysis. Logistic regression models were fitted using all possible configurations of vehicle, demographic, and morphomic variables. Different models were ranked by the Akaike Information Criteria (AIC). An averaged logistic regression model approach was used due to the limited sample size relative to the number of variables. This approach is helpful when addressing variable selection, building prediction models, and assessing the importance of individual variables. The final predictive results were developed using this approach, based on the top 100 models in the AIC ranking. Model-averaging minimized model uncertainty, decreased the overall prediction variance, and provided an approach to evaluating the importance of individual variables. There were 17 variables investigated: four vehicle, four demographic, and nine morphomic. More than 130,000 logistic models were investigated in total. The models were characterized into four scenarios to assess individual variable contribution to injury risk. Scenario

Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

PubMed

Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

2017-07-15

New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier

Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands

USGS Publications Warehouse

Poitras, Travis; Villarreal, Miguel; Waller, Eric K.; Nauman, Travis; Miller, Mark E.; Duniway, Michael C.

2018-01-01

Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.

Predictive Models for Escherichia coli Concentrations at Inland Lake Beaches and Relationship of Model Variables to Pathogen Detection

PubMed Central

Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M. G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public. PMID:23291550

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

USGS Publications Warehouse

Francy, Donna S.; Stelzer, Erin A.; Duris, Joseph W.; Brady, Amie M.G.; Harrison, John H.; Johnson, Heather E.; Ware, Michael W.

2013-01-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection.

PubMed

Francy, Donna S; Stelzer, Erin A; Duris, Joseph W; Brady, Amie M G; Harrison, John H; Johnson, Heather E; Ware, Michael W

2013-03-01

Predictive models, based on environmental and water quality variables, have been used to improve the timeliness and accuracy of recreational water quality assessments, but their effectiveness has not been studied in inland waters. Sampling at eight inland recreational lakes in Ohio was done in order to investigate using predictive models for Escherichia coli and to understand the links between E. coli concentrations, predictive variables, and pathogens. Based upon results from 21 beach sites, models were developed for 13 sites, and the most predictive variables were rainfall, wind direction and speed, turbidity, and water temperature. Models were not developed at sites where the E. coli standard was seldom exceeded. Models were validated at nine sites during an independent year. At three sites, the model resulted in increased correct responses, sensitivities, and specificities compared to use of the previous day's E. coli concentration (the current method). Drought conditions during the validation year precluded being able to adequately assess model performance at most of the other sites. Cryptosporidium, adenovirus, eaeA (E. coli), ipaH (Shigella), and spvC (Salmonella) were found in at least 20% of samples collected for pathogens at five sites. The presence or absence of the three bacterial genes was related to some of the model variables but was not consistently related to E. coli concentrations. Predictive models were not effective at all inland lake sites; however, their use at two lakes with high swimmer densities will provide better estimates of public health risk than current methods and will be a valuable resource for beach managers and the public.

Identifying and predicting subgroups of information needs among cancer patients: an initial study using latent class analysis.

PubMed

Neumann, Melanie; Wirtz, Markus; Ernstmann, Nicole; Ommen, Oliver; Längler, Alfred; Edelhäuser, Friedrich; Scheffer, Christian; Tauschel, Diethard; Pfaff, Holger

2011-08-01

Understanding how the information needs of cancer patients (CaPts) vary is important because met information needs affect health outcomes and CaPts' satisfaction. The goals of the study were to identify subgroups of CaPts based on self-reported cancer- and treatment-related information needs and to determine whether subgroups could be predicted on the basis of selected sociodemographic, clinical and clinician-patient relationship variables. Three hundred twenty-three CaPts participated in a survey using the "Cancer Patients Information Needs" scale, which is a new tool for measuring cancer-related information needs. The number of information need subgroups and need profiles within each subgroup was identified using latent class analysis (LCA). Multinomial logistic regression was applied to predict class membership. LCA identified a model of five subgroups exhibiting differences in type and extent of CaPts' unmet information needs: a subgroup with "no unmet needs" (31.4% of the sample), two subgroups with "high level of psychosocial unmet information needs" (27.0% and 12.0%), a subgroup with "high level of purely medical unmet information needs" (16.0%) and a subgroup with "high level of medical and psychosocial unmet information needs" (13.6%). An assessment of sociodemographic and clinical characteristics revealed that younger CaPts and CaPts' requiring psychological support seem to belong to subgroups with a higher level of unmet information needs. However, the most significant predictor for the subgroups with unmet information needs is a good clinician-patient relationship, i.e. subjective perception of high level of trust in and caring attention from nurses together with high degree of physician empathy seems to be predictive for inclusion in the subgroup with no unmet information needs. The results of our study can be used by oncology nurses and physicians to increase their awareness of the complexity and heterogeneity of information needs among CaPts and of

Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models.

PubMed

Sae-Lim, Panya; Komen, Hans; Kause, Antti; Mulder, Han A

2014-02-26

Identifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes. Reaction norm and factor analytic models were used to identify which environmental variables (age at harvest, water temperature, oxygen, and photoperiod) may have caused the observed GxE interaction. Data on body weight at harvest was recorded on 8976 offspring reared in various locations: (1) a breeding environment in the USA (nucleus), (2) a recirculating aquaculture system in the Freshwater Institute in West Virginia, USA, (3) a high-altitude farm in Peru, and (4) a low-water temperature farm in Germany. Akaike and Bayesian information criteria were used to compare models. The combination of days to harvest multiplied with daily temperature (Day*Degree) and photoperiod were identified by the reaction norm model as the environmental variables responsible for the GxE interaction. The latent common factor that was identified by the factor analytic model showed the highest correlation with Day*Degree. Day*Degree and photoperiod were the environmental variables that differed most between Peru and other environments. Akaike and Bayesian information criteria indicated that the factor analytical model was more parsimonious than the reaction norm model. Day*Degree and photoperiod were identified as environmental

Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models

PubMed Central

2014-01-01

Background Identifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes. Methods Reaction norm and factor analytic models were used to identify which environmental variables (age at harvest, water temperature, oxygen, and photoperiod) may have caused the observed GxE interaction. Data on body weight at harvest was recorded on 8976 offspring reared in various locations: (1) a breeding environment in the USA (nucleus), (2) a recirculating aquaculture system in the Freshwater Institute in West Virginia, USA, (3) a high-altitude farm in Peru, and (4) a low-water temperature farm in Germany. Akaike and Bayesian information criteria were used to compare models. Results The combination of days to harvest multiplied with daily temperature (Day*Degree) and photoperiod were identified by the reaction norm model as the environmental variables responsible for the GxE interaction. The latent common factor that was identified by the factor analytic model showed the highest correlation with Day*Degree. Day*Degree and photoperiod were the environmental variables that differed most between Peru and other environments. Akaike and Bayesian information criteria indicated that the factor analytical model was more parsimonious than the reaction norm model. Conclusions Day*Degree and

The Role of Socio-Cognitive Variables in Predicting Learning Satisfaction in Smart Schools

ERIC Educational Resources Information Center

Firoozi, Mohammad Reza; Kazemi, Ali; Jokar, Maryam

2017-01-01

The present study aimed to investigate the role of Socio-Cognitive variables in predicting learning satisfaction in Smart Schools. The population was all the primary school students studying in smart schools in the city of Shiraz in the school year 2014-2015. The sample, randomly chosen through multi-stage cluster sampling, was 383 primary school…

A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999-2012.

PubMed

Stefanoff, Pawel; Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O; Rosinska, Magdalena

2018-04-04

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping.

Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis.

PubMed

Yao, Shi; Guo, Yan; Dong, Shan-Shan; Hao, Ruo-Han; Chen, Xiao-Feng; Chen, Yi-Xiao; Chen, Jia-Bin; Tian, Qing; Deng, Hong-Wen; Yang, Tie-Lin

2017-08-01

Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants. We further utilized Genetic Factors for Osteoporosis Consortium (GEFOS) and three in-house GWASs samples to validate the associations for predicted positive SNPs. The random forest classifier performed best among all machine learning methods with the F1 score of 0.8871. Using the optimized model, we predicted 37,584 candidate SNPs for osteoporosis. According to the meta-analysis results, a list of regulatory variants was significantly associated with osteoporosis after multiple testing corrections and contributed to the expression of known osteoporosis-associated protein-coding genes. In summary, combining GWASs and regulatory elements through machine learning could provide additional information for understanding the mechanism of osteoporosis. The regulatory variants we predicted will provide novel targets for etiology research and treatment of osteoporosis.

A variable capacitance based modeling and power capability predicting method for ultracapacitor

NASA Astrophysics Data System (ADS)

Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang

2018-01-01

Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

Potential breeding distributions of U.S. birds predicted with both short-term variability and long-term average climate data.

PubMed

Bateman, Brooke L; Pidgeon, Anna M; Radeloff, Volker C; Flather, Curtis H; VanDerWal, Jeremy; Akçakaya, H Resit; Thogmartin, Wayne E; Albright, Thomas P; Vavrus, Stephen J; Heglund, Patricia J

2016-12-01

Climate conditions, such as temperature or precipitation, averaged over several decades strongly affect species distributions, as evidenced by experimental results and a plethora of models demonstrating statistical relations between species occurrences and long-term climate averages. However, long-term averages can conceal climate changes that have occurred in recent decades and may not capture actual species occurrence well because the distributions of species, especially at the edges of their range, are typically dynamic and may respond strongly to short-term climate variability. Our goal here was to test whether bird occurrence models can be predicted by either covariates based on short-term climate variability or on long-term climate averages. We parameterized species distribution models (SDMs) based on either short-term variability or long-term average climate covariates for 320 bird species in the conterminous USA and tested whether any life-history trait-based guilds were particularly sensitive to short-term conditions. Models including short-term climate variability performed well based on their cross-validated area-under-the-curve AUC score (0.85), as did models based on long-term climate averages (0.84). Similarly, both models performed well compared to independent presence/absence data from the North American Breeding Bird Survey (independent AUC of 0.89 and 0.90, respectively). However, models based on short-term variability covariates more accurately classified true absences for most species (73% of true absences classified within the lowest quarter of environmental suitability vs. 68%). In addition, they have the advantage that they can reveal the dynamic relationship between species and their environment because they capture the spatial fluctuations of species potential breeding distributions. With this information, we can identify which species and guilds are sensitive to climate variability, identify sites of high conservation value where climate

Critical shear stress measurement of cohesive soils in streams: identifying device-dependent variability using an in-situ jet test device and conduit flume

NASA Astrophysics Data System (ADS)

Mahalder, B.; Schwartz, J. S.; Palomino, A.; Papanicolaou, T.

2016-12-01

Cohesive soil erodibility and threshold shear stress for stream bed and bank are dependent on both soil physical and geochemical properties in association with the channel vegetative conditions. These properties can be spatially variable therefore making critical shear stress measurement in cohesive soil challenging and leads to a need for a more comprehensive understanding of the erosional processes in streams. Several in-situ and flume-type test devices for estimating critical shear stress have been introduced by different researchers; however reported shear stress estimates per device vary widely in orders of magnitude. Advantages and disadvantages exist between these devices. Development of in-situ test devices leave the bed and/or bank material relatively undisturbed and can capture the variable nature of field soil conditions. However, laboratory flumes provide a means to control environmental conditions that can be quantify and tested. This study was conducted to observe differences in critical shear stress using jet tester and a well-controlled conduit flume. Soil samples were collected from the jet test locations and tested in a pressurized flume following standard operational procedure to calculate the critical shear stress. The results were compared using statistical data analysis (mean-separation ANOVA procedure) to identify possible differences. In addition to the device comparison, the mini jet device was used to measure critical shear stress across geologically diverse regions of Tennessee, USA. Statistical correlation between critical shear stress and the soil physical, and geochemical properties were completed identifying that geological origin plays a significant role in critical shear stress prediction for cohesive soils. Finally, the critical shear stress prediction equations using the jet test data were examined with possible suggestions to modify based on the flume test results.

Prediction of employer-employee relationships from sociodemographic variables and social values in Brunei public and private sector workers.

PubMed

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer-employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer-employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer-employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer-employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer-employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer-employee relationship problems. Other significant social value correlates, but not predictors of employer-employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer-employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the problem and its

Relationships between episodic memory performance prediction and sociodemographic variables among healthy older adults.

PubMed

de Oliveira, Glaucia Martins; Cachioni, Meire; Falcão, Deusivania; Batistoni, Samila; Lopes, Andrea; Guimarães, Vanessa; Lima-Silva, Thais Bento; Neri, Anita Liberalesso; Yassuda, Mônica Sanches

2015-01-01

Previous studies have suggested that performance prediction, an aspect of metamemory, may be associated with objective performance on memory tasks. The objective of the study was to describe memory prediction before performing an episodic memory task, in community-dwelling older adults, stratified by sex, age group and educational level. Additionally, the association between predicted and objective performance on a memory task was investigated. The study was based on data from 359 participants in the FIBRA study carried out at Ermelino Matarazzo, São Paulo. Memory prediction was assessed by posing the question: "If someone showed you a sheet with drawings of 10 pictures to observe for 30 seconds, how many pictures do you think you could remember without seeing the sheet?". Memory performance was assessed by the memorization of 10 black and white pictures from the Brief Cognitive Screening Battery (BCSB). No differences were found between men and women, nor for age group and educational level, in memory performance prediction before carrying out the memory task. There was a modest association (rho=0.11, p=0.041) between memory prediction and performance in immediate memory. On multivariate linear regression analyses, memory performance prediction was moderately significantly associated with immediate memory (p=0.061). In this study, sociodemographic variables did not influence memory prediction, which was only modestly associated with immediate memory on the Brief Cognitive Screening Battery (BCSB).

Trajectories of Substance Use Disorders in Youth: Identifying and Predicting Group Memberships

ERIC Educational Resources Information Center

Lee, Chih-Yuan S.; Winters, Ken C.; Wall, Melanie M.

2010-01-01

This study used latent class regression to identify latent trajectory classes based on individuals' diagnostic course of substance use disorders (SUDs) from late adolescence to early adulthood as well as to examine whether several psychosocial risk factors predicted the trajectory class membership. The study sample consisted of 310 individuals…

Accounting for rainfall spatial variability in the prediction of flash floods

NASA Astrophysics Data System (ADS)

Saharia, Manabendra; Kirstetter, Pierre-Emmanuel; Gourley, Jonathan J.; Hong, Yang; Vergara, Humberto; Flamig, Zachary L.

2017-04-01

Flash floods are a particularly damaging natural hazard worldwide in terms of both fatalities and property damage. In the United States, the lack of a comprehensive database that catalogues information related to flash flood timing, location, causative rainfall, and basin geomorphology has hindered broad characterization studies. First a representative and long archive of more than 15,000 flooding events during 2002-2011 is used to analyze the spatial and temporal variability of flash floods. We also derive large number of spatially distributed geomorphological and climatological parameters such as basin area, mean annual precipitation, basin slope etc. to identify static basin characteristics that influence flood response. For the same period, the National Severe Storms Laboratory (NSSL) has produced a decadal archive of Multi-Radar/Multi-Sensor (MRMS) radar-only precipitation rates at 1-km spatial resolution with 5-min temporal resolution. This provides an unprecedented opportunity to analyze the impact of event-level precipitation variability on flooding using a big data approach. To analyze the impact of sub-basin scale rainfall spatial variability on flooding, certain indices such as the first and second scaled moment of rainfall, horizontal gap, vertical gap etc. are computed from the MRMS dataset. Finally, flooding characteristics such as rise time, lag time, and peak discharge are linked to derived geomorphologic, climatologic, and rainfall indices to identify basin characteristics that drive flash floods. The database has been subjected to rigorous quality control by accounting for radar beam height and percentage snow in basins. So far studies involving rainfall variability indices have only been performed on a case study basis, and a large scale approach is expected to provide a deeper insight into how sub-basin scale precipitation variability affects flooding. Finally, these findings are validated using the National Weather Service storm reports and a

Estimate variable importance for recurrent event outcomes with an application to identify hypoglycemia risk factors.

PubMed

Duan, Ran; Fu, Haoda

2015-08-30

Recurrent event data are an important data type for medical research. In particular, many safety endpoints are recurrent outcomes, such as hypoglycemic events. For such a situation, it is important to identify the factors causing these events and rank these factors by their importance. Traditional model selection methods are not able to provide variable importance in this context. Methods that are able to evaluate the variable importance, such as gradient boosting and random forest algorithms, cannot directly be applied to recurrent events data. In this paper, we propose a two-step method that enables us to evaluate the variable importance for recurrent events data. We evaluated the performance of our proposed method by simulations and applied it to a data set from a diabetes study. Copyright © 2015 John Wiley & Sons, Ltd.

Review of Factors, Methods, and Outcome Definition in Designing Opioid Abuse Predictive Models.

PubMed

Alzeer, Abdullah H; Jones, Josette; Bair, Matthew J

2018-05-01

Several opioid risk assessment tools are available to prescribers to evaluate opioid analgesic abuse among chronic patients. The objectives of this study are to 1) identify variables available in the literature to predict opioid abuse; 2) explore and compare methods (population, database, and analysis) used to develop statistical models that predict opioid abuse; and 3) understand how outcomes were defined in each statistical model predicting opioid abuse. The OVID database was searched for this study. The search was limited to articles written in English and published from January 1990 to April 2016. This search generated 1,409 articles. Only seven studies and nine models met our inclusion-exclusion criteria. We found nine models and identified 75 distinct variables. Three studies used administrative claims data, and four studies used electronic health record data. The majority, four out of seven articles (six out of nine models), were primarily dependent on the presence or absence of opioid abuse or dependence (ICD-9 diagnosis code) to define opioid abuse. However, two articles used a predefined list of opioid-related aberrant behaviors. We identified variables used to predict opioid abuse from electronic health records and administrative data. Medication variables are the recurrent variables in the articles reviewed (33 variables). Age and gender are the most consistent demographic variables in predicting opioid abuse. Overall, there is similarity in the sampling method and inclusion/exclusion criteria (age, number of prescriptions, follow-up period, and data analysis methods). Intuitive research to utilize unstructured data may increase opioid abuse models' accuracy.

Self-Regulated Learning and Ethnic/Racial Variables: Predicting Minority First-Generation College Students' Persistence

ERIC Educational Resources Information Center

Moore, John S., III.

2013-01-01

The purpose of this study was to investigate how self-regulated learning and ethnic/racial variables predict minority first-generation college student persistence and related constructs. Participants were drawn nationally from the U.S. Department of Education funded TRiO Student Support Services Programs. Additional participants from the Talent…

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

Multi-pentad prediction of precipitation variability over Southeast Asia during boreal summer using BCC_CSM1.2

NASA Astrophysics Data System (ADS)

Li, Chengcheng; Ren, Hong-Li; Zhou, Fang; Li, Shuanglin; Fu, Joshua-Xiouhua; Li, Guoping

2018-06-01

Precipitation is highly variable in space and discontinuous in time, which makes it challenging for models to predict on subseasonal scales (10-30 days). We analyze multi-pentad predictions from the Beijing Climate Center Climate System Model version 1.2 (BCC_CSM1.2), which are based on hindcasts from 1997 to 2014. The analysis focus on the skill of the model to predict precipitation variability over Southeast Asia from May to September, as well as its connections with intraseasonal oscillation (ISO). The effective precipitation prediction length is about two pentads (10 days), during which the skill measured by anomaly correlation is greater than 0.1. In order to further evaluate the performance of the precipitation prediction, the diagnosis results of the skills of two related circulation fields show that the prediction skills for the circulation fields exceed that of precipitation. Moreover, the prediction skills tend to be higher when the amplitude of ISO is large, especially for a boreal summer intraseasonal oscillation. The skills associated with phases 2 and 5 are higher, but that of phase 3 is relatively lower. Even so, different initial phases reflect the same spatial characteristics, which shows higher skill of precipitation prediction in the northwest Pacific Ocean. Finally, filter analysis is used on the prediction skills of total and subseasonal anomalies. The results of the two anomaly sets are comparable during the first two lead pentads, but thereafter the skill of the total anomalies is significantly higher than that of the subseasonal anomalies. This paper should help advance research in subseasonal precipitation prediction.

A prediction model of compressor with variable-geometry diffuser based on elliptic equation and partial least squares.

PubMed

Li, Xu; Yang, Chuanlei; Wang, Yinyan; Wang, Hechun

2018-01-01

To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.

Prediction of rectal temperature using non-invasive physiologic variable measurements in hair pregnant ewes subjected to natural conditions of heat stress.

PubMed

Vicente-Pérez, Ricardo; Avendaño-Reyes, Leonel; Mejía-Vázquez, Ángel; Álvarez-Valenzuela, F Daniel; Correa-Calderón, Abelardo; Mellado, Miguel; Meza-Herrera, Cesar A; Guerra-Liera, Juan E; Robinson, P H; Macías-Cruz, Ulises

2016-01-01

Rectal temperature (RT) is the foremost physiological variable indicating if an animal is suffering hyperthermia. However, this variable is traditionally measured by invasive methods, which may compromise animal welfare. Models to predict RT have been developed for growing pigs and lactating dairy cows, but not for pregnant heat-stressed ewes. Our aim was to develop a prediction equation for RT using non-invasive physiological variables in pregnant ewes under heat stress. A total of 192 records of respiratory frequency (RF) and hair coat temperature in various body regions (i.e., head, rump, flank, shoulder, and belly) obtained from 24 Katahdin × Pelibuey pregnant multiparous ewes were collected during the last third of gestation (i.e., d 100 to lambing) with a 15 d sampling interval. Hair coat temperatures were taken using infrared thermal imaging technology. Initially, a Pearson correlation analysis examined the relationship among variables, and then multiple linear regression analysis was used to develop the prediction equations. All predictor variables were positively correlated (P<0.01; r=0.59-0.67) with RT. The adjusted equation which best predicted RT (P<0.01; Radj(2)=56.15%; CV=0.65%) included as predictors RF and head and belly temperatures. Comparison of predicted and observed values for RT indicates a suitable agreement (P<0.01) between them with moderate accuracy (Radj(2)=56.15%) when RT was calculated with the adjusted equation. In general, the final equation does not violate any assumption of multiple regression analysis. The RT in heat-stressed pregnant ewes can be predicted with an adequate accuracy using non-invasive physiologic variables, and the final equation was: RT=35.57+0.004 (RF)+0.067 (heat temperature)+0.028 (belly temperature). Copyright © 2015 Elsevier Ltd. All rights reserved.

Using variable rate models to identify genes under selection in sequence pairs: their validity and limitations for EST sequences.

PubMed

Church, Sheri A; Livingstone, Kevin; Lai, Zhao; Kozik, Alexander; Knapp, Steven J; Michelmore, Richard W; Rieseberg, Loren H

2007-02-01

Using likelihood-based variable selection models, we determined if positive selection was acting on 523 EST sequence pairs from two lineages of sunflower and lettuce. Variable rate models are generally not used for comparisons of sequence pairs due to the limited information and the inaccuracy of estimates of specific substitution rates. However, previous studies have shown that the likelihood ratio test (LRT) is reliable for detecting positive selection, even with low numbers of sequences. These analyses identified 56 genes that show a signature of selection, of which 75% were not identified by simpler models that average selection across codons. Subsequent mapping studies in sunflower show four of five of the positively selected genes identified by these methods mapped to domestication QTLs. We discuss the validity and limitations of using variable rate models for comparisons of sequence pairs, as well as the limitations of using ESTs for identification of positively selected genes.

Explaining and forecasting interannual variability in the flow of the Nile River

NASA Astrophysics Data System (ADS)

Siam, M. S.; Eltahir, E. A. B.

2014-05-01

The natural interannual variability in the flow of Nile River had a significant impact on the ancient civilizations and cultures that flourished on the banks of the river. This is evident from stories in the Bible and Koran, and from the numerous Nilometers discovered near ancient temples. Here, we analyze extensive data sets collected during the 20th century and define four modes of natural variability in the flow of Nile River, identifying a new significant potential for improving predictability of floods and droughts. Previous studies have identified a significant teleconnection between the Nile flow and the Eastern Pacific Ocean. El Niño-Southern Oscillation (ENSO) explains about 25% of the interannual variability in the Nile flow. Here, we identify, for the first time, a region in the southern Indian Ocean with similarly strong teleconnection to the Nile flow. Sea Surface Temperature (SST) in the region (50-80° E and 25-35° S) explains 28% of the interannual variability in the Nile flow. During those years with anomalous SST conditions in both Oceans, we estimate that indices of the SSTs in the Pacific and Indian Oceans can collectively explain up to 84% of the interannual variability in the flow of Nile. Building on these findings, we use classical Bayesian theorem to develop a new hybrid forecasting algorithm that predicts the Nile flow based on global models predictions of indices of the SST in the Eastern Pacific and Southern Indian Oceans.

A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability

PubMed Central

Takemura, Naohiro; Fukui, Takao; Inui, Toshio

2015-01-01

In human reach-to-grasp movement, visual occlusion of a target object leads to a larger peak grip aperture compared to conditions where online vision is available. However, no previous computational and neural network models for reach-to-grasp movement explain the mechanism of this effect. We simulated the effect of online vision on the reach-to-grasp movement by proposing a computational control model based on the hypothesis that the grip aperture is controlled to compensate for both motor variability and sensory uncertainty. In this model, the aperture is formed to achieve a target aperture size that is sufficiently large to accommodate the actual target; it also includes a margin to ensure proper grasping despite sensory and motor variability. To this end, the model considers: (i) the variability of the grip aperture, which is predicted by the Kalman filter, and (ii) the uncertainty of the object size, which is affected by visual noise. Using this model, we simulated experiments in which the effect of the duration of visual occlusion was investigated. The simulation replicated the experimental result wherein the peak grip aperture increased when the target object was occluded, especially in the early phase of the movement. Both predicted motor variability and sensory uncertainty play important roles in the online visuomotor process responsible for grip aperture control. PMID:26696874

Variability and predictability of performance times of elite cross-country skiers.

PubMed

Spencer, Matt; Losnegard, Thomas; Hallén, Jostein; Hopkins, Will G

2014-01-01

Analyses of elite competitive performance provide useful information for research and practical applications. Here the authors analyze performance times of cross-country skiers at international competitions (World Cup, World Championship, and Olympics) in classical and free styles of women's and men's distance and sprint events, each with a total of 410-569 athletes competing in 1-44 races at 15-25 venues from seasons 2002 to 2011. A linear mixed model of race times for each event provided estimates of within-athlete race-to-race variability expressed as a coefficient of variation (CV) after adjustment for fixed or random effects of snow conditions, altitude, race length, and competition terrain. Within-athlete variability was similar for men and women over various events for all athletes (CV of 1.5-1.8%) and for the annual top-10 athletes (1.1-1.4%). Observed effects of snow conditions and altitude on mean time were substantial (~2%) but mostly unclear, owing to large effects of terrain (CV of 4-10% in top-10 analyses). Predictability of performance was extremely high for all athletes (intraclass correlations of .90-.96) but only trivial to poor for top-10 athletes (men .00-.03, women .03-.35). The race-to-race variability of top-ranked skiers is similar to that of other elite endurance athletes. Estimates of the smallest worthwhile performance enhancement (0.3× within-athlete variability) will help researchers and practitioners evaluate strategies affecting performance of elite skiers.

Identifying developmental vascular disruptor compounds using a predictive signature and alternative toxicity models

EPA Science Inventory

Identifying Developmental Vascular Disruptor Compounds Using a Predictive Signature and Alternative Toxicity Models Presenting Author: Tamara Tal Affiliation: U.S. EPA/ORD/ISTD, RTP, NC, USA Chemically induced vascular toxicity during embryonic development can result in a wide...

Fetal Heart Rate and Variability: Stability and Prediction to Developmental Outcomes in Early Childhood

ERIC Educational Resources Information Center

DiPietro, Janet A.; Bornstein, Marc H.; Hahn, Chun-Shin; Costigan, Kathleen; Achy-Brou, Aristide

2007-01-01

Stability in cardiac indicators before birth and their utility in predicting variation in postnatal development were examined. Fetal heart rate and variability were measured longitudinally from 20 through 38 weeks gestation (n = 137) and again at age 2 (n = 79). Significant within-individual stability during the prenatal period and into childhood…

Artificial neural networks identify the predictive values of risk factors on the conversion of amnestic mild cognitive impairment.

PubMed

Tabaton, Massimo; Odetti, Patrizio; Cammarata, Sergio; Borghi, Roberta; Monacelli, Fiammetta; Caltagirone, Carlo; Bossù, Paola; Buscema, Massimo; Grossi, Enzo

2010-01-01

The search for markers that are able to predict the conversion of amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) is crucial for early mechanistic therapies. Using artificial neural networks (ANNs), 22 variables that are known risk factors of AD were analyzed in 80 patients with aMCI, for a period spanning at least 2 years. The cases were chosen from 195 aMCI subjects recruited by four Italian Alzheimer's disease units. The parameters of glucose metabolism disorder, female gender, and apolipoprotein E epsilon3/epsilon4 genotype were found to be the biological variables with high relevance for predicting the conversion of aMCI. The scores of attention and short term memory tests also were predictors. Surprisingly, the plasma concentration of amyloid-beta (42) had a low predictive value. The results support the utility of ANN analysis as a new tool in the interpretation of data from heterogeneous and distinct sources.

Predicting change over time in career planning and career exploration for high school students.

PubMed

Creed, Peter A; Patton, Wendy; Prideaux, Lee-Ann

2007-06-01

This study assessed 166 high school students in Grade 8 and again in Grade 10. Four models were tested: (a) whether the T1 predictor variables (career knowledge, indecision, decision-making self efficacy, self-esteem, demographics) predicted the outcome variable (career planning/exploration) at T1; (b) whether the T1 predictor variables predicted the outcome variable at T2; (c) whether the T1 predictor variables predicted change in the outcome variable from T1-T2; and (d) whether changes in the predictor variables from T1-T2 predicted change in the outcome variable from T1-T2. Strong associations (R(2)=34%) were identified for the T1 analysis (confidence, ability and paid work experience were positively associated with career planning/exploration). T1 variables were less useful predictors of career planning/exploration at T2 (R(2)=9%; having more confidence at T1 was associated with more career planning/exploration at T2) and change in career planning/exploration from T1-T2 (R(2)=11%; less confidence and no work experience were associated with change in career planning/exploration from T1-T2). When testing effect of changes in predictor variables predicting changes in outcome variable (R(2)=22%), three important predictors, indecision, work experience and confidence, were identified. Overall, results indicated important roles for self-efficacy and early work experiences in current and future career planning/exploration of high school students.

Working memory and intraindividual variability as neurocognitive indicators in ADHD: examining competing model predictions.

PubMed

Kofler, Michael J; Alderson, R Matt; Raiker, Joseph S; Bolden, Jennifer; Sarver, Dustin E; Rapport, Mark D

2014-05-01

The current study examined competing predictions of the default mode, cognitive neuroenergetic, and functional working memory models of attention-deficit/hyperactivity disorder (ADHD) regarding the relation between neurocognitive impairments in working memory and intraindividual variability. Twenty-two children with ADHD and 15 typically developing children were assessed on multiple tasks measuring intraindividual reaction time (RT) variability (ex-Gaussian: tau, sigma) and central executive (CE) working memory. Latent factor scores based on multiple, counterbalanced tasks were created for each construct of interest (CE, tau, sigma) to reflect reliable variance associated with each construct and remove task-specific, test-retest, and random error. Bias-corrected, bootstrapped mediation analyses revealed that CE working memory accounted for 88% to 100% of ADHD-related RT variability across models, and between-group differences in RT variability were no longer detectable after accounting for the mediating role of CE working memory. In contrast, RT variability accounted for 10% to 29% of between-group differences in CE working memory, and large magnitude CE working memory deficits remained after accounting for this partial mediation. Statistical comparison of effect size estimates across models suggests directionality of effects, such that the mediation effects of CE working memory on RT variability were significantly greater than the mediation effects of RT variability on CE working memory. The current findings question the role of RT variability as a primary neurocognitive indicator in ADHD and suggest that ADHD-related RT variability may be secondary to underlying deficits in CE working memory.

Resampling procedures to identify important SNPs using a consensus approach.

PubMed

Pardy, Christopher; Motyer, Allan; Wilson, Susan

2011-11-29

Our goal is to identify common single-nucleotide polymorphisms (SNPs) (minor allele frequency > 1%) that add predictive accuracy above that gained by knowledge of easily measured clinical variables. We take an algorithmic approach to predict each phenotypic variable using a combination of phenotypic and genotypic predictors. We perform our procedure on the first simulated replicate and then validate against the others. Our procedure performs well when predicting Q1 but is less successful for the other outcomes. We use resampling procedures where possible to guard against false positives and to improve generalizability. The approach is based on finding a consensus regarding important SNPs by applying random forests and the least absolute shrinkage and selection operator (LASSO) on multiple subsamples. Random forests are used first to discard unimportant predictors, narrowing our focus to roughly 100 important SNPs. A cross-validation LASSO is then used to further select variables. We combine these procedures to guarantee that cross-validation can be used to choose a shrinkage parameter for the LASSO. If the clinical variables were unavailable, this prefiltering step would be essential. We perform the SNP-based analyses simultaneously rather than one at a time to estimate SNP effects in the presence of other causal variants. We analyzed the first simulated replicate of Genetic Analysis Workshop 17 without knowledge of the true model. Post-conference knowledge of the simulation parameters allowed us to investigate the limitations of our approach. We found that many of the false positives we identified were substantially correlated with genuine causal SNPs.

A predictive score to identify hospitalized patients' risk of discharge to a post-acute care facility

PubMed Central

Louis Simonet, Martine; Kossovsky, Michel P; Chopard, Pierre; Sigaud, Philippe; Perneger, Thomas V; Gaspoz, Jean-Michel

2008-01-01

Background Early identification of patients who need post-acute care (PAC) may improve discharge planning. The purposes of the study were to develop and validate a score predicting discharge to a post-acute care (PAC) facility and to determine its best assessment time. Methods We conducted a prospective study including 349 (derivation cohort) and 161 (validation cohort) consecutive patients in a general internal medicine service of a teaching hospital. We developed logistic regression models predicting discharge to a PAC facility, based on patient variables measured on admission (day 1) and on day 3. The value of each model was assessed by its area under the receiver operating characteristics curve (AUC). A simple numerical score was derived from the best model, and was validated in a separate cohort. Results Prediction of discharge to a PAC facility was as accurate on day 1 (AUC: 0.81) as on day 3 (AUC: 0.82). The day-3 model was more parsimonious, with 5 variables: patient's partner inability to provide home help (4 pts); inability to self-manage drug regimen (4 pts); number of active medical problems on admission (1 pt per problem); dependency in bathing (4 pts) and in transfers from bed to chair (4 pts) on day 3. A score ≥ 8 points predicted discharge to a PAC facility with a sensitivity of 87% and a specificity of 63%, and was significantly associated with inappropriate hospital days due to discharge delays. Internal and external validations confirmed these results. Conclusion A simple score computed on the 3rd hospital day predicted discharge to a PAC facility with good accuracy. A score > 8 points should prompt early discharge planning. PMID:18647410

A prediction model of compressor with variable-geometry diffuser based on elliptic equation and partial least squares

PubMed Central

Yang, Chuanlei; Wang, Yinyan; Wang, Hechun

2018-01-01

To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future. PMID:29410849

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus.

PubMed

Lu, Yao; Deng, Jingyuan; Rhodes, Judith C; Lu, Hui; Lu, Long Jason

2014-06-01

Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, "the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs". Expanding the target space by rapidly identifying new essential genes has thus been described as "the most important task of genomics-based target validation". In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus Neurospora crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach. The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to

Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation.

PubMed

Evans, R Scott; Benuzillo, Jose; Horne, Benjamin D; Lloyd, James F; Bradshaw, Alejandra; Budge, Deborah; Rasmusson, Kismet D; Roberts, Colleen; Buckway, Jason; Geer, Norma; Garrett, Teresa; Lappé, Donald L

2016-09-01

Develop and evaluate an automated identification and predictive risk report for hospitalized heart failure (HF) patients. Dictated free-text reports from the previous 24 h were analyzed each day with natural language processing (NLP), to help improve the early identification of hospitalized patients with HF. A second application that uses an Intermountain Healthcare-developed predictive score to determine each HF patient's risk for 30-day hospital readmission and 30-day mortality was also developed. That information was included in an identification and predictive risk report, which was evaluated at a 354-bed hospital that treats high-risk HF patients. The addition of NLP-identified HF patients increased the identification score's sensitivity from 82.6% to 95.3% and its specificity from 82.7% to 97.5%, and the model's positive predictive value is 97.45%. Daily multidisciplinary discharge planning meetings are now based on the information provided by the HF identification and predictive report, and clinician's review of potential HF admissions takes less time compared to the previously used manual methodology (10 vs 40 min). An evaluation of the use of the HF predictive report identified a significant reduction in 30-day mortality and a significant increase in patient discharges to home care instead of to a specialized nursing facility. Using clinical decision support to help identify HF patients and automatically calculating their 30-day all-cause readmission and 30-day mortality risks, coupled with a multidisciplinary care process pathway, was found to be an effective process to improve HF patient identification, significantly reduce 30-day mortality, and significantly increase patient discharges to home care. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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

Prediction of employer–employee relationships from sociodemographic variables and social values in Brunei public and private sector workers

PubMed Central

Mundia, Lawrence; Mahalle, Salwa; Matzin, Rohani; Nasir Zakaria, Gamal Abdul; Abdullah, Nor Zaiham Midawati; Abdul Latif, Siti Norhedayah

2017-01-01

The purpose of the study was to identify the sociodemographic variables and social value correlates and predictors of employer–employee relationship problems in a random sample of 860 Brunei public and private sector workers of both genders. A quantitative field survey design was used and data were analyzed by correlation and logistic regression. The rationale and justification for using this approach is explained. The main sociodemographic correlates and predictors of employer–employee relationship problems in this study were educational level and the district in which the employee resided and worked. Other correlates, but not necessarily predictors, of employer–employee relationship problems were seeking help from the Bomo (traditional healer); obtaining help from online social networking; and workers with children in the family. The two best and most significant social value correlates and predictors of employer–employee relationship problems included interpersonal communications; and self-regulation and self-direction. Low scorers on the following variables were also associated with high likelihood for possessing employer–employee relationship problems: satisfaction with work achievements; and peace and security, while low scorers on work stress had lower odds of having employer–employee relationship problems. Other significant social value correlates, but not predictors of employer–employee relationship problems were self-presentation; interpersonal trust; peace and security; and general anxiety. Consistent with findings of relevant previous studies conducted elsewhere, there were the variables that correlated with and predicted employer–employee relationship problems in Brunei public and private sector workers. Having identified these, the next step, efforts and priority should be directed at addressing the presenting issues via counseling and psychotherapy with affected employees. Further research is recommended to understand better the

Predictive Accuracy of Sweep Frequency Impedance Technology in Identifying Conductive Conditions in Newborns.

PubMed

Aithal, Venkatesh; Kei, Joseph; Driscoll, Carlie; Murakoshi, Michio; Wada, Hiroshi

2018-02-01

Diagnosing conductive conditions in newborns is challenging for both audiologists and otolaryngologists. Although high-frequency tympanometry (HFT), acoustic stapedial reflex tests, and wideband absorbance measures are useful diagnostic tools, there is performance measure variability in their detection of middle ear conditions. Additional diagnostic sensitivity and specificity measures gained through new technology such as sweep frequency impedance (SFI) measures may assist in the diagnosis of middle ear dysfunction in newborns. The purpose of this study was to determine the test performance of SFI to predict the status of the outer and middle ear in newborns against commonly used reference standards. Automated auditory brainstem response (AABR), HFT (1000 Hz), transient evoked otoacoustic emission (TEOAE), distortion product otoacoustic emission (DPOAE), and SFI tests were administered to the study sample. A total of 188 neonates (98 males and 90 females) with a mean gestational age of 39.4 weeks were included in the sample. Mean age at the time of testing was 44.4 hr. Diagnostic accuracy of SFI was assessed in terms of its ability to identify conductive conditions in neonates when compared with nine different reference standards (including four single tests [AABR, HFT, TEOAE, and DPOAE] and five test batteries [HFT + DPOAE, HFT + TEOAE, DPOAE + TEOAE, DPOAE + AABR, and TEOAE + AABR]), using receiver operating characteristic (ROC) analysis and traditional test performance measures such as sensitivity and specificity. The test performance of SFI against the test battery reference standard of HFT + DPOAE and single reference standard of HFT was high with an area under the ROC curve (AROC) of 0.87 and 0.82, respectively. Although the HFT + DPOAE test battery reference standard performed better than the HFT reference standard in predicting middle ear conductive conditions in neonates, the difference in AROC was not significant. Further analysis revealed that the

Age at disease onset and peak ammonium level rather than interventional variables predict the neurological outcome in urea cycle disorders.

PubMed

Posset, Roland; Garcia-Cazorla, Angeles; Valayannopoulos, Vassili; Teles, Elisa Leão; Dionisi-Vici, Carlo; Brassier, Anaïs; Burlina, Alberto B; Burgard, Peter; Cortès-Saladelafont, Elisenda; Dobbelaere, Dries; Couce, Maria L; Sykut-Cegielska, Jolanta; Häberle, Johannes; Lund, Allan M; Chakrapani, Anupam; Schiff, Manuel; Walter, John H; Zeman, Jiri; Vara, Roshni; Kölker, Stefan

2016-09-01

Patients with urea cycle disorders (UCDs) have an increased risk of neurological disease manifestation. Determining the effect of diagnostic and therapeutic interventions on the neurological outcome. Evaluation of baseline, regular follow-up and emergency visits of 456 UCD patients prospectively followed between 2011 and 2015 by the E-IMD patient registry. About two-thirds of UCD patients remained asymptomatic until age 12 days [i.e. the median age at diagnosis of patients identified by newborn screening (NBS)] suggesting a potential benefit of NBS. In fact, NBS lowered the age at diagnosis in patients with late onset of symptoms (>28 days), and a trend towards improved long-term neurological outcome was found for patients with argininosuccinate synthetase and lyase deficiency as well as argininemia identified by NBS. Three to 17 different drug combinations were used for maintenance therapy, but superiority of any single drug or specific drug combination above other combinations was not demonstrated. Importantly, non-interventional variables of disease severity, such as age at disease onset and peak ammonium level of the initial hyperammonemic crisis (cut-off level: 500 μmol/L) best predicted the neurological outcome. Promising results of NBS for late onset UCD patients are reported and should be re-evaluated in a larger and more advanced age group. However, non-interventional variables affect the neurological outcome of UCD patients. Available evidence-based guideline recommendations are currently heterogeneously implemented into practice, leading to a high variability of drug combinations that hamper our understanding of optimised long-term and emergency treatment.

MicroRNA Expression in Formalin-fixed Paraffin-embedded Cancer Tissue: Identifying Reference MicroRNAs and Variability.

PubMed

Boisen, Mogens Karsbøl; Dehlendorff, Christian; Linnemann, Dorte; Schultz, Nicolai Aagaard; Jensen, Benny Vittrup; Høgdall, Estrid Vilma Solyom; Johansen, Julia Sidenius

2015-12-29

Archival formalin-fixed paraffin-embedded (FFPE) cancer tissue samples are a readily available resource for microRNA (miRNA) biomarker identification. No established standard for reference miRNAs in FFPE tissue exists. We sought to identify stable reference miRNAs for normalization of miRNA expression in FFPE tissue samples from patients with colorectal (CRC) and pancreatic (PC) cancer and to quantify the variability associated with sample age and fixation. High-throughput miRNA profiling results from 203 CRC and 256 PC FFPE samples as well as from 37 paired frozen/FFPE samples from nine other CRC tumors (methodological samples) were used. Candidate reference miRNAs were identified by their correlation with global mean expression. The stability of reference genes was analyzed according to published methods. The association between sample age and global mean miRNA expression was tested using linear regression. Variability was described using correlation coefficients and linear mixed effects models. Normalization effects were determined by changes in standard deviation and by hierarchical clustering. We created lists of 20 miRNAs with the best correlation to global mean expression in each cancer type. Nine of these miRNAs were present in both lists, and miR-103a-3p was the most stable reference miRNA for both CRC and PC FFPE tissue. The optimal number of reference miRNAs was 4 in CRC and 10 in PC. Sample age had a significant effect on global miRNA expression in PC (50% reduction over 20 years) but not in CRC. Formalin fixation for 2-6 days decreased miRNA expression 30-65%. Normalization using global mean expression reduced variability for technical and biological replicates while normalization using the expression of the identified reference miRNAs reduced variability only for biological replicates. Normalization only had a minor impact on clustering results. We identified suitable reference miRNAs for future miRNA expression experiments using CRC- and PC FFPE

Anthropometric Variables Accurately Predict Dual Energy X-Ray Absorptiometric-Derived Body Composition and Can Be Used to Screen for Diabetes

PubMed Central

Yavari, Reza; McEntee, Erin; McEntee, Michael; Brines, Michael

2011-01-01

The current world-wide epidemic of obesity has stimulated interest in developing simple screening methods to identify individuals with undiagnosed diabetes mellitus type 2 (DM2) or metabolic syndrome (MS). Prior work utilizing body composition obtained by sophisticated technology has shown that the ratio of abdominal fat to total fat is a good predictor for DM2 or MS. The goals of this study were to determine how well simple anthropometric variables predict the fat mass distribution as determined by dual energy x-ray absorptometry (DXA), and whether these are useful to screen for DM2 or MS within a population. To accomplish this, the body composition of 341 females spanning a wide range of body mass indices and with a 23% prevalence of DM2 and MS was determined using DXA. Stepwise linear regression models incorporating age, weight, height, waistline, and hipline predicted DXA body composition (i.e., fat mass, trunk fat, fat free mass, and total mass) with good accuracy. Using body composition as independent variables, nominal logistic regression was then performed to estimate the probability of DM2. The results show good discrimination with the receiver operating characteristic (ROC) having an area under the curve (AUC) of 0.78. The anthropometrically-derived body composition equations derived from the full DXA study group were then applied to a group of 1153 female patients selected from a general endocrinology practice. Similar to the smaller study group, the ROC from logistical regression using body composition had an AUC of 0.81 for the detection of DM2. These results are superior to screening based on questionnaires and compare favorably with published data derived from invasive testing, e.g., hemoglobin A1c. This anthropometric approach offers promise for the development of simple, inexpensive, non-invasive screening to identify individuals with metabolic dysfunction within large populations. PMID:21915276

PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables.

PubMed

Pounds, Stan; Cheng, Cheng; Cao, Xueyuan; Crews, Kristine R; Plunkett, William; Gandhi, Varsha; Rubnitz, Jeffrey; Ribeiro, Raul C; Downing, James R; Lamba, Jatinder

2009-08-15

In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables. Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis. Documented R routines are freely available from www.stjuderesearch.org/depts/biostats and will soon be available as a Bioconductor package from www.bioconductor.org.

Framework for making better predictions by directly estimating variables’ predictivity

PubMed Central

Chernoff, Herman; Lo, Shaw-Hwa

2016-01-01

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 I-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 I-score provides an effective approach to selecting highly predictive variables. We offer simulations and an application of the I-score on real data to demonstrate the statistic’s predictive performance on sample data. We conjecture that using the partition retention and I-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. PMID:27911830

Baseline Chromatin Modification Levels May Predict Interindividual Variability in Ozone-Induced Gene Expression

EPA Science Inventory

Traditional toxicological paradigms have relied on factors such as age, genotype, and disease status to explain variability in responsiveness to toxicant exposure; however, these are neither sufficient to faithfully identify differentially responsive individuals nor are they modi...

A Predictive Statistical Model of Navy Career Enlisted Retention Behavior Utilizing Economic Variables.

DTIC Science & Technology

1980-12-01

career retention rates , and to predict future career retention rates in the Navy. The statistical model utilizes economic variables as predictors...The model developed r has a high correlation with Navy career retention rates . The problem of Navy career retention has not been adequately studied, 0D...findings indicate Navy policymakers must be cognizant of the relationships of economic factors to Navy career retention rates . Accrzsiofl ’or NTIS GRA&I

Predicting General Academic Performance and Identifying the Differential Contribution of Participating Variables Using Artificial Neural Networks

ERIC Educational Resources Information Center

Musso, Mariel F.; Kyndt, Eva; Cascallar, Eduardo C.; Dochy, Filip

2013-01-01

Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not only for the study of learning processes, but also as tools for improving curriculum designs, tutorial systems, and students'…

A Bayesian prediction model between a biomarker and the clinical endpoint for dichotomous variables.

PubMed

Jiang, Zhiwei; Song, Yang; Shou, Qiong; Xia, Jielai; Wang, William

2014-12-20

Early biomarkers are helpful for predicting clinical endpoints and for evaluating efficacy in clinical trials even if the biomarker cannot replace clinical outcome as a surrogate. The building and evaluation of an association model between biomarkers and clinical outcomes are two equally important concerns regarding the prediction of clinical outcome. This paper is to address both issues in a Bayesian framework. A Bayesian meta-analytic approach is proposed to build a prediction model between the biomarker and clinical endpoint for dichotomous variables. Compared with other Bayesian methods, the proposed model only requires trial-level summary data of historical trials in model building. By using extensive simulations, we evaluate the link function and the application condition of the proposed Bayesian model under scenario (i) equal positive predictive value (PPV) and negative predictive value (NPV) and (ii) higher NPV and lower PPV. In the simulations, the patient-level data is generated to evaluate the meta-analytic model. PPV and NPV are employed to describe the patient-level relationship between the biomarker and the clinical outcome. The minimum number of historical trials to be included in building the model is also considered. It is seen from the simulations that the logit link function performs better than the odds and cloglog functions under both scenarios. PPV/NPV ≥0.5 for equal PPV and NPV, and PPV + NPV ≥1 for higher NPV and lower PPV are proposed in order to predict clinical outcome accurately and precisely when the proposed model is considered. Twenty historical trials are required to be included in model building when PPV and NPV are equal. For unequal PPV and NPV, the minimum number of historical trials for model building is proposed to be five. A hypothetical example shows an application of the proposed model in global drug development. The proposed Bayesian model is able to predict well the clinical endpoint from the observed biomarker

Prediction models for successful external cephalic version: a systematic review.

PubMed

Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

2015-12-01

To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

A Western diet ecological module identified from the 'humanized' mouse microbiota predicts diet in adults and formula feeding in children.

PubMed

Siddharth, Jay; Holway, Nicholas; Parkinson, Scott J

2013-01-01

The interplay between diet and the microbiota has been implicated in the growing frequency of chronic diseases associated with the Western lifestyle. However, the complexity and variability of microbial ecology in humans and preclinical models has hampered identification of the molecular mechanisms underlying the association of the microbiota in this context. We sought to address two key questions. Can the microbial ecology of preclinical models predict human populations? And can we identify underlying principles that surpass the plasticity of microbial ecology in humans? To do this, we focused our study on diet; perhaps the most influential factor determining the composition of the gut microbiota. Beginning with a study in 'humanized' mice we identified an interactive module of 9 genera allied with Western diet intake. This module was applied to a controlled dietary study in humans. The abundance of the Western ecological module correctly predicted the dietary intake of 19/21 top and 21/21 of the bottom quartile samples inclusive of all 5 Western and 'low-fat' diet subjects, respectively. In 98 volunteers the abundance of the Western module correlated appropriately with dietary intake of saturated fatty acids, fat-soluble vitamins and fiber. Furthermore, it correlated with the geographical location and dietary habits of healthy adults from the Western, developing and third world. The module was also coupled to dietary intake in children (and piglets) correlating with formula (vs breast) feeding and associated with a precipitous development of the ecological module in young children. Our study provides a conceptual platform to translate microbial ecology from preclinical models to humans and identifies an ecological network module underlying the association of the gut microbiota with Western dietary habits.

On the importance of identifying, characterizing, and predicting fundamental phenomena towards microbial electrochemistry applications.

PubMed

Torres, César Iván

2014-06-01

The development of microbial electrochemistry research toward technological applications has increased significantly in the past years, leading to many process configurations. This short review focuses on the need to identify and characterize the fundamental phenomena that control the performance of microbial electrochemical cells (MXCs). Specifically, it discusses the importance of recent efforts to discover and characterize novel microorganisms for MXC applications, as well as recent developments to understand transport limitations in MXCs. As we increase our understanding of how MXCs operate, it is imperative to continue modeling efforts in order to effectively predict their performance, design efficient MXC technologies, and implement them commercially. Thus, the success of MXC technologies largely depends on the path of identifying, understanding, and predicting fundamental phenomena that determine MXC performance. Copyright © 2013 Elsevier Ltd. All rights reserved.

Using a predictive model to evaluate spatiotemporal variability in streamflow permanence across the Pacific Northwest region

NASA Astrophysics Data System (ADS)

Jaeger, K. L.

2017-12-01

The U.S. Geological Survey (USGS) has developed the PRObability Of Streamflow PERmanence (PROSPER) model, a GIS-based empirical model that provides predictions of the annual probability of a stream channel having year-round flow (Streamflow permanence probability; SPP) for any unregulated and minimally-impaired stream channel in the Pacific Northwest (Washington, Oregon, Idaho, western Montana). The model provides annual predictions for 2004-2016 at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions, and static physiographic variables associated with the upstream basin. Prediction locations correspond to the channel network consistent with the National Hydrography Dataset stream grid and are publicly available through the USGS StreamStats platform (https://water.usgs.gov/osw/streamstats/). In snowmelt-driven systems, the most informative predictor variable was mean upstream snow water equivalent on May 1, which highlights the influence of late spring snow cover for supporting streamflow in mountain river networks. In non-snowmelt-driven systems, the most informative variable was mean annual precipitation. Streamflow permanence probabilities varied across the study area by geography and from year-to-year. Notably lower SPP corresponded to the climatically drier subregions of the study area. Higher SPP were concentrated in coastal and higher elevation mountain regions. In addition, SPP appeared to trend with average hydroclimatic conditions, which were also geographically coherent. The year-to-year variability lends support for the growing recognition of the spatiotemporal dynamism of streamflow permanence. An analysis of three focus basins located in contrasting geographical and hydroclimatic settings demonstrates differences in the sensitivity of streamflow permanence to antecedent climate conditions as a function of geography. Consequently, results suggest that PROSPER model can be a useful tool to evaluate regions of the

Variables that Predict Serve Efficacy in Elite Men’s Volleyball with Different Quality of Opposition Sets

PubMed Central

Valhondo, Álvaro; Fernández-Echeverría, Carmen; González-Silva, Jara; Claver, Fernando; Moreno, M. Perla

2018-01-01

Abstract The objective of this study was to determine the variables that predicted serve efficacy in elite men’s volleyball, in sets with different quality of opposition. 3292 serve actions were analysed, of which 2254 were carried out in high quality of opposition sets and 1038 actions were in low quality of opposition sets, corresponding to a total of 24 matches played during the Men’s European Volleyball Championships held in 2011. The independent variables considered in this study were the serve zone, serve type, serving player, serve direction, reception zone, receiving player and reception type; the dependent variable was serve efficacy and the situational variable was quality of opposition sets. The variables that acted as predictors in both high and low quality of opposition sets were the serving player, reception zone and reception type. The serve type variable only acted as a predictor in high quality of opposition sets, while the serve zone variable only acted as a predictor in low quality of opposition sets. These results may provide important guidance in men’s volleyball training processes. PMID:29599869

Inter-annual variability and long term predictability of exchanges through the Strait of Gibraltar

NASA Astrophysics Data System (ADS)

Boutov, Dmitri; Peliz, Álvaro; Miranda, Pedro M. A.; Soares, Pedro M. M.; Cardoso, Rita M.; Prieto, Laura; Ruiz, Javier; García-Lafuente, Jesus

2014-03-01

Inter-annual variability of calculated barotropic (netflow) and simulated baroclinic (inflow and outflow) exchanges through the Strait of Gibraltar is analyzed and their response to the main modes of atmospheric variability is investigated. Time series of the outflow obtained by high resolution simulations and estimated from in-situ Acoustic Doppler Current Profiler (ADCP) current measurements are compared. The time coefficients (TC) of the leading empirical orthogonal function (EOF) modes that describe zonal atmospheric circulation in the vicinity of the Strait (1st and 3rd of Sea-Level Pressure (SLP) and 1st of the wind) show significant covariance with the inflow and outflow. Based on these analyses, a regression model between these SLP TCs and outflow of the Mediterranean Water was developed. This regression outflow time series was compared with estimates based on current meter observations and the predictability and reconstruction of past exchange variability based on atmospheric pressure fields are discussed. The simple regression model seems to reproduce the outflow evolution fairly reasonably, with the exception of the year 2008, which is apparently anomalous without available physical explanation yet. The exchange time series show a reduced inter-annual variability (less than 1%, 2.6% and 3.1% of total 2-day variability, for netflow, inflow and outflow, respectively). From a statistical point of view no clear long-term tendencies were revealed. Anomalously high baroclinic fluxes are reported for the years of 2000-2001 that are coincident with strong impact on the Alboran Sea ecosystem. The origin of the anomalous flow is associated with a strong negative anomaly (~ - 9 hPa) in atmospheric pressure fields settled north of Iberian Peninsula and extending over the central Atlantic, favoring an increased zonal circulation in winter 2000/2001. These low pressure fields forced intense and durable westerly winds in the Gulf of Cadiz-Alboran system. The signal of

A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012

PubMed Central

Rubikowska, Barbara; Bratkowski, Jakub; Ustrnul, Zbigniew; Vanwambeke, Sophie O.

2018-01-01

During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping. PMID:29617333

Predictive value of clinical and laboratory variables for vesicoureteral reflux in children.

PubMed

Soylu, Alper; Kasap, Belde; Demir, Korcan; Türkmen, Mehmet; Kavukçu, Salih

2007-06-01

We aimed to determine the predictability of clinical and laboratory variables for vesicoureteral reflux (VUR) in children with urinary tract infection (UTI). Data of children with febrile UTI who underwent voiding cystoureterography between 2002 and 2005 were evaluated retrospectively for clinical (age, gender, fever > or = 38.5 degrees C, recurrent UTI), laboratory [leukocytosis, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), pyuria, serum creatinine (S(Cr))] and imaging (renal ultrasonography) variables. Children with VUR (group 1) vs. no VUR (group 2) and children with high-grade (III-V) VUR (group 3) vs. no or low-grade (I-II) VUR (group 4) were compared. Among 88 patients (24 male), 38 had VUR and 21 high-grade VUR. Fever > or = 38.5 degrees C was associated with VUR [odds ratio (OR): 7.5]. CRP level of 50 mg/l was the best cut-off level for predicting high-grade VUR (OR 15.5; discriminative ability 0.89 +/- 0.05). Performing voiding cystourethrography based on this CRP level would result in failure to notice 9% of patients with high-grade VUR, whereas 69% of children with no/low-grade VUR would be spared from this invasive test. In conclusion, fever > or = 38 degrees C and CRP > 50 mg/l seem to be potentially useful clinical predictors of VUR and high-grade VUR, respectively, in pediatric patients with UTI. Further validation of these findings could limit unnecessary voiding cystourethrography.

Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease

PubMed Central

Fagan, A.M.; Williams, M.M.; Ghoshal, N.; Aeschleman, M.; Grant, E.A.; Marcus, D.S.; Mintun, M.A.; Holtzman, D.M.; Morris, J.C.

2011-01-01

Objective: To investigate factors, including cognitive and brain reserve, which may independently predict prevalent and incident dementia of the Alzheimer type (DAT) and to determine whether inclusion of identified factors increases the predictive accuracy of the CSF biomarkers Aβ42, tau, ptau181, tau/Aβ42, and ptau181/Aβ42. Methods: Logistic regression identified variables that predicted prevalent DAT when considered together with each CSF biomarker in a cross-sectional sample of 201 participants with normal cognition and 46 with DAT. The area under the receiver operating characteristic curve (AUC) from the resulting model was compared with the AUC generated using the biomarker alone. In a second sample with normal cognition at baseline and longitudinal data available (n = 213), Cox proportional hazards models identified variables that predicted incident DAT together with each biomarker, and the models' concordance probability estimate (CPE), which was compared to the CPE generated using the biomarker alone. Results: APOE genotype including an ε4 allele, male gender, and smaller normalized whole brain volumes (nWBV) were cross-sectionally associated with DAT when considered together with every biomarker. In the longitudinal sample (mean follow-up = 3.2 years), 14 participants (6.6%) developed DAT. Older age predicted a faster time to DAT in every model, and greater education predicted a slower time in 4 of 5 models. Inclusion of ancillary variables resulted in better cross-sectional prediction of DAT for all biomarkers (p < 0.0021), and better longitudinal prediction for 4 of 5 biomarkers (p < 0.0022). Conclusions: The predictive accuracy of CSF biomarkers is improved by including age, education, and nWBV in analyses. PMID:21228296

Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models.

PubMed

Rahman, Anisur; Faqeerzada, Mohammad A; Cho, Byoung-Kwan

2018-03-14

Allicin and soluble solid content (SSC) in garlic is the responsible for its pungent flavor and odor. However, current conventional methods such as the use of high-pressure liquid chromatography and a refractometer have critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to predict allicin and SSC in garlic using hyperspectral imaging in combination with variable selection algorithms and calibration models. Hyperspectral images of 100 garlic cloves were acquired that covered two spectral ranges, from which the mean spectra of each clove were extracted. The calibration models included partial least squares (PLS) and least squares-support vector machine (LS-SVM) regression, as well as different spectral pre-processing techniques, from which the highest performing spectral preprocessing technique and spectral range were selected. Then, variable selection methods, such as regression coefficients, variable importance in projection (VIP) and the successive projections algorithm (SPA), were evaluated for the selection of effective wavelengths (EWs). Furthermore, PLS and LS-SVM regression methods were applied to quantitatively predict the quality attributes of garlic using the selected EWs. Of the established models, the SPA-LS-SVM model obtained an Rpred2 of 0.90 and standard error of prediction (SEP) of 1.01% for SSC prediction, whereas the VIP-LS-SVM model produced the best result with an Rpred2 of 0.83 and SEP of 0.19 mg g -1 for allicin prediction in the range 1000-1700 nm. Furthermore, chemical images of garlic were developed using the best predictive model to facilitate visualization of the spatial distributions of allicin and SSC. The present study clearly demonstrates that hyperspectral imaging combined with an appropriate chemometrics method can potentially be employed as a fast, non-invasive method to predict the allicin and SSC in garlic. © 2018 Society of Chemical Industry. © 2018

Intraindividual Cognitive Variability in Middle Age Predicts Cognitive Impairment 8-10 Years Later: Results from the Wisconsin Registry for Alzheimer's Prevention.

PubMed

Koscik, Rebecca L; Berman, Sara E; Clark, Lindsay R; Mueller, Kimberly D; Okonkwo, Ozioma C; Gleason, Carey E; Hermann, Bruce P; Sager, Mark A; Johnson, Sterling C

2016-11-01

Intraindividual cognitive variability (IICV) has been shown to differentiate between groups with normal cognition, mild cognitive impairment (MCI), and dementia. This study examined whether baseline IICV predicted subsequent mild to moderate cognitive impairment in a cognitively normal baseline sample. Participants with 4 waves of cognitive assessment were drawn from the Wisconsin Registry for Alzheimer's Prevention (WRAP; n=684; 53.6(6.6) baseline age; 9.1(1.0) years follow-up; 70% female; 74.6% parental history of Alzheimer's disease). The primary outcome was Wave 4 cognitive status ("cognitively normal" vs. "impaired") determined by consensus conference; "impaired" included early MCI (n=109), clinical MCI (n=11), or dementia (n=1). Primary predictors included two IICV variables, each based on the standard deviation of a set of scores: "6 Factor IICV" and "4 Test IICV". Each IICV variable was tested in a series of logistic regression models to determine whether IICV predicted cognitive status. In exploratory analyses, distribution-based cutoffs incorporating memory, executive function, and IICV patterns were used to create and test an MCI risk variable. Results were similar for the IICV variables: higher IICV was associated with greater risk of subsequent impairment after covariate adjustment. After adjusting for memory and executive functioning scores contributing to IICV, IICV was not significant. The MCI risk variable also predicted risk of impairment. While IICV in middle-age predicts subsequent impairment, it is a weaker risk indicator than the memory and executive function scores contributing to its calculation. Exploratory analyses suggest potential to incorporate IICV patterns into risk assessment in clinical settings. (JINS, 2016, 22, 1016-1025).

Personality, emotion-related variables, and media pressure predict eating disorders via disordered eating in Lebanese university students.

PubMed

Sanchez-Ruiz, Maria Jose; El-Jor, Claire; Abi Kharma, Joelle; Bassil, Maya; Zeeni, Nadine

2017-04-18

Disordered eating behaviors are on the rise among youth. The present study investigates psychosocial and weight-related variables as predictors of eating disorders (ED) through disordered eating (DE) dimensions (namely restrained, external, and emotional eating) in Lebanese university students. The sample consisted of 244 undergraduates (143 female) aged from 18 to 31 years (M = 20.06; SD = 1.67). Using path analysis, two statistical models were built separately with restrained and emotional eating as dependent variables, and all possible direct and indirect pathways were tested for mediating effects. The variables tested for were media influence, perfectionism, trait emotional intelligence, and the Big Five dimensions. In the first model, media pressure, self-control, and extraversion predicted eating disorders via emotional eating. In the second model, media pressure and perfectionism predicted eating disorders via restrained eating. Findings from this study provide an understanding of the dynamics between DE, ED, and key personality, emotion-related, and social factors in youth. Lastly, implications and recommendations for future studies are advanced.

Detection limits of tidal-wetland sequences to identify variable rupture modes of megathrust earthquakes

NASA Astrophysics Data System (ADS)

Shennan, Ian; Garrett, Ed; Barlow, Natasha

2016-10-01

Recent paleoseismological studies question whether segment boundaries identified for 20th and 21st century great, >M8, earthquakes persist through multiple earthquake cycles or whether smaller segments with different boundaries rupture and cause significant hazards. The smaller segments may include some currently slipping rather than locked. In this review, we outline general principles regarding indicators of relative sea-level change in tidal wetlands and the conditions in which paleoseismic indicators must be distinct from those resulting from non-seismic processes. We present new evidence from sites across southcentral Alaska to illustrate different detection limits of paleoseismic indicators and consider alternative interpretations for marsh submergence and emergence. We compare predictions of coseismic uplift and subsidence derived from geophysical models of earthquakes with different rupture modes. The spatial patterns of agreement and misfits between model predictions and quantitative reconstructions of coseismic submergence and emergence suggest that no earthquake within the last 4000 years had a pattern of rupture the same as the Mw 9.2 Alaska earthquake in 1964. From the Alaska examples and research from other subduction zones we suggest that If we want to understand whether a megathrust ruptures in segments of variable length in different earthquakes, we need to be site-specific as to what sort of geological-based criteria eliminate the possibility of a particular rupture mode in different earthquakes. We conclude that coastal paleoseismological studies benefit from a methodological framework that employs rigorous evaluation of five essential criteria and a sixth which may be very robust but only occur at some sites: 1 - lateral extent of peat-mud or mud-peat couplets with sharp contacts; 2 - suddenness of submergence or emergence, and replicated within each site; 3 - amount of vertical motion, quantified with 95% error terms and replicated within each

Predicting the Risk of Clostridium difficile Infection upon Admission: A Score to Identify Patients for Antimicrobial Stewardship Efforts.

PubMed

Kuntz, Jennifer L; Smith, David H; Petrik, Amanda F; Yang, Xiuhai; Thorp, Micah L; Barton, Tracy; Barton, Karen; Labreche, Matthew; Spindel, Steven J; Johnson, Eric S

2016-01-01

Increasing morbidity and health care costs related to Clostridium difficile infection (CDI) have heightened interest in methods to identify patients who would most benefit from interventions to mitigate the likelihood of CDI. To develop a risk score that can be calculated upon hospital admission and used by antimicrobial stewards, including pharmacists and clinicians, to identify patients at risk for CDI who would benefit from enhanced antibiotic review and patient education. We assembled a cohort of Kaiser Permanente Northwest patients with a hospital admission from July 1, 2005, through December 30, 2012, and identified CDI in the six months following hospital admission. Using Cox regression, we constructed a score to identify patients at high risk for CDI on the basis of preadmission characteristics. We calculated and plotted the observed six-month CDI risk for each decile of predicted risk. We identified 721 CDIs following 54,186 hospital admissions-a 6-month incidence of 13.3 CDIs/1000 patient admissions. Patients with the highest predicted risk of CDI had an observed incidence of 53 CDIs/1000 patient admissions. The score differentiated between patients who do and do not develop CDI, with values for the extended C-statistic of 0.75. Predicted risk for CDI agreed closely with observed risk. Our risk score accurately predicted six-month risk for CDI using preadmission characteristics. Accurate predictions among the highest-risk patient subgroups allow for the identification of patients who could be targeted for and who would likely benefit from review of inpatient antibiotic use or enhanced educational efforts at the time of discharge planning.

Variability, trends, and predictability of seasonal sea ice retreat and advance in the Chukchi Sea

NASA Astrophysics Data System (ADS)

Serreze, Mark C.; Crawford, Alex D.; Stroeve, Julienne C.; Barrett, Andrew P.; Woodgate, Rebecca A.

2016-10-01

As assessed over the period 1979-2014, the date that sea ice retreats to the shelf break (150 m contour) of the Chukchi Sea has a linear trend of -0.7 days per year. The date of seasonal ice advance back to the shelf break has a steeper trend of about +1.5 days per year, together yielding an increase in the open water period of 80 days. Based on detrended time series, we ask how interannual variability in advance and retreat dates relate to various forcing parameters including radiation fluxes, temperature and wind (from numerical reanalyses), and the oceanic heat inflow through the Bering Strait (from in situ moorings). Of all variables considered, the retreat date is most strongly correlated (r ˜ 0.8) with the April through June Bering Strait heat inflow. After testing a suite of statistical linear models using several potential predictors, the best model for predicting the date of retreat includes only the April through June Bering Strait heat inflow, which explains 68% of retreat date variance. The best model predicting the ice advance date includes the July through September inflow and the date of retreat, explaining 67% of advance date variance. We address these relationships by discussing heat balances within the Chukchi Sea, and the hypothesis of oceanic heat transport triggering ocean heat uptake and ice-albedo feedback. Developing an operational prediction scheme for seasonal retreat and advance would require timely acquisition of Bering Strait heat inflow data. Predictability will likely always be limited by the chaotic nature of atmospheric circulation patterns.

Identifying predictive factors for long-term complications following button battery impactions: A case series and literature review.

PubMed

Eliason, Michael J; Melzer, Jonathan M; Winters, Jessica R; Gallagher, Thomas Q

2016-08-01

To complement a case series review of button battery impactions managed at our single military tertiary care center with a thorough literature review of laboratory research and clinical cases to develop a protocol to optimize patient care. Specifically, to identify predictive factors of long-term complications which can be used by the pediatric otolaryngologist to guide patient management after button battery impactions. A retrospective review of the Department of Defense's electronic medical record systems was conducted to identify patients with button battery ingestions and then characterize their treatment course. A thorough literature review complemented the lessons learned to identify potentially predictive clinical measures for long-term complications. Eight patients were identified as being treated for button battery impaction in the aerodigestive tract with two sustaining long-term complications. The median age of the patients treated was 33 months old and the median estimated time of impaction in the aerodigestive tract prior to removal was 10.5 h. Time of impaction, anatomic direction of the battery's negative pole, and identifying specific battery parameters were identified as factors that may be employed to predict sequelae. Based on case reviews, advancements in battery manufacturing, and laboratory research, there are distinct clinical factors that should be assessed at the time of initial therapy to guide follow-up management to minimize potential catastrophic sequelae of button battery ingestion. Published by Elsevier Ireland Ltd.

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

Father involvement: Identifying and predicting family members' shared and unique perceptions.

PubMed

Dyer, W Justin; Day, Randal D; Harper, James M

2014-08-01

Father involvement research has typically not recognized that reports of involvement contain at least two components: 1 reflecting a view of father involvement that is broadly recognized in the family, and another reflecting each reporter's unique perceptions. Using a longitudinal sample of 302 families, this study provides a first examination of shared and unique views of father involvement (engagement and warmth) from the perspectives of fathers, children, and mothers. This study also identifies influences on these shared and unique perspectives. Father involvement reports were obtained when the child was 12 and 14 years old. Mother reports overlapped more with the shared view than father or child reports. This suggests the mother's view may be more in line with broadly recognized father involvement. Regarding antecedents, for fathers' unique view, a compensatory model partially explains results; that is, negative aspects of family life were positively associated with fathers' unique view. Children's unique view of engagement may partially reflect a sentiment override with father antisocial behaviors being predictive. Mothers' unique view of engagement was predicted by father and mother work hours and her unique view of warmth was predicted by depression and maternal gatekeeping. Taken, together finding suggests a far more nuanced view of father involvement should be considered.

Prediction of space sickness in astronauts from preflight fluid, electrolyte, and cardiovascular variables and Weightless Environmental Training Facility (WETF) training

NASA Technical Reports Server (NTRS)

Simanonok, K.; Mosely, E.; Charles, J.

1992-01-01

Nine preflight variables related to fluid, electrolyte, and cardiovascular status from 64 first-time Shuttle crewmembers were differentially weighted by discrimination analysis to predict the incidence and severity of each crewmember's space sickness as rated by NASA flight surgeons. The nine variables are serum uric acid, red cell count, environmental temperature at the launch site, serum phosphate, urine osmolality, serum thyroxine, sitting systolic blood pressure, calculated blood volume, and serum chloride. Using two methods of cross-validation on the original samples (jackknife and a stratefied random subsample), these variables enable the prediction of space sickness incidence (NONE or SICK) with 80 percent sickness and space severity (NONE, MILD, MODERATE, of SEVERE) with 59 percent success by one method of cross-validation and 67 percent by another method. Addition of a tenth variable, hours spent in the Weightlessness Environment Training Facility (WETF) did not improve the prediction of space sickness incidences but did improve the prediction of space sickness severity to 66 percent success by the first method of cross-validation of original samples and to 71 percent by the second method. Results to date suggest the presence of predisposing physiologic factors to space sickness that implicate fluid shift etiology. The data also suggest that prior exposure to fluid shift during WETF training may produce some circulatory pre-adaption to fluid shifts in weightlessness that results in a reduction of space sickness severity.

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.

Can Predictive Modeling Identify Head and Neck Oncology Patients at Risk for Readmission?

PubMed

Manning, Amy M; Casper, Keith A; Peter, Kay St; Wilson, Keith M; Mark, Jonathan R; Collar, Ryan M

2018-05-01

Objective Unplanned readmission within 30 days is a contributor to health care costs in the United States. The use of predictive modeling during hospitalization to identify patients at risk for readmission offers a novel approach to quality improvement and cost reduction. Study Design Two-phase study including retrospective analysis of prospectively collected data followed by prospective longitudinal study. Setting Tertiary academic medical center. Subjects and Methods Prospectively collected data for patients undergoing surgical treatment for head and neck cancer from January 2013 to January 2015 were used to build predictive models for readmission within 30 days of discharge using logistic regression, classification and regression tree (CART) analysis, and random forests. One model (logistic regression) was then placed prospectively into the discharge workflow from March 2016 to May 2016 to determine the model's ability to predict which patients would be readmitted within 30 days. Results In total, 174 admissions had descriptive data. Thirty-two were excluded due to incomplete data. Logistic regression, CART, and random forest predictive models were constructed using the remaining 142 admissions. When applied to 106 consecutive prospective head and neck oncology patients at the time of discharge, the logistic regression model predicted readmissions with a specificity of 94%, a sensitivity of 47%, a negative predictive value of 90%, and a positive predictive value of 62% (odds ratio, 14.9; 95% confidence interval, 4.02-55.45). Conclusion Prospectively collected head and neck cancer databases can be used to develop predictive models that can accurately predict which patients will be readmitted. This offers valuable support for quality improvement initiatives and readmission-related cost reduction in head and neck cancer care.

Inter-Annual Variability of the Acoustic Propagation in the Mediterranean Sea Identified from a Synoptic Monthly Gridded Database as Compared with GDEM

DTIC Science & Technology

2016-12-01

VARIABILITY OF THE ACOUSTIC PROPAGATION IN THE MEDITERRANEAN SEA IDENTIFIED FROM A SYNOPTIC MONTHLY GRIDDED DATABASE AS COMPARED WITH GDEM by...ANNUAL VARIABILITY OF THE ACOUSTIC PROPAGATION IN THE MEDITERRANEAN SEA IDENTIFIED FROM A SYNOPTIC MONTHLY GRIDDED DATABASE AS COMPARED WITH GDEM 5...profiles obtained from the synoptic monthly gridded World Ocean Database (SMD-WOD) and Generalized Digital Environmental Model (GDEM) temperature (T

Posttreatment Variables Improve Outcome Prediction after Intra-Arterial Therapy for Acute Ischemic Stroke

PubMed Central

Prabhakaran, Shyam; Jovin, Tudor G.; Tayal, Ashis H.; Hussain, Muhammad S.; Nguyen, Thanh N.; Sheth, Kevin N.; Terry, John B.; Nogueira, Raul G.; Horev, Anat; Gandhi, Dheeraj; Wisco, Dolora; Glenn, Brenda A.; Ludwig, Bryan; Clemmons, Paul F.; Cronin, Carolyn A.; Tian, Melissa; Liebeskind, David; Zaidat, Osama O.; Castonguay, Alicia C.; Martin, Coleman; Mueller-Kronast, Nils; English, Joey D.; Linfante, Italo; Malisch, Timothy W.; Gupta, Rishi

2014-01-01

Background There are multiple clinical and radiographic factors that influence outcomes after endovascular reperfusion therapy (ERT) in acute ischemic stroke (AIS). We sought to derive and validate an outcome prediction score for AIS patients undergoing ERT based on readily available pretreatment and posttreatment factors. Methods The derivation cohort included 511 patients with anterior circulation AIS treated with ERT at 10 centers between September 2009 and July 2011. The prospective validation cohort included 223 patients with anterior circulation AIS treated in the North American Solitaire Acute Stroke registry. Multivariable logistic regression identified predictors of good outcome (modified Rankin score ≤2 at 3 months) in the derivation cohort; model β coefficients were used to assign points and calculate a risk score. Discrimination was tested using C statistics with 95% confidence intervals (CIs) in the derivation and validation cohorts. Calibration was assessed using the Hosmer-Lemeshow test and plots of observed to expected outcomes. We assessed the net reclassification improvement for the derived score compared to the Totaled Health Risks in Vascular Events (THRIVE) score. Subgroup analysis in patients with pretreatment Alberta Stroke Program Early CT Score (ASPECTS) and posttreatment final infarct volume measurements was also performed to identify whether these radiographic predictors improved the model compared to simpler models. Results Good outcome was noted in 186 (36.4%) and 100 patients (44.8%) in the derivation and validation cohorts, respectively. Combining readily available pretreatment and posttreatment variables, we created a score (acronym: SNARL) based on the following parameters: symptomatic hemorrhage [2 points: none, hemorrhagic infarction (HI)1–2 or parenchymal hematoma (PH) type 1; 0 points: PH2], baseline National Institutes of Health Stroke Scale score (3 points: 0–10; 1 point: 11–20; 0 points: >20), age (2 points: <60

A framework for monitoring-based commissioning: Identifying variables that act as barriers and enablers to the process

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

Harris, Nora; Shealy, Tripp; Kramer, Hannah

The practice of monitoring-based commissioning (MBCx) using energy management and information systems (EMIS) has been shown to enable and help sustain up to 20% energy savings in buildings. Despite research that has quantified the costs, benefits, and energy savings of MBCx, the process remains under-utilized. To understand why MBCx is not more frequently adopted and how to encourage its use, this research synthesizes qualitative data from over 40 organizations, currently engaging in MBCx. The outcome of this research is a framework containing variables that emerged from the qualitative data, marked as barriers or enablers, organized by phases of the MBCxmore » process. The framework is comprised of 51 emergent variables that fall within 13 different categories. The variables that most frequently act as barriers are data configuration, measurement & verification (M&V), developing specifications for EMIS, and data architecture. Although some variables that act as barriers for one organization were identified as enablers for another. For example, payback/ROI was considered a barrier 7 times and an enabler 3 times. One organization had difficulty making the business case for the initial investment for MBCx due to lack of cost information, while another was able to justify large investments with documented savings of previously implemented measures identified through MBCx. The framework formally validates barriers found in previous research, and can be used by practitioners to better understand common experiences with MBCx. This research also highlights the need for a similar collective data set to validate common enablers to MBCx and also the need for empirical research to determine relationships between variables.« less

A framework for monitoring-based commissioning: Identifying variables that act as barriers and enablers to the process

DOE PAGES

Harris, Nora; Shealy, Tripp; Kramer, Hannah; ...

2018-03-16

The practice of monitoring-based commissioning (MBCx) using energy management and information systems (EMIS) has been shown to enable and help sustain up to 20% energy savings in buildings. Despite research that has quantified the costs, benefits, and energy savings of MBCx, the process remains under-utilized. To understand why MBCx is not more frequently adopted and how to encourage its use, this research synthesizes qualitative data from over 40 organizations, currently engaging in MBCx. The outcome of this research is a framework containing variables that emerged from the qualitative data, marked as barriers or enablers, organized by phases of the MBCxmore » process. The framework is comprised of 51 emergent variables that fall within 13 different categories. The variables that most frequently act as barriers are data configuration, measurement & verification (M&V), developing specifications for EMIS, and data architecture. Although some variables that act as barriers for one organization were identified as enablers for another. For example, payback/ROI was considered a barrier 7 times and an enabler 3 times. One organization had difficulty making the business case for the initial investment for MBCx due to lack of cost information, while another was able to justify large investments with documented savings of previously implemented measures identified through MBCx. The framework formally validates barriers found in previous research, and can be used by practitioners to better understand common experiences with MBCx. This research also highlights the need for a similar collective data set to validate common enablers to MBCx and also the need for empirical research to determine relationships between variables.« less

PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables

PubMed Central

Pounds, Stan; Cheng, Cheng; Cao, Xueyuan; Crews, Kristine R.; Plunkett, William; Gandhi, Varsha; Rubnitz, Jeffrey; Ribeiro, Raul C.; Downing, James R.; Lamba, Jatinder

2009-01-01

Motivation: In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables. Results: Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis. Availability: Documented R routines are freely available from www.stjuderesearch.org/depts/biostats and will soon be available as a Bioconductor package from www.bioconductor.org. Contact: stanley.pounds@stjude.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19528086

Using naturalistic driving data to identify variables associated with infrequent, occasional, and consistent seat belt use.

PubMed

Reagan, Ian J; McClafferty, Julie A; Berlin, Sharon P; Hankey, Jonathan M

2013-01-01

Seat belt use is one of the most effective countermeasures to reduce traffic fatalities and injuries. The success of efforts to increase use is measured by road side observations and self-report questionnaires. These methods have shortcomings, with the former requiring a binary point estimate and the latter being subjective. The 100-car naturalistic driving study presented a unique opportunity to study seat belt use in that seat belt status was known for every trip each driver made during a 12-month period. Drivers were grouped into infrequent, occasional, or consistent seat belt users based on the frequency of belt use. Analyses were then completed to assess if these groups differed on several measures including personality, demographics, self-reported driving style variables as well as measures from the 100-car study instrumentation suite (average trip speed, trips per day). In addition, detailed analyses of the occasional belt user group were completed to identify factors that were predictive of occasional belt users wearing their belts. The analyses indicated that consistent seat belt users took fewer trips per day, and that increased average trip speed was associated with increased belt use among occasional belt users. The results of this project may help focus messaging efforts to convert occasional and inconsistent seat belt users to consistent users. Copyright © 2012 Elsevier Ltd. All rights reserved.

Appraisal of Weather Research and Forecasting Model Downscaling of Hydro-meteorological Variables and their Applicability for Discharge Prediction: Prognostic Approach for Ungauged Basin

NASA Astrophysics Data System (ADS)

Srivastava, P. K.; Han, D.; Rico-Ramirez, M. A.; Bray, M.; Islam, T.; Petropoulos, G.; Gupta, M.

2015-12-01

Hydro-meteorological variables such as Precipitation and Reference Evapotranspiration (ETo) are the most important variables for discharge prediction. However, it is not always possible to get access to them from ground based measurements, particularly in ungauged catchments. The mesoscale model WRF (Weather Research & Forecasting model) can be used for prediction of hydro-meteorological variables. However, hydro-meteorologists would like to know how well the downscaled global data products are as compared to ground based measurements and whether it is possible to use the downscaled data for ungauged catchments. Even with gauged catchments, most of the stations have only rain and flow gauges installed. Measurements of other weather hydro-meteorological variables such as solar radiation, wind speed, air temperature, and dew point are usually missing and thus complicate the problems. In this study, for downscaling the global datasets, the WRF model is setup over the Brue catchment with three nested domains (D1, D2 and D3) of horizontal grid spacing of 81 km, 27 km and 9 km are used. The hydro-meteorological variables are downscaled using the WRF model from the National Centers for Enviromental Prediction (NCEP) reanalysis datasets and subsequently used for the ETo estimation using the Penman Monteith equation. The analysis of weather variables and precipitation are compared against the ground based datasets, which indicate that the datasets are in agreement with the observed datasets for complete monitoring period as well as during the seasons except precipitation whose performance is poorer in comparison to the measured rainfall. After a comparison, the WRF estimated precipitation and ETo are then used as a input parameter in the Probability Distributed Model (PDM) for discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimation are also taken into account for the PDM calibration and prediction following the Generalised

Do Variables Associated with Quality Child Care Programs Predict the Inclusion of Children with Disabilities?

ERIC Educational Resources Information Center

Essa, Eva L.; Bennett, Patrick R.; Burnham, Melissa M.; Martin, Sally S.; Bingham, Ann; Allred, Keith

2008-01-01

Little research has been carried out on the inclusion of children with special needs in child care. The purpose of this study was to determine what variables predict the inclusion of children with disabilities in centers and home care. Logistic regression was used to examine the association of several indicators of quality child care and…

Developing Predictive Models for Algal Bloom Occurrence and Identifying Factors Controlling their Occurrence in the Charlotte County and Surroundings

NASA Astrophysics Data System (ADS)

Karki, S.; Sultan, M.; Elkadiri, R.; Chouinard, K.

2017-12-01

Numerous occurrences of harmful algal blooms (Karenia Brevis) were reported from Southwest Florida along the coast of Charlotte County, Florida. We are developing data-driven (remote sensing, field, and meteorological data) models to accomplish the following: (1) identify the factors controlling bloom development, (2) forecast bloom occurrences, and (3) make recommendations for monitoring variables that are found to be most indicative of algal bloom occurrences and for identifying optimum locations for monitoring stations. To accomplish these three tasks we completed/are working on the following steps. Firstly, we developed an automatic system for downloading and processing of ocean color data acquired through MODIS Terra and MODIS Aqua products using SeaDAS ocean color processing software. Examples of extracted variables include: chlorophyll a (OC3M), chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel- Maritorena (GSM), sea surface temperature (SST), Secchi disk depth, euphotic depth, turbidity index, wind direction and speed, colored dissolved organic material (CDOM). Secondly we are developing a GIS database and a web-based GIS to host the generated remote sensing-based products in addition to relevant meteorological and field data. Examples of the meteorological and field inputs include: precipitation amount and rates, concentrations of nitrogen, phosphorous, fecal coliform and Dissolved Oxygen (DO). Thirdly, we are constructing and validating a multivariate regression model and an artificial neural network model to simulate past algal bloom occurrences using the compiled archival remote sensing, meteorological, and field data. The validated model will then be used to predict the timing and location of algal bloom occurrences. The developed system, upon completion, could enhance the decision making process, improve the citizen's quality of life, and strengthen the local economy.

Development and Validation of a Clinic-Based Prediction Tool to Identify Female Athletes at High Risk for Anterior Cruciate Ligament Injury

PubMed Central

Myer, Gregory D.; Ford, Kevin R.; Khoury, Jane; Succop, Paul; Hewett, Timothy E.

2012-01-01

Background Prospective measures of high knee abduction moment (KAM) during landing identify female athletes at high risk for anterior cruciate ligament injury. Laboratory-based measurements demonstrate 90% accuracy in prediction of high KAM. Clinic-based prediction algorithms that employ correlates derived from laboratory-based measurements also demonstrate high accuracy for prediction of high KAM mechanics during landing. Hypotheses Clinic-based measures derived from highly predictive laboratory-based models are valid for the accurate prediction of high KAM status, and simultaneous measurements using laboratory-based and clinic-based techniques highly correlate. Study Design Cohort study (diagnosis); Level of evidence, 2. Methods One hundred female athletes (basketball, soccer, volleyball players) were tested using laboratory-based measures to confirm the validity of identified laboratory-based correlate variables to clinic-based measures included in a prediction algorithm to determine high KAM status. To analyze selected clinic-based surrogate predictors, another cohort of 20 female athletes was simultaneously tested with both clinic-based and laboratory-based measures. Results The prediction model (odds ratio: 95% confidence interval), derived from laboratory-based surrogates including (1) knee valgus motion (1.59: 1.17-2.16 cm), (2) knee flexion range of motion (0.94: 0.89°-1.00°), (3) body mass (0.98: 0.94-1.03 kg), (4) tibia length (1.55: 1.20-2.07 cm), and (5) quadriceps-to-hamstrings ratio (1.70: 0.48%-6.0%), predicted high KAM status with 84% sensitivity and 67% specificity (P < .001). Clinic-based techniques that used a calibrated physician’s scale, a standard measuring tape, standard camcorder, ImageJ software, and an isokinetic dynamometer showed high correlation (knee valgus motion, r = .87; knee flexion range of motion, r = .95; and tibia length, r = .98) to simultaneous laboratory-based measurements. Body mass and quadriceps-to-hamstrings ratio

Identifying the necessary and sufficient number of risk factors for predicting academic failure.

PubMed

Lucio, Robert; Hunt, Elizabeth; Bornovalova, Marina

2012-03-01

Identifying the point at which individuals become at risk for academic failure (grade point average [GPA] < 2.0) involves an understanding of which and how many factors contribute to poor outcomes. School-related factors appear to be among the many factors that significantly impact academic success or failure. This study focused on 12 school-related factors. Using a thorough 5-step process, we identified which unique risk factors place one at risk for academic failure. Academic engagement, academic expectations, academic self-efficacy, homework completion, school relevance, school safety, teacher relationships (positive relationship), grade retention, school mobility, and school misbehaviors (negative relationship) were uniquely related to GPA even after controlling for all relevant covariates. Next, a receiver operating characteristic curve was used to determine a cutoff point for determining how many risk factors predict academic failure (GPA < 2.0). Results yielded a cutoff point of 2 risk factors for predicting academic failure, which provides a way for early identification of individuals who are at risk. Further implications of these findings are discussed. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Children's First Experience of Taking Anabolic-Androgenic Steroids can Occur before Their 10th Birthday: A Systematic Review Identifying 9 Factors That Predicted Doping among Young People

PubMed Central

Nicholls, Adam R.; Cope, Ed; Bailey, Richard; Koenen, Katrin; Dumon, Detlef; Theodorou, Nikolaos C.; Chanal, Benoit; Saint Laurent, Delphine; Müller, David; Andrés, Mar P.; Kristensen, Annemarie H.; Thompson, Mark A.; Baumann, Wolfgang; Laurent, Jean-Francois

2017-01-01

Taking performance-enhancing drugs (PEDs) can cause serious and irreversible health consequences, which can ultimately lead to premature death. Some young people may take PEDs without fully understanding the ramifications of their actions or based on the advice from others. The purpose of this systematic review was to identify the main factors that predicted doping among young people. The literature was systematically reviewed using search engines, manually searching specialist journals, and pearl growing. Fifty-two studies, which included 187,288 young people aged between 10 and 21 years of age, 883 parents of adolescent athletes, and 11 adult coaches, who were interviewed regarding young athletes, were included in this review. Nine factors predicted doping among young people: gender; age; sports participation; sport type; psychological variables; entourage; ethnicity; nutritional supplements; and health harming behaviors. In regards to psychological variables, 22 different constructs were associated with doping among young people. Some psychological constructs were negatively associated with doping (e.g., self-esteem, resisting social pressure, and perfectionist strivings), whereas other were positively associated with doping (e.g., suicide risk, anticipated regret, and aggression). Policy makers and National Anti-Doping Organizations could use these findings to help identify athletes who are more at risk of doping and then expose these individuals to anti-doping education. Based on the current findings, it also appears that education programs should commence at the onset of adolescence or even late childhood, due to the young age in which some individuals start doping. PMID:28676778

Children's First Experience of Taking Anabolic-Androgenic Steroids can Occur before Their 10th Birthday: A Systematic Review Identifying 9 Factors That Predicted Doping among Young People.

PubMed

Nicholls, Adam R; Cope, Ed; Bailey, Richard; Koenen, Katrin; Dumon, Detlef; Theodorou, Nikolaos C; Chanal, Benoit; Saint Laurent, Delphine; Müller, David; Andrés, Mar P; Kristensen, Annemarie H; Thompson, Mark A; Baumann, Wolfgang; Laurent, Jean-Francois

2017-01-01

Taking performance-enhancing drugs (PEDs) can cause serious and irreversible health consequences, which can ultimately lead to premature death. Some young people may take PEDs without fully understanding the ramifications of their actions or based on the advice from others. The purpose of this systematic review was to identify the main factors that predicted doping among young people. The literature was systematically reviewed using search engines, manually searching specialist journals, and pearl growing. Fifty-two studies, which included 187,288 young people aged between 10 and 21 years of age, 883 parents of adolescent athletes, and 11 adult coaches, who were interviewed regarding young athletes, were included in this review. Nine factors predicted doping among young people: gender; age; sports participation; sport type; psychological variables; entourage; ethnicity; nutritional supplements; and health harming behaviors. In regards to psychological variables, 22 different constructs were associated with doping among young people. Some psychological constructs were negatively associated with doping (e.g., self-esteem, resisting social pressure, and perfectionist strivings), whereas other were positively associated with doping (e.g., suicide risk, anticipated regret, and aggression). Policy makers and National Anti-Doping Organizations could use these findings to help identify athletes who are more at risk of doping and then expose these individuals to anti-doping education. Based on the current findings, it also appears that education programs should commence at the onset of adolescence or even late childhood, due to the young age in which some individuals start doping.

Recent and past musical activity predicts cognitive aging variability: direct comparison with general lifestyle activities.

PubMed

Hanna-Pladdy, Brenda; Gajewski, Byron

2012-01-01

Studies evaluating the impact of modifiable lifestyle factors on cognition offer potential insights into sources of cognitive aging variability. Recently, we reported an association between extent of musical instrumental practice throughout the life span (greater than 10 years) on preserved cognitive functioning in advanced age. These findings raise the question of whether there are training-induced brain changes in musicians that can transfer to non-musical cognitive abilities to allow for compensation of age-related cognitive declines. However, because of the relationship between engagement in general lifestyle activities and preserved cognition, it remains unclear whether these findings are specifically driven by musical training or the types of individuals likely to engage in greater activities in general. The current study controlled for general activity level in evaluating cognition between musicians and nomusicians. Also, the timing of engagement (age of acquisition, past versus recent) was assessed in predictive models of successful cognitive aging. Seventy age and education matched older musicians (>10 years) and non-musicians (ages 59-80) were evaluated on neuropsychological tests and general lifestyle activities. Musicians scored higher on tests of phonemic fluency, verbal working memory, verbal immediate recall, visuospatial judgment, and motor dexterity, but did not differ in other general leisure activities. Partition analyses were conducted on significant cognitive measures to determine aspects of musical training predictive of enhanced cognition. The first partition analysis revealed education best predicted visuospatial functions in musicians, followed by recent musical engagement which offset low education. In the second partition analysis, early age of musical acquisition (<9 years) predicted enhanced verbal working memory in musicians, while analyses for other measures were not predictive. Recent and past musical activity, but not general

Can Infants Be "Taught" to Attend to a New Physical Variable in an Event Category? The Case of Height in Covering Events

ERIC Educational Resources Information Center

Wang, Su-hua; Baillargeon, Renee

2008-01-01

As they observe or produce events, infants identify variables that help them predict outcomes in each category of events. How do infants identify a new variable? An explanation-based learning (EBL) account suggests three essential steps: (1) observing contrastive outcomes relevant to the variable; (2) discovering the conditions associated with…

Revealing the ultrafast outflow in IRAS 13224-3809 through spectral variability

NASA Astrophysics Data System (ADS)

Parker, M. L.; Alston, W. N.; Buisson, D. J. K.; Fabian, A. C.; Jiang, J.; Kara, E.; Lohfink, A.; Pinto, C.; Reynolds, C. S.

2017-08-01

We present an analysis of the long-term X-ray variability of the extreme narrow-line Seyfert 1 galaxy IRAS 13224-3809 using principal component analysis (PCA) and fractional excess variability (Fvar) spectra to identify model-independent spectral components. We identify a series of variability peaks in both the first PCA component and Fvar spectrum which correspond to the strongest predicted absorption lines from the ultrafast outflow (UFO) discovered by Parker et al. (2017). We also find higher order PCA components, which correspond to variability of the soft excess and reflection features. The subtle differences between RMS and PCA results argue that the observed flux-dependence of the absorption is due to increased ionization of the gas, rather than changes in column density or covering fraction. This result demonstrates that we can detect outflows from variability alone and that variability studies of UFOs are an extremely promising avenue for future research.

Measuring the effect of inter-study variability on estimating prediction error.

PubMed

Ma, Shuyi; Sung, Jaeyun; Magis, Andrew T; Wang, Yuliang; Geman, Donald; Price, Nathan D

2014-01-01

The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in "batch-effects") and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies. Here we quantify the impact of these combined "study-effects" on a disease signature's predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance. As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification. We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when "sufficient" diversity has been achieved for learning a molecular

Variability of pCO2 in surface waters and development of prediction model.

PubMed

Chung, Sewoong; Park, Hyungseok; Yoo, Jisu

2018-05-01

Inland waters are substantial sources of atmospheric carbon, but relevant data are rare in Asian monsoon regions including Korea. Emissions of CO 2 to the atmosphere depend largely on the partial pressure of CO 2 (pCO 2 ) in water; however, measured pCO 2 data are scarce and calculated pCO 2 can show large uncertainty. This study had three objectives: 1) to examine the spatial variability of pCO 2 in diverse surface water systems in Korea; 2) to compare pCO 2 calculated using pH-total alkalinity (Alk) and pH-dissolved inorganic carbon (DIC) with pCO 2 measured by an in situ submersible nondispersive infrared detector; and 3) to characterize the major environmental variables determining the variation of pCO 2 based on physical, chemical, and biological data collected concomitantly. Of 30 samples, 80% were found supersaturated in CO 2 with respect to the overlying atmosphere. Calculated pCO 2 using pH-Alk and pH-DIC showed weak prediction capability and large variations with respect to measured pCO 2 . Error analysis indicated that calculated pCO 2 is highly sensitive to the accuracy of pH measurements, particularly at low pH. Stepwise multiple linear regression (MLR) and random forest (RF) techniques were implemented to develop the most parsimonious model based on 10 potential predictor variables (pH, Alk, DIC, Uw, Cond, Turb, COD, DOC, TOC, Chla) by optimizing model performance. The RF model showed better performance than the MLR model, and the most parsimonious RF model (pH, Turb, Uw, Chla) improved pCO 2 prediction capability considerably compared with the simple calculation approach, reducing the RMSE from 527-544 to 105μatm at the study sites. Copyright © 2017 Elsevier B.V. All rights reserved.

Evaluating predictive models for solar energy growth in the US states and identifying the key drivers

NASA Astrophysics Data System (ADS)

Chakraborty, Joheen; Banerji, Sugata

2018-03-01

Driven by a desire to control climate change and reduce the dependence on fossil fuels, governments around the world are increasing the adoption of renewable energy sources. However, among the US states, we observe a wide disparity in renewable penetration. In this study, we have identified and cleaned over a dozen datasets representing solar energy penetration in each US state, and the potentially relevant socioeconomic and other factors that may be driving the growth in solar. We have applied a number of predictive modeling approaches - including machine learning and regression - on these datasets over a 17-year period and evaluated the relative performance of the models. Our goals were: (1) identify the most important factors that are driving the growth in solar, (2) choose the most effective predictive modeling technique for solar growth, and (3) develop a model for predicting next year’s solar growth using this year’s data. We obtained very promising results with random forests (about 90% efficacy) and varying degrees of success with support vector machines and regression techniques (linear, polynomial, ridge). We also identified states with solar growth slower than expected and representing a potential for stronger growth in future.

The variability and IRI2007-predictability of hmF2 over South Africa

NASA Astrophysics Data System (ADS)

Mbambo, M. C.; McKinnell, Lee-Anne; Habarulema, J. B.

2013-11-01

This paper presents an investigation into the variability and predictability of the maximum height of the ionospheric F2 layer, hmF2 over the South African region. Data from three South African stations, namely Madimbo (22.4°S, 26.5°E, dip angle: -61.47°), Grahamstown (33.3°S, 26.5°E, dip angle: -64.08°) and Louisvale (28.5°S, 21.2°E, dip angle: -65.44°) were used in this study. The results indicate that hmF2 shows a larger variability around midnight than during the daytime for all seasons. Monthly median hmF2 values were used in all cases and were compared with predictions from the IRI-2007 model, using the URSI (Union Radio-Scientifique Internationale) coefficient option. The analysis covers the diurnal and seasonal hourly hmF2 values for the selected months and time sectors e.g. January, July, April and October for 2003 and 2005. The time ranges between (03h00-23h00 UT; LT = UT + 2h) representing the local sunrise, midday, sunset and midnight hours. The time covers sunrise, midday, sunrise, and midnight hours (03-06h00 UT, 07-11h00 UT, sunrise 16-18h00 UT and 22-23h00 UT; LT = UT + 2h). The dependence of the results on solar activity levels was also investigated. The IRI-2007 predictions follow fairly well the diurnal and seasonal variation patterns of the observed hmF2 values at all the stations. However, the IRI-2007 model overestimates and underestimates the hmF2 value during different months for all the solar activity periods.

kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets.

PubMed

Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S; Beer, Michael A

2013-07-01

Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167-80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org.

kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets

PubMed Central

Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S.; Beer, Michael A.

2013-01-01

Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167–80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org. PMID:23771147

Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability

EPA Science Inventory

We incorporate inter-individual variability, including variability across demographic subgroups, into an open-source high-throughput (HT) toxicokinetics (TK) modeling framework for use in a next-generation risk prioritization approach. Risk prioritization involves rapid triage of...

Free variable selection QSPR study to predict 19F chemical shifts of some fluorinated organic compounds using Random Forest and RBF-PLS methods

NASA Astrophysics Data System (ADS)

Goudarzi, Nasser

2016-04-01

In this work, two new and powerful chemometrics methods are applied for the modeling and prediction of the 19F chemical shift values of some fluorinated organic compounds. The radial basis function-partial least square (RBF-PLS) and random forest (RF) are employed to construct the models to predict the 19F chemical shifts. In this study, we didn't used from any variable selection method and RF method can be used as variable selection and modeling technique. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. The root-mean-square errors of prediction (RMSEP) for the training set and the prediction set for the RBF-PLS and RF models were 44.70, 23.86, 29.77, and 23.69, respectively. Also, the correlation coefficients of the prediction set for the RBF-PLS and RF models were 0.8684 and 0.9313, respectively. The results obtained reveal that the RF model can be used as a powerful chemometrics tool for the quantitative structure-property relationship (QSPR) studies.

A predictability study of Lorenz's 28-variable model as a dynamical system

NASA Technical Reports Server (NTRS)

Krishnamurthy, V.

1993-01-01

The dynamics of error growth in a two-layer nonlinear quasi-geostrophic model has been studied to gain an understanding of the mathematical theory of atmospheric predictability. The growth of random errors of varying initial magnitudes has been studied, and the relation between this classical approach and the concepts of the nonlinear dynamical systems theory has been explored. The local and global growths of random errors have been expressed partly in terms of the properties of an error ellipsoid and the Liapunov exponents determined by linear error dynamics. The local growth of small errors is initially governed by several modes of the evolving error ellipsoid but soon becomes dominated by the longest axis. The average global growth of small errors is exponential with a growth rate consistent with the largest Liapunov exponent. The duration of the exponential growth phase depends on the initial magnitude of the errors. The subsequent large errors undergo a nonlinear growth with a steadily decreasing growth rate and attain saturation that defines the limit of predictability. The degree of chaos and the largest Liapunov exponent show considerable variation with change in the forcing, which implies that the time variation in the external forcing can introduce variable character to the predictability.

The nature and use of prediction skills in a biological computer simulation

NASA Astrophysics Data System (ADS)

Lavoie, Derrick R.; Good, Ron

The primary goal of this study was to examine the science process skill of prediction using qualitative research methodology. The think-aloud interview, modeled after Ericsson and Simon (1984), let to the identification of 63 program exploration and prediction behaviors.The performance of seven formal and seven concrete operational high-school biology students were videotaped during a three-phase learning sequence on water pollution. Subjects explored the effects of five independent variables on two dependent variables over time using a computer-simulation program. Predictions were made concerning the effect of the independent variables upon dependent variables through time. Subjects were identified according to initial knowledge of the subject matter and success at solving three selected prediction problems.Successful predictors generally had high initial knowledge of the subject matter and were formal operational. Unsuccessful predictors generally had low initial knowledge and were concrete operational. High initial knowledge seemed to be more important to predictive success than stage of Piagetian cognitive development.Successful prediction behaviors involved systematic manipulation of the independent variables, note taking, identification and use of appropriate independent-dependent variable relationships, high interest and motivation, and in general, higher-level thinking skills. Behaviors characteristic of unsuccessful predictors were nonsystematic manipulation of independent variables, lack of motivation and persistence, misconceptions, and the identification and use of inappropriate independent-dependent variable relationships.

Predicting Fog in the Nocturnal Boundary Layer

NASA Astrophysics Data System (ADS)

Izett, Jonathan; van de Wiel, Bas; Baas, Peter; van der Linden, Steven; van Hooft, Antoon; Bosveld, Fred

2017-04-01

Fog is a global phenomenon that presents a hazard to navigation and human safety, resulting in significant economic impacts for air and shipping industries as well as causing numerous road traffic accidents. Accurate prediction of fog events, however, remains elusive both in terms of timing and occurrence itself. Statistical methods based on set threshold criteria for key variables such as wind speed have been developed, but high rates of correct prediction of fog events still lead to similarly high "false alarms" when the conditions appear favourable, but no fog forms. Using data from the CESAR meteorological observatory in the Netherlands, we analyze specific cases and perform statistical analyses of event climatology, in order to identify the necessary conditions for correct prediction of fog. We also identify potential "missing ingredients" in current analysis that could help to reduce the number of false alarms. New variables considered include the indicators of boundary layer stability, as well as the presence of aerosols conducive to droplet formation. The poster presents initial findings of new research as well as plans for continued research.

Use of generalised additive models to categorise continuous variables in clinical prediction

PubMed Central

2013-01-01

Background In medical practice many, essentially continuous, clinical parameters tend to be categorised by physicians for ease of decision-making. Indeed, categorisation is a common practice both in medical research and in the development of clinical prediction rules, particularly where the ensuing models are to be applied in daily clinical practice to support clinicians in the decision-making process. Since the number of categories into which a continuous predictor must be categorised depends partly on the relationship between the predictor and the outcome, the need for more than two categories must be borne in mind. Methods We propose a categorisation methodology for clinical-prediction models, using Generalised Additive Models (GAMs) with P-spline smoothers to determine the relationship between the continuous predictor and the outcome. The proposed method consists of creating at least one average-risk category along with high- and low-risk categories based on the GAM smooth function. We applied this methodology to a prospective cohort of patients with exacerbated chronic obstructive pulmonary disease. The predictors selected were respiratory rate and partial pressure of carbon dioxide in the blood (PCO2), and the response variable was poor evolution. An additive logistic regression model was used to show the relationship between the covariates and the dichotomous response variable. The proposed categorisation was compared to the continuous predictor as the best option, using the AIC and AUC evaluation parameters. The sample was divided into a derivation (60%) and validation (40%) samples. The first was used to obtain the cut points while the second was used to validate the proposed methodology. Results The three-category proposal for the respiratory rate was ≤ 20;(20,24];> 24, for which the following values were obtained: AIC=314.5 and AUC=0.638. The respective values for the continuous predictor were AIC=317.1 and AUC=0.634, with no statistically

Prediction of placebo responses: a systematic review of the literature

PubMed Central

Horing, Bjoern; Weimer, Katja; Muth, Eric R.; Enck, Paul

2014-01-01

Objective: Predicting who responds to placebo treatment—and under which circumstances—has been a question of interest and investigation for generations. However, the literature is disparate and inconclusive. This review aims to identify publications that provide high quality data on the topic of placebo response (PR) prediction. Methods: To identify studies concerned with PR prediction, independent searches were performed in an expert database (for all symptom modalities) and in PubMed (for pain only). Articles were selected when (a) they assessed putative predictors prior to placebo treatment and (b) an adequate control group was included when the associations of predictors and PRs were analyzed. Results: Twenty studies were identified, most with pain as dependent variable. Most predictors of PRs were psychological constructs related to actions, expected outcomes and the emotional valence attached to these events (goal-seeking, self-efficacy/-esteem, locus of control, optimism). Other predictors involved behavioral control (desire for control, eating restraint), personality variables (fun seeking, sensation seeking, neuroticism), or biological markers (sex, a single nucleotide polymorphism related to dopamine metabolism). Finally, suggestibility and beliefs in expectation biases, body consciousness, and baseline symptom severity were found to be predictive. Conclusions: While results are heterogeneous, some congruence of predictors can be identified. PRs mainly appear to be moderated by expectations of how the symptom might change after treatment, or expectations of how symptom repetition can be coped with. It is suggested to include the listed constructs in future research. Furthermore, a closer look at variables moderating symptom change in control groups seems warranted. PMID:25324797

Variables Associated with First Year Teacher Morale Which Can Be Identified in a Teacher Education Program.

ERIC Educational Resources Information Center

Thomson, James R., Jr.; Schuck, Robert F.

This paper presents a study of the personal variables associated with first-year teacher morale that can be identified early in the training programs of novice teachers. This study is based on data derived from 96 (76.6 percent) of the graduates teaching in Mississippi. Data were collected through the use of five special instruments: (1)…

Beyond the mean: the role of variability in predicting ecological effects of stream temperature on salmon

Treesearch

E. Ashley Steel; Abby Tillotson; Donald A. Larson; Aimee H. Fullerton; Keith P. Denton; Brian R. Beckman

2012-01-01

Alterations in variance of riverine thermal regimes have been observed and are predicted with climate change and human development. We tested whether changes in daily or seasonal thermal variability, aside from changes in mean temperature, could have biological consequences by exposing Chinook salmon (Oncorhynchus tshawytscha) eggs to eight...

Quantitative DNA Methylation Analysis Identifies a Single CpG Dinucleotide Important for ZAP-70 Expression and Predictive of Prognosis in Chronic Lymphocytic Leukemia

PubMed Central

Claus, Rainer; Lucas, David M.; Stilgenbauer, Stephan; Ruppert, Amy S.; Yu, Lianbo; Zucknick, Manuela; Mertens, Daniel; Bühler, Andreas; Oakes, Christopher C.; Larson, Richard A.; Kay, Neil E.; Jelinek, Diane F.; Kipps, Thomas J.; Rassenti, Laura Z.; Gribben, John G.; Döhner, Hartmut; Heerema, Nyla A.; Marcucci, Guido; Plass, Christoph; Byrd, John C.

2012-01-01

Purpose Increased ZAP-70 expression predicts poor prognosis in chronic lymphocytic leukemia (CLL). Current methods for accurately measuring ZAP-70 expression are problematic, preventing widespread application of these tests in clinical decision making. We therefore used comprehensive DNA methylation profiling of the ZAP-70 regulatory region to identify sites important for transcriptional control. Patients and Methods High-resolution quantitative DNA methylation analysis of the entire ZAP-70 gene regulatory regions was conducted on 247 samples from patients with CLL from four independent clinical studies. Results Through this comprehensive analysis, we identified a small area in the 5′ regulatory region of ZAP-70 that showed large variability in methylation in CLL samples but was universally methylated in normal B cells. High correlation with mRNA and protein expression, as well as activity in promoter reporter assays, revealed that within this differentially methylated region, a single CpG dinucleotide and neighboring nucleotides are particularly important in ZAP-70 transcriptional regulation. Furthermore, by using clustering approaches, we identified a prognostic role for this site in four independent data sets of patients with CLL using time to treatment, progression-free survival, and overall survival as clinical end points. Conclusion Comprehensive quantitative DNA methylation analysis of the ZAP-70 gene in CLL identified important regions responsible for transcriptional regulation. In addition, loss of methylation at a specific single CpG dinucleotide in the ZAP-70 5′ regulatory sequence is a highly predictive and reproducible biomarker of poor prognosis in this disease. This work demonstrates the feasibility of using quantitative specific ZAP-70 methylation analysis as a relevant clinically applicable prognostic test in CLL. PMID:22564988

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.

Predictive Inference Using Latent Variables with Covariates*

PubMed Central

Schofield, Lynne Steuerle; Junker, Brian; Taylor, Lowell J.; Black, Dan A.

2014-01-01

Plausible Values (PVs) are a standard multiple imputation tool for analysis of large education survey data that measures latent proficiency variables. When latent proficiency is the dependent variable, we reconsider the standard institutionally-generated PV methodology and find it applies with greater generality than shown previously. When latent proficiency is an independent variable, we show that the standard institutional PV methodology produces biased inference because the institutional conditioning model places restrictions on the form of the secondary analysts’ model. We offer an alternative approach that avoids these biases based on the mixed effects structural equations (MESE) model of Schofield (2008). PMID:25231627

Predicting Infected Bile Among Patients Undergoing Percutaneous Cholecystostomy

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

Beardsley, Shannon L.; Shlansky-Goldberg, Richard D.; Patel, Aalpen

2005-04-15

Purpose. Patients may not achieve a clinical benefit after percutaneous cholecystostomy due to the inherent difficulty in identifying patients who truly have infected gallbladders. We attempted to identify imaging and biochemical parameters which would help to predict which patients have infected gallbladders. Methods. A retrospective review was performed of 52 patients undergoing percutaneous cholecystostomy for clinical suspicion of acute cholecystitis in whom bile culture results were available. Multiple imaging and biochemical variables were examined alone and in combination as predictors of infected bile, using logistic regression. Results. Of the 52 patients, 25 (48%) had infected bile. Organisms cultured included Enterococcus,more » Enterobacter, Klebsiella, Pseudomonas, E. coli, Citrobacter and Candida. No biochemical parameters were significantly predictive of infected bile; white blood cell count >15,000 was weakly associated with greater odds of infected bile (odds ratio 2.0, p = NS). The presence of gallstones, sludge, gallbladder wall thickening and pericholecystic fluid by ultrasound or CT were not predictive of infected bile, alone or in combination, although a trend was observed among patients with CT findings of acute cholecystitis toward a higher 30-day mortality. Radionuclide scans were performed in 31% of patients; all were positive and 66% of these patients had infected bile. Since no patient who underwent a radionuclide scan had a negative study, this variable could not be entered into the regression model due to collinearity. Conclusion. No single CT or ultrasound imaging variable was predictive of infected bile, and only a weak association of white blood cell count with infected bile was seen. No other biochemical parameters had any association with infected bile. The ability of radionuclide scanning to predict infected bile was higher than that of ultrasound or CT. This study illustrates the continued challenge to identify bacterial

A multivariate and stochastic approach to identify key variables to rank dairy farms on profitability.

PubMed

Atzori, A S; Tedeschi, L O; Cannas, A

2013-05-01

The economic efficiency of dairy farms is the main goal of farmers. The objective of this work was to use routinely available information at the dairy farm level to develop an index of profitability to rank dairy farms and to assist the decision-making process of farmers to increase the economic efficiency of the entire system. A stochastic modeling approach was used to study the relationships between inputs and profitability (i.e., income over feed cost; IOFC) of dairy cattle farms. The IOFC was calculated as: milk revenue + value of male calves + culling revenue - herd feed costs. Two databases were created. The first one was a development database, which was created from technical and economic variables collected in 135 dairy farms. The second one was a synthetic database (sDB) created from 5,000 synthetic dairy farms using the Monte Carlo technique and based on the characteristics of the development database data. The sDB was used to develop a ranking index as follows: (1) principal component analysis (PCA), excluding IOFC, was used to identify principal components (sPC); and (2) coefficient estimates of a multiple regression of the IOFC on the sPC were obtained. Then, the eigenvectors of the sPC were used to compute the principal component values for the original 135 dairy farms that were used with the multiple regression coefficient estimates to predict IOFC (dRI; ranking index from development database). The dRI was used to rank the original 135 dairy farms. The PCA explained 77.6% of the sDB variability and 4 sPC were selected. The sPC were associated with herd profile, milk quality and payment, poor management, and reproduction based on the significant variables of the sPC. The mean IOFC in the sDB was 0.1377 ± 0.0162 euros per liter of milk (€/L). The dRI explained 81% of the variability of the IOFC calculated for the 135 original farms. When the number of farms below and above 1 standard deviation (SD) of the dRI were calculated, we found that 21

Analysis of individual cells identifies cell-to-cell variability following induction of cellular senescence.

PubMed

Wiley, Christopher D; Flynn, James M; Morrissey, Christapher; Lebofsky, Ronald; Shuga, Joe; Dong, Xiao; Unger, Marc A; Vijg, Jan; Melov, Simon; Campisi, Judith

2017-10-01

Senescent cells play important roles in both physiological and pathological processes, including cancer and aging. In all cases, however, senescent cells comprise only a small fraction of tissues. Senescent phenotypes have been studied largely in relatively homogeneous populations of cultured cells. In vivo, senescent cells are generally identified by a small number of markers, but whether and how these markers vary among individual cells is unknown. We therefore utilized a combination of single-cell isolation and a nanofluidic PCR platform to determine the contributions of individual cells to the overall gene expression profile of senescent human fibroblast populations. Individual senescent cells were surprisingly heterogeneous in their gene expression signatures. This cell-to-cell variability resulted in a loss of correlation among the expression of several senescence-associated genes. Many genes encoding senescence-associated secretory phenotype (SASP) factors, a major contributor to the effects of senescent cells in vivo, showed marked variability with a subset of highly induced genes accounting for the increases observed at the population level. Inflammatory genes in clustered genomic loci showed a greater correlation with senescence compared to nonclustered loci, suggesting that these genes are coregulated by genomic location. Together, these data offer new insights into how genes are regulated in senescent cells and suggest that single markers are inadequate to identify senescent cells in vivo. © 2017 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Testing predictive models of positive and negative affect with psychosocial, acculturation, and coping variables in a multiethnic undergraduate sample.

PubMed

Kuo, Ben Ch; Kwantes, Catherine T

2014-01-01

Despite the prevalence and popularity of research on positive and negative affect within the field of psychology, there is currently little research on affect involving the examination of cultural variables and with participants of diverse cultural and ethnic backgrounds. To the authors' knowledge, currently no empirical studies have comprehensively examined predictive models of positive and negative affect based specifically on multiple psychosocial, acculturation, and coping variables as predictors with any sample populations. Therefore, the purpose of the present study was to test the predictive power of perceived stress, social support, bidirectional acculturation (i.e., Canadian acculturation and heritage acculturation), religious coping and cultural coping (i.e., collective, avoidance, and engagement coping) in explaining positive and negative affect in a multiethnic sample of 301 undergraduate students in Canada. Two hierarchal multiple regressions were conducted, one for each affect as the dependent variable, with the above described predictors. The results supported the hypotheses and showed the two overall models to be significant in predicting affect of both kinds. Specifically, a higher level of positive affect was predicted by a lower level of perceived stress, less use of religious coping, and more use of engagement coping in dealing with stress by the participants. Higher level of negative affect, however, was predicted by a higher level of perceived stress and more use of avoidance coping in responding to stress. The current findings highlight the value and relevance of empirically examining the stress-coping-adaptation experiences of diverse populations from an affective conceptual framework, particularly with the inclusion of positive affect. Implications and recommendations for advancing future research and theoretical works in this area are considered and presented.

The role of clinical variables, neuropsychological performance and SLC6A4 and COMT gene polymorphisms on the prediction of early response to fluoxetine in major depressive disorder.

PubMed

Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Cruz, David; Hernández, Sandra; Genis, Alma; Carrillo-Guerrero, Mariana Y; Avilés Reyes, Rubén; Guàrdia-Olmos, Joan

2010-12-01

Major depressive disorder (MDD) is treated with antidepressants, but only between 50% and 70% of the patients respond to the initial treatment. Several authors suggested different factors that could predict antidepressant response, including clinical, psychophysiological, neuropsychological, neuroimaging, and genetic variables. However, these different predictors present poor prognostic sensitivity and specificity by themselves. 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 in the prediction of the response to fluoxetine after 4weeks of treatment in a sample of patient with MDD. 64 patients with MDD were genotyped according to the above-mentioned polymorphisms, and were clinically and neuropsychologically assessed before a 4-week fluoxetine treatment. Fluoxetine response was assessed by using the Hamilton Depression Rating Scale. We carried out a binary logistic regression model for the potential predictive variables. Out of the clinical variables studied, only the number of anxiety disorders comorbid with MDD have predicted a poor response to the treatment. A combination of a good performance in variables of attention and low performance in planning could predict a good response to fluoxetine in patients with MDD. None of the genetic variables studied had predictive value in our model. The possible placebo effect has not been controlled. Our study is focused on response prediction but not in remission prediction. Our work suggests that the combination of the number of comorbid anxiety disorders, an attentional variable, and two planning variables makes it possible to correctly classify 82% of the depressed patients who responded to the treatment with fluoxetine, and 74% of the patients who did not respond to that treatment. Copyright © 2010 Elsevier B.V. All rights reserved.

Predictive Validity of Curriculum-Embedded Measures on Outcomes of Kindergarteners Identified as At Risk for Reading Difficulty

ERIC Educational Resources Information Center

Oslund, Eric L.; Hagan-Burke, Shanna; Simmons, Deborah C.; Clemens, Nathan H.; Simmons, Leslie E.; Taylor, Aaron B.; Kwok, Oi-man; Coyne, Michael D.

2017-01-01

This study examined the predictive validity of formative assessments embedded in a Tier 2 intervention curriculum for kindergarten students identified as at risk for reading difficulty. We examined when (i.e., months during the school year) measures could predict reading outcomes gathered at the end of kindergarten and whether the predictive…

Identifying and Predicting Profiles of Medical Noncompliance: Pediatric Caregivers' Antibiotic Stewardship.

PubMed

Smith, Rachel A; Kim, Youllee; M'Ikanatha, Nkuchia M

2018-05-14

Sometimes compliance with medical recommendations is problematic. We investigated pediatric caregivers' (N = 606) patterns of noncompliance with antibiotic stewardship based on the obstacle hypothesis. We tested predictors of noncompliance framed by the obstacle hypothesis, dissonance theory, and psychological reactance. The results revealed four profiles of caregivers' stewardship: one marked by compliance (Stewards) and three marked by types of noncompliance (Stockers, Persuaders, and Dissenters). The covariate analysis showed that, although psychological reactance predicted being noncompliant, it was types of obstacles and discrepant experiences that predicted caregivers' patterns of noncompliance with antibiotic stewardship. Campaign planning often focuses on identifying the belief most associated with the targeted outcome, such as compliance. Noncompliance research, however, points out that persuaders may be successful to the extent to which they anticipate obstacles to compliance and address them in their influence attempts. A shift from medical noncompliance to patient engagement also affords an opportunity to consider how some recommendations create obstacles for others and to find positive ways to embrace conflicting needs, tensions, and reasons for refusal in order to promote collective goals.

Individual aptitude in Mandarin lexical tone perception predicts effectiveness of high-variability training

PubMed Central

Sadakata, Makiko; McQueen, James M.

2014-01-01

Although the high-variability training method can enhance learning of non-native speech categories, this can depend on individuals’ aptitude. The current study asked how general the effects of perceptual aptitude are by testing whether they occur with training materials spoken by native speakers and whether they depend on the nature of the to-be-learned material. Forty-five native Dutch listeners took part in a 5-day training procedure in which they identified bisyllabic Mandarin pseudowords (e.g., asa) pronounced with different lexical tone combinations. The training materials were presented to different groups of listeners at three levels of variability: low (many repetitions of a limited set of words recorded by a single speaker), medium (fewer repetitions of a more variable set of words recorded by three speakers), and high (similar to medium but with five speakers). Overall, variability did not influence learning performance, but this was due to an interaction with individuals’ perceptual aptitude: increasing variability hindered improvements in performance for low-aptitude perceivers while it helped improvements in performance for high-aptitude perceivers. These results show that the previously observed interaction between individuals’ aptitude and effects of degree of variability extends to natural tokens of Mandarin speech. This interaction was not found, however, in a closely matched study in which native Dutch listeners were trained on the Japanese geminate/singleton consonant contrast. This may indicate that the effectiveness of high-variability training depends not only on individuals’ aptitude in speech perception but also on the nature of the categories being acquired. PMID:25505434

Intraindividual variability in basic reaction time predicts middle-aged and older pilots' flight simulator performance.

PubMed

Kennedy, Quinn; Taylor, Joy; Heraldez, Daniel; Noda, Art; Lazzeroni, Laura C; Yesavage, Jerome

2013-07-01

Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Two-hundred and thirty-six pilots (40-69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%-12% of the negative age effect on initial flight performance. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance.

Intraindividual Variability in Basic Reaction Time Predicts Middle-Aged and Older Pilots’ Flight Simulator Performance

PubMed Central

2013-01-01

Objectives. Intraindividual variability (IIV) is negatively associated with cognitive test performance and is positively associated with age and some neurological disorders. We aimed to extend these findings to a real-world task, flight simulator performance. We hypothesized that IIV predicts poorer initial flight performance and increased rate of decline in performance among middle-aged and older pilots. Method. Two-hundred and thirty-six pilots (40–69 years) completed annual assessments comprising a cognitive battery and two 75-min simulated flights in a flight simulator. Basic and complex IIV composite variables were created from measures of basic reaction time and shifting and divided attention tasks. Flight simulator performance was characterized by an overall summary score and scores on communication, emergencies, approach, and traffic avoidance components. Results. Although basic IIV did not predict rate of decline in flight performance, it had a negative association with initial performance for most flight measures. After taking into account processing speed, basic IIV explained an additional 8%–12% of the negative age effect on initial flight performance. Discussion. IIV plays an important role in real-world tasks and is another aspect of cognition that underlies age-related differences in cognitive performance. PMID:23052365

A Western Diet Ecological Module Identified from the ‘Humanized’ Mouse Microbiota Predicts Diet in Adults and Formula Feeding in Children

PubMed Central

Siddharth, Jay; Holway, Nicholas; Parkinson, Scott J.

2013-01-01

The interplay between diet and the microbiota has been implicated in the growing frequency of chronic diseases associated with the Western lifestyle. However, the complexity and variability of microbial ecology in humans and preclinical models has hampered identification of the molecular mechanisms underlying the association of the microbiota in this context. We sought to address two key questions. Can the microbial ecology of preclinical models predict human populations? And can we identify underlying principles that surpass the plasticity of microbial ecology in humans? To do this, we focused our study on diet; perhaps the most influential factor determining the composition of the gut microbiota. Beginning with a study in ‘humanized’ mice we identified an interactive module of 9 genera allied with Western diet intake. This module was applied to a controlled dietary study in humans. The abundance of the Western ecological module correctly predicted the dietary intake of 19/21 top and 21/21 of the bottom quartile samples inclusive of all 5 Western and ‘low-fat’ diet subjects, respectively. In 98 volunteers the abundance of the Western module correlated appropriately with dietary intake of saturated fatty acids, fat-soluble vitamins and fiber. Furthermore, it correlated with the geographical location and dietary habits of healthy adults from the Western, developing and third world. The module was also coupled to dietary intake in children (and piglets) correlating with formula (vs breast) feeding and associated with a precipitous development of the ecological module in young children. Our study provides a conceptual platform to translate microbial ecology from preclinical models to humans and identifies an ecological network module underlying the association of the gut microbiota with Western dietary habits. PMID:24391809

Ex vivo metabolic fingerprinting identifies biomarkers predictive of prostate cancer recurrence following radical prostatectomy.

PubMed

Braadland, Peder R; Giskeødegård, Guro; Sandsmark, Elise; Bertilsson, Helena; Euceda, Leslie R; Hansen, Ailin F; Guldvik, Ingrid J; Selnæs, Kirsten M; Grytli, Helene H; Katz, Betina; Svindland, Aud; Bathen, Tone F; Eri, Lars M; Nygård, Ståle; Berge, Viktor; Taskén, Kristin A; Tessem, May-Britt

2017-11-21

Robust biomarkers that identify prostate cancer patients with high risk of recurrence will improve personalised cancer care. In this study, we investigated whether tissue metabolites detectable by high-resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS MRS) were associated with recurrence following radical prostatectomy. We performed a retrospective ex vivo study using HR-MAS MRS on tissue samples from 110 radical prostatectomy specimens obtained from three different Norwegian cohorts collected between 2002 and 2010. At the time of analysis, 50 patients had experienced prostate cancer recurrence. Associations between metabolites, clinicopathological variables, and recurrence-free survival were evaluated using Cox proportional hazards regression modelling, Kaplan-Meier survival analyses and concordance index (C-index). High intratumoural spermine and citrate concentrations were associated with longer recurrence-free survival, whereas high (total-choline+creatine)/spermine (tChoCre/Spm) and higher (total-choline+creatine)/citrate (tChoCre/Cit) ratios were associated with shorter time to recurrence. Spermine concentration and tChoCre/Spm were independently associated with recurrence in multivariate Cox proportional hazards modelling after adjusting for clinically relevant risk factors (C-index: 0.769; HR: 0.72; P=0.016 and C-index: 0.765; HR: 1.43; P=0.014, respectively). Spermine concentration and tChoCre/Spm ratio in prostatectomy specimens were independent prognostic markers of recurrence. These metabolites can be noninvasively measured in vivo and may thus offer predictive value to establish preoperative risk assessment nomograms.

Initial sociometric impressions of attention-deficit hyperactivity disorder and comparison boys: predictions from social behaviors and from nonbehavioral variables.

PubMed

Erhardt, Drew; Hinshaw, Stephen P

1994-08-01

This study systematically compared the influence of naturalistic social behaviors and nonbehavioral variables on the development of peer status in 49 previously unfamiliar boys, aged 6-12 years, who attended a summer research program. Twenty-five boys with attention-deficit hyperactivity disorder (ADHD) and 24 comparison boys participated. Physical attractiveness, motor competence, intelligence, and academic achievement constituted the nonbehavioral variables; social behaviors included noncompliance, aggression, prosocial actions, and isolation, measured by live observations of classroom and playground interactions. As early as the first day of interaction, ADHD and comparison boys displayed clear differences in social behaviors, and the ADHD youngsters were overwhelmingly rejected. Whereas prosocial behavior independently predicted friendship ratings during the first week, the magnitude of prediction was small. In contrast, the boys' aggression (or noncompliance) strongly predicted negative nominations, even with nonbehavioral factors, group status (ADHD versus comparison), and other social behaviors controlled statistically. Implications for understanding and remediating negative peer reputations are discussed.

Reward-related neural activity and structure predict future substance use in dysregulated youth.

PubMed

Bertocci, M A; Bebko, G; Versace, A; Iyengar, S; Bonar, L; Forbes, E E; Almeida, J R C; Perlman, S B; Schirda, C; Travis, M J; Gill, M K; Diwadkar, V A; Sunshine, J L; Holland, S K; Kowatch, R A; Birmaher, B; Axelson, D A; Frazier, T W; Arnold, L E; Fristad, M A; Youngstrom, E A; Horwitz, S M; Findling, R L; Phillips, M L

2017-06-01

Identifying youth who may engage in future substance use could facilitate early identification of substance use disorder vulnerability. We aimed to identify biomarkers that predicted future substance use in psychiatrically un-well youth. LASSO regression for variable selection was used to predict substance use 24.3 months after neuroimaging assessment in 73 behaviorally and emotionally dysregulated youth aged 13.9 (s.d. = 2.0) years, 30 female, from three clinical sites in the Longitudinal Assessment of Manic Symptoms (LAMS) study. Predictor variables included neural activity during a reward task, cortical thickness, and clinical and demographic variables. Future substance use was associated with higher left middle prefrontal cortex activity, lower left ventral anterior insula activity, thicker caudal anterior cingulate cortex, higher depression and lower mania scores, not using antipsychotic medication, more parental stress, older age. This combination of variables explained 60.4% of the variance in future substance use, and accurately classified 83.6%. These variables explained a large proportion of the variance, were useful classifiers of future substance use, and showed the value of combining multiple domains to provide a comprehensive understanding of substance use development. This may be a step toward identifying neural measures that can identify future substance use disorder risk, and act as targets for therapeutic interventions.

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.

Spectral analysis of heart rate variability predicts mortality and instability from vascular injury.

PubMed

Koko, Kiavash R; McCauley, Brian D; Gaughan, John P; Fromer, Marc W; Nolan, Ryan S; Hagaman, Ashleigh L; Brown, Spencer A; Hazelton, Joshua P

2018-04-01

Spectral analysis of continuous blood pressure and heart rate variability provides a quantitative assessment of autonomic response to hemorrhage. This may reveal markers of mortality as well as endpoints of resuscitation. Fourteen male Yorkshire pigs, ranging in weight from 33 to 36 kg, were included in the analysis. All pigs underwent laparotomy and then sustained a standardized retrohepatic inferior vena cava injury. Animals were then allowed to progress to class 3 hemorrhagic shock and where then treated with abdominal sponge packing followed by 6 h of crystalloid resuscitation. If the pigs survived the 6 h resuscitation, they were in the survival (S) group, otherwise they were placed in the nonsurvival (NS) group. Fast Fourier transformation calculations were used to convert the components of blood pressure and heart rate variability into corresponding frequency classifications. Autonomic tones are represented as the following: high frequency (HF) = parasympathetic tone, low frequency (LF) = sympathetic, and very low frequency (VLF) = renin-angiotensin aldosterone system. The relative sympathetic to parasympathetic tone was expressed as LF/HF ratio. Baseline hemodynamic parameters were equal for the S (n = 11) and NS groups. LF/HF was lower at baseline for the NS group but was higher after hemorrhage and the resuscitation period indicative of a predominately parasympathetic response during hemorrhagic shock before mortality. HF signal was lower in the NS group during the resuscitation indicating a relatively lower sympathetic tone during hemorrhagic shock, which may have contributed to mortality. Finally, the NS group had a lower VLF signal at baseline (e.g., [S] 16.3 ± 2.5 versus [NS] 4.6 ± 2.9 P < 0.05,) which was predictive of mortality and hemodynamic instability in response to a similar hemorrhagic injury. An increased LF/HF ratio, indicative of parasympathetic predominance following injury and during resuscitation of hemorrhagic shock

Probabilistic versus deterministic skill in predicting the western North Pacific-East Asian summer monsoon variability with multimodel ensembles

NASA Astrophysics Data System (ADS)

Yang, Xiu-Qun; Yang, Dejian; Xie, Qian; Zhang, Yaocun; Ren, Xuejuan; Tang, Youmin

2017-04-01

Based on historical forecasts of three quasi-operational multi-model ensemble (MME) systems, this study assesses the superiority of coupled MME over contributing single-model ensembles (SMEs) and over uncoupled atmospheric MME in predicting the Western North Pacific-East Asian summer monsoon variability. The probabilistic and deterministic forecast skills are measured by Brier skill score (BSS) and anomaly correlation (AC), respectively. A forecast-format dependent MME superiority over SMEs is found. The probabilistic forecast skill of the MME is always significantly better than that of each SME, while the deterministic forecast skill of the MME can be lower than that of some SMEs. The MME superiority arises from both the model diversity and the ensemble size increase in the tropics, and primarily from the ensemble size increase in the subtropics. The BSS is composed of reliability and resolution, two attributes characterizing probabilistic forecast skill. The probabilistic skill increase of the MME is dominated by the dramatic improvement in reliability, while resolution is not always improved, similar to AC. A monotonic resolution-AC relationship is further found and qualitatively explained, whereas little relationship can be identified between reliability and AC. It is argued that the MME's success in improving the reliability arises from an effective reduction of the overconfidence in forecast distributions. Moreover, it is examined that the seasonal predictions with coupled MME are more skillful than those with the uncoupled atmospheric MME forced by persisting sea surface temperature (SST) anomalies, since the coupled MME has better predicted the SST anomaly evolution in three key regions.

Shedding light on the variability of optical skin properties: finding a path towards more accurate prediction of light propagation in human cutaneous compartments

PubMed Central

Mignon, C.; Tobin, D. J.; Zeitouny, M.; Uzunbajakava, N. E.

2018-01-01

Finding a path towards a more accurate prediction of light propagation in human skin remains an aspiration of biomedical scientists working on cutaneous applications both for diagnostic and therapeutic reasons. The objective of this study was to investigate variability of the optical properties of human skin compartments reported in literature, to explore the underlying rational of this variability and to propose a dataset of values, to better represent an in vivo case and recommend a solution towards a more accurate prediction of light propagation through cutaneous compartments. To achieve this, we undertook a novel, logical yet simple approach. We first reviewed scientific articles published between 1981 and 2013 that reported on skin optical properties, to reveal the spread in the reported quantitative values. We found variations of up to 100-fold. Then we extracted the most trust-worthy datasets guided by a rule that the spectral properties should reflect the specific biochemical composition of each of the skin layers. This resulted in the narrowing of the spread in the calculated photon densities to 6-fold. We conclude with a recommendation to use the identified most robust datasets when estimating light propagation in human skin using Monte Carlo simulations. Alternatively, otherwise follow our proposed strategy to screen any new datasets to determine their biological relevance. PMID:29552418

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

An Investigation of the Variables Predicting Faculty of Education Students' Speaking Anxiety through Ordinal Logistic Regression Analysis

ERIC Educational Resources Information Center

Bozpolat, Ebru

2017-01-01

The purpose of this study is to determine whether Cumhuriyet University Faculty of Education students' levels of speaking anxiety are predicted by the variables of gender, department, grade, such sub-dimensions of "Speaking Self-Efficacy Scale for Pre-Service Teachers" as "public speaking," "effective speaking,"…

Heart rate variability among women undergoing in vitro fertilization treatment: Its predictive ability for pregnancy

PubMed Central

Wu, Meng-Hsing; Su, Pei-Fang; Chen, Kuan-Ya; Tie, Tung-Hee; Ke, Hsu-Cheng; Chen, Hau; Su, Yu-Chi; Su, Yu-Chen

2018-01-01

Objective This study aimed to assess predictive ability of heart rate variability (HRV) for pregnancy outcomes with in vitro fertilization (IVF) treatment. Research design and method A total of 180 women with 261 cycles of IVF and 211 embryo transfers (ETs) were analyzed. HRV was measured at four times during IVF treatment: the first date of menstruation, r-HCG (Ovidrel) administration, and before and after ET. Pregnancy indicators included chemical pregnancy, ongoing pregnancy (> 10 weeks), and live birth (pregnancy > 24 weeks). Mixed effect models were applied to identify predictors for IVF pregnancy. The area under the receiver operating characteristic curve (AUC) was used to assess prediction models for pregnancy. Results The HRV values increased during IVF treatment and then decreased after ET. The trend of changes in HRV values during IVF treatment was significant among patients with chemical pregnancy (p < 0.01) and those with live birth (p = 0.02). Women without pregnancy had lower HRV compared to those with IVF pregnancy (p < 0.05). With a one unit increase in HRV difference before and after ET, the odds of chemical pregnancy decreased by 18% (odds ratio; OR: 0.82, 95% CI: 0.70–0.97, p < 0.02). With a one year increase in maternal age, the odds decreased by 16% (OR: 0.84, 95% CI: 0.76–0.93, p < 0.01), 25% (OR: 0.75, 95% CI: 0.58–0.93, p = 0.02), and 28% (OR: 0.72, 95% CI: 0.54–0.91, p = 0.01) for chemical pregnancy, ongoing pregnancy, and live birth, respectively. The AUCs were 0.77 (95% CI: 0.70, 0.84), 0.89 (0.79, 0.98), and 0.91(0.83, 0.99) for the prediction models for chemical pregnancy, ongoing pregnancy, and live birth, respectively. Conclusions Reduced HRV may be an indicator for low chance of IVF pregnancy. The changes in HRV before and after ET and maternal age might be prognostic predictors of IVF pregnancy. PMID:29529100

Collaborative Research: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic

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

Gutowski, William J.

This project developed and applied a regional Arctic System model for enhanced decadal predictions. It built on successful research by four of the current PIs with support from the DOE Climate Change Prediction Program, which has resulted in the development of a fully coupled Regional Arctic Climate Model (RACM) consisting of atmosphere, land-hydrology, ocean and sea ice components. An expanded RACM, a Regional Arctic System Model (RASM), has been set up to include ice sheets, ice caps, mountain glaciers, and dynamic vegetation to allow investigation of coupled physical processes responsible for decadal-scale climate change and variability in the Arctic. RASMmore » can have high spatial resolution (~4-20 times higher than currently practical in global models) to advance modeling of critical processes and determine the need for their explicit representation in Global Earth System Models (GESMs). The pan-Arctic region is a key indicator of the state of global climate through polar amplification. However, a system-level understanding of critical arctic processes and feedbacks needs further development. Rapid climate change has occurred in a number of Arctic System components during the past few decades, including retreat of the perennial sea ice cover, increased surface melting of the Greenland ice sheet, acceleration and thinning of outlet glaciers, reduced snow cover, thawing permafrost, and shifts in vegetation. Such changes could have significant ramifications for global sea level, the ocean thermohaline circulation and heat budget, ecosystems, native communities, natural resource exploration, and commercial transportation. The overarching goal of the RASM project has been to advance understanding of past and present states of arctic climate and to improve seasonal to decadal predictions. To do this the project has focused on variability and long-term change of energy and freshwater flows through the arctic climate system. The three foci of this research are

Profiles of neurological outcome prediction among intensivists.

PubMed

Racine, Eric; Dion, Marie-Josée; Wijman, Christine A C; Illes, Judy; Lansberg, Maarten G

2009-12-01

Advances in intensive care medicine have increased survival rates of patients with critical neurological conditions. The focus of prognostication for such patients is therefore shifting from predicting chances of survival to meaningful neurological recovery. This study assessed the variability in long-term outcome predictions among physicians and aimed to identify factors that may account for this variability. Based on a clinical vignette describing a comatose patient suffering from post-anoxic brain injury intensivists were asked in a semi-structured interview about the patient's specific neurological prognosis and about prognostication in general. Qualitative research methods were used to identify areas of variability in prognostication and to classify physicians according to specific prognostication profiles. Quantitative statistics were used to assess for associations between prognostication profiles and physicians' demographic and practice characteristics. Eighteen intensivists participated. Functional outcome predictions varied along an evaluative dimension (fair/good-poor) and a confidence dimension (certain-uncertain). More experienced physicians tended to be more pessimistic about the patient's functional outcome and more certain of their prognosis. Attitudes toward quality of life varied along an evaluative dimension (good-poor) and a "style" dimension (objective-subjective). Older and more experienced physicians were more likely to express objective judgments of quality of life and to predict a worse quality of life for the patient than their younger and less experienced counterparts. Various prognostication profiles exist among intensivists. These may be dictated by factors such as physicians' age and clinical experience. Awareness of these associations may be a first step to more uniform prognostication.

Prediction complements explanation in understanding the developing brain.

PubMed

Rosenberg, Monica D; Casey, B J; Holmes, Avram J

2018-02-21

A central aim of human neuroscience is understanding the neurobiology of cognition and behavior. Although we have made significant progress towards this goal, reliance on group-level studies of the developed adult brain has limited our ability to explain population variability and developmental changes in neural circuitry and behavior. In this review, we suggest that predictive modeling, a method for predicting individual differences in behavior from brain features, can complement descriptive approaches and provide new ways to account for this variability. Highlighting the outsized scientific and clinical benefits of prediction in developmental populations including adolescence, we show that predictive brain-based models are already providing new insights on adolescent-specific risk-related behaviors. Together with large-scale developmental neuroimaging datasets and complementary analytic approaches, predictive modeling affords us the opportunity and obligation to identify novel treatment targets and individually tailor the course of interventions for developmental psychopathologies that impact so many young people today.

Lawsuit lead time prediction: Comparison of data mining techniques based on categorical response variable.

PubMed

Gruginskie, Lúcia Adriana Dos Santos; Vaccaro, Guilherme Luís Roehe

2018-01-01

The quality of the judicial system of a country can be verified by the overall length time of lawsuits, or the lead time. When the lead time is excessive, a country's economy can be affected, leading to the adoption of measures such as the creation of the Saturn Center in Europe. Although there are performance indicators to measure the lead time of lawsuits, the analysis and the fit of prediction models are still underdeveloped themes in the literature. To contribute to this subject, this article compares different prediction models according to their accuracy, sensitivity, specificity, precision, and F1 measure. The database used was from TRF4-the Tribunal Regional Federal da 4a Região-a federal court in southern Brazil, corresponding to the 2nd Instance civil lawsuits completed in 2016. The models were fitted using support vector machine, naive Bayes, random forests, and neural network approaches with categorical predictor variables. The lead time of the 2nd Instance judgment was selected as the response variable measured in days and categorized in bands. The comparison among the models showed that the support vector machine and random forest approaches produced measurements that were superior to those of the other models. The evaluation of the models was made using k-fold cross-validation similar to that applied to the test models.

Increasing work-time influence: consequences for flexibility, variability, regularity and predictability.

PubMed

Nabe-Nielsen, Kirsten; Garde, Anne Helene; Aust, Birgit; Diderichsen, Finn

2012-01-01

This quasi-experimental study investigated how an intervention aiming at increasing eldercare workers' influence on their working hours affected the flexibility, variability, regularity and predictability of the working hours. We used baseline (n = 296) and follow-up (n = 274) questionnaire data and interviews with intervention-group participants (n = 32). The work units in the intervention group designed their own intervention comprising either implementation of computerised self-scheduling (subgroup A), collection of information about the employees' work-time preferences by questionnaires (subgroup B), or discussion of working hours (subgroup C). Only computerised self-scheduling changed the working hours and the way they were planned. These changes implied more flexible but less regular working hours and an experience of less predictability and less continuity in the care of clients and in the co-operation with colleagues. In subgroup B and C, the participants ended up discussing the potential consequences of more work-time influence without actually implementing any changes. Employee work-time influence may buffer the adverse effects of shift work. However, our intervention study suggested that while increasing the individual flexibility, increasing work-time influence may also result in decreased regularity of the working hours and less continuity in the care of clients and co-operation with colleagues.

Improved geometric variables for predicting disturbed flow at the normal carotid bifurcation

NASA Astrophysics Data System (ADS)

Bijari, Payam B.; Antiga, Luca; Steinman, David A.

2011-03-01

Recent work from our group has shown the primacy of the bifurcation area ratio and tortuosity in determining the amount of disturbed flow at the carotid bifurcation, believed to be a local risk factor for the carotid atherosclerosis. We have also presented fast and reliable methods of extraction of geometry from routine 3D contrast-enhanced magnetic resonance angiography, as the necessary step along the way for large-scale trials of such local risk factors. In the present study, we refine our original geometric variables to better reflect the underlying fluid mechanical principles. Flaring of the bifurcation, leading to flow separation, is defined by the maximum relative expansion of the common carotid artery (CCA), proximal to the bifurcation apex. The beneficial effect of curvature on flow inertia, via its suppression of flow separation, is now characterized by the tortuosity of CCA as it enters the flare region. Based on data from 50 normal carotid bifurcations, multiple linear regressions of these new independent geometric predictors against the dependent disturbed flow burden reveals adjusted R2 values approaching 0.5, better than the values closer to 0.3 achieved using the original variables. The excellent scan-rescan reproducibility demonstrated for our earlier geometric variables is shown to be preserved for the new definitions. Improved prediction of disturbed flow by robust and reproducible vascular geometry offers a practical pathway to large-scale studies of local risk factors in atherosclerosis.

A new simplex chemometric approach to identify olive oil blends with potentially high traceability.

PubMed

Semmar, N; Laroussi-Mezghani, S; Grati-Kamoun, N; Hammami, M; Artaud, J

2016-10-01

Olive oil blends (OOBs) are complex matrices combining different cultivars at variable proportions. Although qualitative determinations of OOBs have been subjected to several chemometric works, quantitative evaluations of their contents remain poorly developed because of traceability difficulties concerning co-occurring cultivars. Around this question, we recently published an original simplex approach helping to develop predictive models of the proportions of co-occurring cultivars from chemical profiles of resulting blends (Semmar & Artaud, 2015). Beyond predictive model construction and validation, this paper presents an extension based on prediction errors' analysis to statistically define the blends with the highest predictability among all the possible ones that can be made by mixing cultivars at different proportions. This provides an interesting way to identify a priori labeled commercial products with potentially high traceability taking into account the natural chemical variability of different constitutive cultivars. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting Preservice Music Teachers' Performance Success in Instrumental Courses Using Self-Regulated Study Strategies and Predictor Variables

ERIC Educational Resources Information Center

Ersozlu, Zehra N.; Nietfeld, John L.; Huseynova, Lale

2017-01-01

The purpose of this study was to examine the extent to which self-regulated study strategies and predictor variables predict performance success in instrumental performance college courses. Preservice music teachers (N = 123) from a music education department in two state universities in Turkey completed the Music Self-Regulated Studying…

Identifying and describing feelings and psychological flexibility predict mental health in men with HIV.

PubMed

Landstra, Jodie M B; Ciarrochi, Joseph; Deane, Frank P; Hillman, Richard J

2013-11-01

Difficulty identifying and describing feelings (DIDF) and psychological flexibility (PF) predict poor emotional adjustment. To examine the relationship between DIDF and PF and whether DIDF and low PF would put men undergoing cancer screening at risk for poor adjustment. Longitudinal self-report survey. Two hundred and one HIV-infected men who have sex with men participated in anal cancer screening at two time points over 14 weeks. Psychological flexibility was assessed by the Acceptance and Action Questionnaire II and DIDF by the Toronto Alexithymia Scale-20. We also measured depression, anxiety, stress (DASS) and health-related quality of life (QOL; SF-12). Both DIDF and PF were reliable predictors of mental health. When levels of baseline mental health were controlled, greater DIDF predicted increases in Time 2 depression, anxiety and stress and decreases in mental and physical QOL. The link between PF and mental health was entirely mediated by DIDF. Being chronically low in PF could lead to greater DIDF and thereby worse mental health. Having more PF promotes the ability to identify and differentiate the nuances of pleasant and unpleasant emotions, which enhances an individual's mental health. Intentionally enhancing men's ability to identify and describe feelings or PF may assist them to better manage a range of difficult life experiences such as health screenings and other potentially threatening information. © 2013 The British Psychological Society.

Peak-summer East Asian rainfall predictability and prediction part I: Southeast Asia

NASA Astrophysics Data System (ADS)

Xing, Wen; Wang, Bin; Yim, So-Young

2016-07-01

The interannual variation of East Asia summer monsoon (EASM) rainfall exhibits considerable differences between early summer [May-June (MJ)] and peak summer [July-August (JA)]. The present study focuses on peak summer. During JA, the mean ridge line of the western Pacific subtropical High (WPSH) divides EASM domain into two sub-domains: the tropical EA (5°N-26.5°N) and subtropical-extratropical EA (26.5°N-50°N). Since the major variability patterns in the two sub-domains and their origins are substantially different, the Part I of this study concentrates on the tropical EA or Southeast Asia (SEA). We apply the predictable mode analysis approach to explore the predictability and prediction of the SEA peak summer rainfall. Four principal modes of interannual rainfall variability during 1979-2013 are identified by EOF analysis: (1) the WPSH-dipole sea surface temperature (SST) feedback mode in the Northern Indo-western Pacific warm pool associated with the decay of eastern Pacific El Niño/Southern Oscillation (ENSO), (2) the central Pacific-ENSO mode, (3) the Maritime continent SST-Australian High coupled mode, which is sustained by a positive feedback between anomalous Australian high and sea surface temperature anomalies (SSTA) over Indian Ocean, and (4) the ENSO developing mode. Based on understanding of the sources of the predictability for each mode, a set of physics-based empirical (P-E) models is established for prediction of the first four leading principal components (PCs). All predictors are selected from either persistent atmospheric lower boundary anomalies from March to June or the tendency from spring to early summer. We show that these four modes can be predicted reasonably well by the P-E models, thus they are identified as the predictable modes. Using the predicted PCs and the corresponding observed spatial patterns, we have made a 35-year cross-validated hindcast, setting up a bench mark for dynamic models' predictions. The P-E hindcast

Predictive models for Escherichia coli concentrations at inland lake beaches and relationship of model variables to pathogen detection

EPA Science Inventory

Methods are needed improve the timeliness and accuracy of recreational water‐quality assessments. Traditional culture methods require 18–24 h to obtain results and may not reflect current conditions. Predictive models, based on environmental and water quality variables, have been...

Intraindividual Variability in Executive Functions but Not Speed of Processing or Conflict Resolution Predicts Performance Differences in Gait Speed in Older Adults

PubMed Central

Mahoney, Jeannette; Verghese, Joe

2014-01-01

Background. The relationship between executive functions (EF) and gait speed is well established. However, with the exception of dual tasking, the key components of EF that predict differences in gait performance have not been determined. Therefore, the current study was designed to determine whether processing speed, conflict resolution, and intraindividual variability in EF predicted variance in gait performance in single- and dual-task conditions. Methods. Participants were 234 nondemented older adults (mean age 76.48 years; 55% women) enrolled in a community-based cohort study. Gait speed was assessed using an instrumented walkway during single- and dual-task conditions. The flanker task was used to assess EF. Results. Results from the linear mixed effects model showed that (a) dual-task interference caused a significant dual-task cost in gait speed (estimate = 35.99; 95% CI = 33.19–38.80) and (b) of the cognitive predictors, only intraindividual variability was associated with gait speed (estimate = −.606; 95% CI = −1.11 to −.10). In unadjusted analyses, the three EF measures were related to gait speed in single- and dual-task conditions. However, in fully adjusted linear regression analysis, only intraindividual variability predicted performance differences in gait speed during dual tasking (B = −.901; 95% CI = −1.557 to −.245). Conclusion. Among the three EF measures assessed, intraindividual variability but not speed of processing or conflict resolution predicted performance differences in gait speed. PMID:24285744

Identifying emotional intelligence skills of Turkish clinical nurses according to sociodemographic and professional variables.

PubMed

Kahraman, Nilgün; Hiçdurmaz, Duygu

2016-04-01

This study aimed to identify the emotional intelligence skills of Turkish clinical nurses according to sociodemographic and professional variables. Emotional intelligence is "the ability of a person to comprehend self-emotions, to show empathy towards the feelings of others, and to control self-emotions in a way that enriches life." Nurses with a higher emotional intelligence level offer more efficient and professional care, and they accomplish more in their social and professional lives. We designed a descriptive cross-sectional study. The Introductory Information Form and the Bar-On emotional intelligence Inventory were used to collect data between 20th June and 20th August 2012. The study was conducted with 312 nurses from 37 hospitals located within the borders of the metropolitan municipality in Ankara. There were no significant differences between emotional intelligence scores of the nurses according to demographic variables such as age, gender, marital status, having children. Thus, sociodemographic factors did not appear to be key factors, but some professional variables did. Higher total emotional intelligence scores were observed in those who had 10 years or longer experience, who found oneself successful in professional life, who stated that emotional intelligence is an improvable skill and who previously received self-improvement training. Interpersonal skills were higher in those with a graduate degree and in nurses working in polyclinics and paediatric units. These findings indicate which groups require improvement in emotional intelligence skills and which skills need improvement. Additionally, these results provide knowledge and create awareness about emotional intelligence skills of nurses and the distribution of these skills according to sociodemographic and professional variables. Implementation of emotional intelligence improvement programmes targeting the determined clinical nursing groups by nursing administrations can help the increase in

No cataclysmic variables missing: higher merger rate brings into agreement observed and predicted space densities

NASA Astrophysics Data System (ADS)

Belloni, Diogo; Schreiber, Matthias R.; Zorotovic, Mónica; Iłkiewicz, Krystian; Hurley, Jarrod R.; Giersz, Mirek; Lagos, Felipe

2018-06-01

The predicted and observed space density of cataclysmic variables (CVs) have been for a long time discrepant by at least an order of magnitude. The standard model of CV evolution predicts that the vast majority of CVs should be period bouncers, whose space density has been recently measured to be ρ ≲ 2 × 10-5 pc-3. We performed population synthesis of CVs using an updated version of the Binary Stellar Evolution (BSE) code for single and binary star evolution. We find that the recently suggested empirical prescription of consequential angular momentum loss (CAML) brings into agreement predicted and observed space densities of CVs and period bouncers. To progress with our understanding of CV evolution it is crucial to understand the physical mechanism behind empirical CAML. Our changes to the BSE code are also provided in details, which will allow the community to accurately model mass transfer in interacting binaries in which degenerate objects accrete from low-mass main-sequence donor stars.

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.

The protection motivation theory within the stages of the transtheoretical model - stage-specific interplay of variables and prediction of exercise stage transitions.

PubMed

Lippke, Sonia; Plotnikoff, Ronald C

2009-05-01

Two different theories of health behaviour have been chosen with the aim of theory integration: a continuous theory (protection motivation theory, PMT) and a stage model (transtheoretical model, TTM). This is the first study to test whether the stages of the TTM moderate the interrelation of PMT-variables and the mediation of motivation, as well as PMT-variables' interactions in predicting stage transitions. Hypotheses were tested regarding (1) mean patterns, stage pair-comparisons and nonlinear trends using ANOVAs; (2) prediction-patterns for the different stage groups employing multi-group structural equation modelling (MSEM) and nested model analyses; and (3) stage transitions using binary logistic regression analyses. Adults (N=1,602) were assessed over a 6 month period on their physical activity stages, PMT-variables and subsequent behaviour. (1) Particular mean differences and nonlinear trends in all test variables were found. (2) The PMT adequately fitted the five stage groups. The MSEM revealed that covariances within threat appraisal and coping appraisal were invariant and all other constrains were stage-specific, i.e. stage was a moderator. Except for self-efficacy, motivation fully mediated the relationship between the social-cognitive variables and behaviour. (3) Predicting stage transitions with the PMT-variables underscored the importance of self-efficacy. Only when threat appraisal and coping appraisal were high, stage movement was more likely in the preparation stage. Results emphasize stage-specific differences of the PMT mechanisms, and hence, support the stage construct. The findings may guide further theory building and research integrating different theoretical approaches.

Climate Modeling and Causal Identification for Sea Ice Predictability

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

Hunke, Elizabeth Clare; Urrego Blanco, Jorge Rolando; Urban, Nathan Mark

This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments inmore » which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.« less

Collaborative Proposal: Improving Decadal Prediction of Arctic Climate Variability and Change Using a Regional Arctic System Model (RASM)

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

Maslowski, Wieslaw

This project aims to develop, apply and evaluate a regional Arctic System model (RASM) for enhanced decadal predictions. Its overarching goal is to advance understanding of the past and present states of arctic climate and to facilitate improvements in seasonal to decadal predictions. In particular, it will focus on variability and long-term change of energy and freshwater flows through the arctic climate system. The project will also address modes of natural climate variability as well as extreme and rapid climate change in a region of the Earth that is: (i) a key indicator of the state of global climate throughmore » polar amplification and (ii) which is undergoing environmental transitions not seen in instrumental records. RASM will readily allow the addition of other earth system components, such as ecosystem or biochemistry models, thus allowing it to facilitate studies of climate impacts (e.g., droughts and fires) and of ecosystem adaptations to these impacts. As such, RASM is expected to become a foundation for more complete Arctic System models and part of a model hierarchy important for improving climate modeling and predictions.« less

Prediction of hypotension during spinal anesthesia for elective cesarean section by altered heart rate variability induced by postural change.

PubMed

Sakata, K; Yoshimura, N; Tanabe, K; Kito, K; Nagase, K; Iida, H

2017-02-01

Maternal hypotension is a common complication during cesarean section performed under spinal anesthesia. Changes in maternal heart rate with postural changes or values of heart rate variability have been reported to predict hypotension. Therefore, we hypothesized that changes in heart rate variability due to postural changes can predict hypotension. A total of 45 women scheduled to undergo cesarean section under spinal anesthesia were enrolled. A postural change test was performed the day before cesarean section. The ratio of the power of low and high frequency components contributing to heart rate variability was assessed in the order of supine, left lateral, and supine. Patients who exhibited a ⩾two-fold increase in the low-to-high frequency ratio when moving to supine from the lateral position were assigned to the postural change test-positive group. According to the findings of the postural change test, patients were assigned to the positive (n=22) and negative (n=23) groups, respectively. Hypotension occurred in 35/45 patients, of whom 21 (60%) were in the positive group and 14 (40%) were in the negative group. The incidence of hypotension was greater in the positive group (P<0.01). The total dose of ephedrine was greater in the positive group (15±11 vs. 7±7mg, P=0.005). The area under the receiver operating characteristic curve was 0.76 for the postural change test as a predictor of hypotension. The postural change test with heart rate variability analysis may be used to predict the risk of hypotension during spinal anesthesia for cesarean section. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predictability of North Atlantic Multidecadal Climate Variability

PubMed

Griffies; Bryan

1997-01-10

Atmospheric weather systems become unpredictable beyond a few weeks, but climate variations can be predictable over much longer periods because of the coupling of the ocean and atmosphere. With the use of a global coupled ocean-atmosphere model, it is shown that the North Atlantic may have climatic predictability on the order of a decade or longer. These results suggest that variations of the dominant multidecadal sea surface temperature patterns in the North Atlantic, which have been associated with changes in climate over Eurasia, can be predicted if an adequate and sustainable system for monitoring the Atlantic Ocean exists.

Technical Report Series on Global Modeling and Data Assimilation. Volume 13; Interannual Variability and Potential Predictability in Reanalysis Products

NASA Technical Reports Server (NTRS)

Min, Wei; Schubert, Siegfried D.; Suarez, Max J. (Editor)

1997-01-01

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables.

Developing models to predict 8th grade students' achievement levels on timss science based on opportunity-to-learn variables

NASA Astrophysics Data System (ADS)

Whitford, Melinda M.

Science educational reforms have placed major emphasis on improving science classroom instruction and it is therefore vital to study opportunity-to-learn (OTL) variables related to student science learning experiences and teacher teaching practices. This study will identify relationships between OTL and student science achievement and will identify OTL predictors of students' attainment at various distinct achievement levels (low/intermediate/high/advanced). Specifically, the study (a) address limitations of previous studies by examining a large number of independent and control variables that may impact students' science achievement and (b) it will test hypotheses of structural relations to how the identified predictors and mediating factors impact on student achievement levels. The study will follow a multi-stage and integrated bottom-up and top-down approach to identify predictors of students' achievement levels on standardized tests using TIMSS 2011 dataset. Data mining or pattern recognition, a bottom-up approach will identify the most prevalent association patterns between different student achievement levels and variables related to student science learning experiences, teacher teaching practices and home and school environments. The second stage is a top-down approach, testing structural equation models of relations between the significant predictors and students' achievement levels according.

Effective prediction of biodiversity in tidal flat habitats using an artificial neural network.

PubMed

Yoo, Jae-Won; Lee, Yong-Woo; Lee, Chang-Gun; Kim, Chang-Soo

2013-02-01

Accurate predictions of benthic macrofaunal biodiversity greatly benefit the efficient planning and management of habitat restoration efforts in tidal flat habitats. Artificial neural network (ANN) prediction models for such biodiversity were developed and tested based on 13 biophysical variables, collected from 50 sites of tidal flats along the coast of Korea during 1991-2006. The developed model showed high predictions during training, cross-validation and testing. Besides the training and testing procedures, an independent dataset from a different time period (2007-2010) was used to test the robustness and practical usage of the model. High prediction on the independent dataset (r = 0.84) validated the networks proper learning of predictive relationship and its generality. Key influential variables identified by follow-up sensitivity analyses were related with topographic dimension, environmental heterogeneity, and water column properties. Study demonstrates the successful application of ANN for the accurate prediction of benthic macrofaunal biodiversity and understanding of dynamics of candidate variables. Copyright © 2012 Elsevier Ltd. All rights reserved.

Variability and prediction of freshwater and nitrate fluxes for the Louisiana-Texas shelf: Mississippi and Atchafalaya River source functions

USGS Publications Warehouse

Bratkovich, A.; Dinnel, S.P.; Goolsby, D.A.

1994-01-01

Time histories of riverine water discharge, nitrate concentration, and nitrate, flux have been analyzed for the Mississippi and Atchafalaya rivers. Results indicate that water discharge variability is dominated by the annual cycle and shorter-time-scale episodic events presumably associated with snowmelt runoff and spring or summer rains. Interannual variability in water discharge is relatively small compared to the above. In contrast, nitrate concentration exhibits strongest variability at decadal time scales. The interannual variability is not monotonic but more complicated in structure. Weak covariability between water discharge and nitrate concentration leads to a relatively “noisy” nitrate flux signal. Nitrate flux variations exhibit a low-amplitude, long-term modulation of a dominant annual cycle. Predictor-hindcastor analyses indicate that skilled forecasts of nitrate concentration and nitrate flux fields are feasible. Water discharge was the most reliably hindcast (on seasonal to interannual time scales) due to the fundamental strength of the annual hydrologic cycle. However, the forecasting effort for this variable was less successful than the hindcasting effort, mostly due to a phase shift in the annual cycle during our relatively short test period (18 mo). Nitrate concentration was more skillfully predicted (seasonal to interannual time scales) due to the relative dominance of the decadal-scale portion of the signal. Nitrate flux was also skillfully forecast even though historical analyses seemed to indicate that it should be more difficult to predict than either water discharge or nitrate concentration.

Variability in the Propagation Phase of CFD-Based Noise Prediction: Summary of Results From Category 8 of the BANC-III Workshop

NASA Technical Reports Server (NTRS)

Lopes, Leonard; Redonnet, Stephane; Imamura, Taro; Ikeda, Tomoaki; Zawodny, Nikolas; Cunha, Guilherme

2015-01-01

The usage of Computational Fluid Dynamics (CFD) in noise prediction typically has been a two part process: accurately predicting the flow conditions in the near-field and then propagating the noise from the near-field to the observer. Due to the increase in computing power and the cost benefit when weighed against wind tunnel testing, the usage of CFD to estimate the local flow field of complex geometrical structures has become more routine. Recently, the Benchmark problems in Airframe Noise Computation (BANC) workshops have provided a community focus on accurately simulating the local flow field near the body with various CFD approaches. However, to date, little effort has been given into assessing the impact of the propagation phase of noise prediction. This paper includes results from the BANC-III workshop which explores variability in the propagation phase of CFD-based noise prediction. This includes two test cases: an analytical solution of a quadrupole source near a sphere and a computational solution around a nose landing gear. Agreement between three codes was very good for the analytic test case, but CFD-based noise predictions indicate that the propagation phase can introduce 3dB or more of variability in noise predictions.

Design study and performance analysis of a high-speed multistage variable-geometry fan for a variable cycle engine

NASA Technical Reports Server (NTRS)

Sullivan, T. J.; Parker, D. E.

1979-01-01

A design technology study was performed to identify a high speed, multistage, variable geometry fan configuration capable of achieving wide flow modulation with near optimum efficiency at the important operating condition. A parametric screening study of the front and rear block fans was conducted in which the influence of major fan design features on weight and efficiency was determined. Key design parameters were varied systematically to determine the fan configuration most suited for a double bypass, variable cycle engine. Two and three stage fans were considered for the front block. A single stage, core driven fan was studied for the rear block. Variable geometry concepts were evaluated to provide near optimum off design performance. A detailed aerodynamic design and a preliminary mechanical design were carried out for the selected fan configuration. Performance predictions were made for the front and rear block fans.

Intraindividual variability in executive functions but not speed of processing or conflict resolution predicts performance differences in gait speed in older adults.

PubMed

Holtzer, Roee; Mahoney, Jeannette; Verghese, Joe

2014-08-01

The relationship between executive functions (EF) and gait speed is well established. However, with the exception of dual tasking, the key components of EF that predict differences in gait performance have not been determined. Therefore, the current study was designed to determine whether processing speed, conflict resolution, and intraindividual variability in EF predicted variance in gait performance in single- and dual-task conditions. Participants were 234 nondemented older adults (mean age 76.48 years; 55% women) enrolled in a community-based cohort study. Gait speed was assessed using an instrumented walkway during single- and dual-task conditions. The flanker task was used to assess EF. Results from the linear mixed effects model showed that (a) dual-task interference caused a significant dual-task cost in gait speed (estimate = 35.99; 95% CI = 33.19-38.80) and (b) of the cognitive predictors, only intraindividual variability was associated with gait speed (estimate = -.606; 95% CI = -1.11 to -.10). In unadjusted analyses, the three EF measures were related to gait speed in single- and dual-task conditions. However, in fully adjusted linear regression analysis, only intraindividual variability predicted performance differences in gait speed during dual tasking (B = -.901; 95% CI = -1.557 to -.245). Among the three EF measures assessed, intraindividual variability but not speed of processing or conflict resolution predicted performance differences in gait speed. © The Author 2013. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Use of NMR and NMR Prediction Software to Identify Components in Red Bull Energy Drinks

ERIC Educational Resources Information Center

Simpson, Andre J.; Shirzadi, Azadeh; Burrow, Timothy E.; Dicks, Andrew P.; Lefebvre, Brent; Corrin, Tricia

2009-01-01

A laboratory experiment designed as part of an upper-level undergraduate analytical chemistry course is described. Students investigate two popular soft drinks (Red Bull Energy Drink and sugar-free Red Bull Energy Drink) by NMR spectroscopy. With assistance of modern NMR prediction software they identify and quantify major components in each…

Comparison of Predicted Thermoelectric Energy Conversion Efficiency by Cumulative Properties and Reduced Variables Approaches

NASA Astrophysics Data System (ADS)

Linker, Thomas M.; Lee, Glenn S.; Beekman, Matt

2018-06-01

The semi-analytical methods of thermoelectric energy conversion efficiency calculation based on the cumulative properties approach and reduced variables approach are compared for 21 high performance thermoelectric materials. Both approaches account for the temperature dependence of the material properties as well as the Thomson effect, thus the predicted conversion efficiencies are generally lower than that based on the conventional thermoelectric figure of merit ZT for nearly all of the materials evaluated. The two methods also predict material energy conversion efficiencies that are in very good agreement which each other, even for large temperature differences (average percent difference of 4% with maximum observed deviation of 11%). The tradeoff between obtaining a reliable assessment of a material's potential for thermoelectric applications and the complexity of implementation of the three models, as well as the advantages of using more accurate modeling approaches in evaluating new thermoelectric materials, are highlighted.

When to simulate and when to associate? Accounting for inter-talker variability in the speech signal.

PubMed

Trude, Alison M

2013-08-01

Pickering & Garrod's (P&G's) theory could be modified to describe how listeners rapidly incorporate context to generate predictions about speech despite inter-talker variability. However, in order to do so, the content of "impoverished" predicted percepts must be expanded to include phonetic information. Further, the way listeners identify and represent inter-talker differences and subsequently determine which prediction method to use would require further specification.

Applying a Physics-Based Description of Fatigue Variability Behavior to Probabilistic Life Prediction (Preprint)

DTIC Science & Technology

2007-07-01

predicted at lower stress levels as illustrated for the α+β titanium alloy, Ti-6Al-2Sn-4Zr-6Mo (Ti- 6 -2- 4 - 6 ), in Fig. 2((a) and (b)) [7, 8]. The...variability by a bimodal probability density representing the superposition of these dual mechanisms with reference to the α+β titanium alloy, Ti- 6 -2- 4 - 6 ...these trends. 7 The alternate description is illustrated with respect to the α+β titanium alloy, Ti- 6 -2- 4 - 6 . The stress vs. total lifetime behavior

Identifying and Tracking Pedestrians Based on Sensor Fusion and Motion Stability Predictions

PubMed Central

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Mª; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle. PMID:22163639

Identifying and tracking pedestrians based on sensor fusion and motion stability predictions.

PubMed

Musleh, Basam; García, Fernando; Otamendi, Javier; Armingol, José Maria; de la Escalera, Arturo

2010-01-01

The lack of trustworthy sensors makes development of Advanced Driver Assistance System (ADAS) applications a tough task. It is necessary to develop intelligent systems by combining reliable sensors and real-time algorithms to send the proper, accurate messages to the drivers. In this article, an application to detect and predict the movement of pedestrians in order to prevent an imminent collision has been developed and tested under real conditions. The proposed application, first, accurately measures the position of obstacles using a two-sensor hybrid fusion approach: a stereo camera vision system and a laser scanner. Second, it correctly identifies pedestrians using intelligent algorithms based on polylines and pattern recognition related to leg positions (laser subsystem) and dense disparity maps and u-v disparity (vision subsystem). Third, it uses statistical validation gates and confidence regions to track the pedestrian within the detection zones of the sensors and predict their position in the upcoming frames. The intelligent sensor application has been experimentally tested with success while tracking pedestrians that cross and move in zigzag fashion in front of a vehicle.

Predicting word-recognition performance in noise by young listeners with normal hearing using acoustic, phonetic, and lexical variables.

PubMed

McArdle, Rachel; Wilson, Richard H

2008-06-01

To analyze the 50% correct recognition data that were from the Wilson et al (this issue) study and that were obtained from 24 listeners with normal hearing; also to examine whether acoustic, phonetic, or lexical variables can predict recognition performance for monosyllabic words presented in speech-spectrum noise. The specific variables are as follows: (a) acoustic variables (i.e., effective root-mean-square sound pressure level, duration), (b) phonetic variables (i.e., consonant features such as manner, place, and voicing for initial and final phonemes; vowel phonemes), and (c) lexical variables (i.e., word frequency, word familiarity, neighborhood density, neighborhood frequency). The descriptive, correlational study will examine the influence of acoustic, phonetic, and lexical variables on speech recognition in noise performance. Regression analysis demonstrated that 45% of the variance in the 50% point was accounted for by acoustic and phonetic variables whereas only 3% of the variance was accounted for by lexical variables. These findings suggest that monosyllabic word-recognition-in-noise is more dependent on bottom-up processing than on top-down processing. The results suggest that when speech-in-noise testing is used in a pre- and post-hearing-aid-fitting format, the use of monosyllabic words may be sensitive to changes in audibility resulting from amplification.

Automatically Identifying and Predicting Unplanned Wind Turbine Stoppages Using SCADA and Alarms System Data: Case Study and Results

NASA Astrophysics Data System (ADS)

Leahy, Kevin; Gallagher, Colm; Bruton, Ken; O'Donovan, Peter; O'Sullivan, Dominic T. J.

2017-11-01

Using 10-minute wind turbine SCADA data for fault prediction offers an attractive way of gaining additional prognostic capabilities without needing to invest in extra hardware. To use these data-driven methods effectively, the historical SCADA data must be labelled with the periods when the turbine was in faulty operation as well the sub-system the fault was attributed to. Manually identifying faults using maintenance logs can be effective, but is also highly time consuming and tedious due to the disparate nature of these logs across manufacturers, operators and even individual maintenance events. Turbine alarm systems can help to identify these periods, but the sheer volume of alarms and false positives generated makes analysing them on an individual basis ineffective. In this work, we present a new method for automatically identifying historical stoppages on the turbine using SCADA and alarms data. Each stoppage is associated with either a fault in one of the turbine’s sub-systems, a routine maintenance activity, a grid-related event or a number of other categories. This is then checked against maintenance logs for accuracy and the labelled data fed into a classifier for predicting when these stoppages will occur. Results show that the automated labelling process correctly identifies each type of stoppage, and can be effectively used for SCADA-based prediction of turbine faults.

Physical attraction to reliable, low variability nervous systems: Reaction time variability predicts attractiveness.

PubMed

Butler, Emily E; Saville, Christopher W N; Ward, Robert; Ramsey, Richard

2017-01-01

The human face cues a range of important fitness information, which guides mate selection towards desirable others. Given humans' high investment in the central nervous system (CNS), cues to CNS function should be especially important in social selection. We tested if facial attractiveness preferences are sensitive to the reliability of human nervous system function. Several decades of research suggest an operational measure for CNS reliability is reaction time variability, which is measured by standard deviation of reaction times across trials. Across two experiments, we show that low reaction time variability is associated with facial attractiveness. Moreover, variability in performance made a unique contribution to attractiveness judgements above and beyond both physical health and sex-typicality judgements, which have previously been associated with perceptions of attractiveness. In a third experiment, we empirically estimated the distribution of attractiveness preferences expected by chance and show that the size and direction of our results in Experiments 1 and 2 are statistically unlikely without reference to reaction time variability. We conclude that an operating characteristic of the human nervous system, reliability of information processing, is signalled to others through facial appearance. Copyright © 2016 Elsevier B.V. All rights reserved.

Perinatal Medical Variables Predict Executive Function within a Sample of Preschoolers Born Very Low Birth Weight

PubMed Central

Duvall, Susanne W.; Erickson, Sarah J.; MacLean, Peggy; Lowe, Jean R.

2014-01-01

The goal was to identify perinatal predictors of early executive dysfunction in preschoolers born very low birth weight. Fifty-seven preschoolers completed three executive function tasks (Dimensional Change Card Sort-Separated (inhibition, working memory and cognitive flexibility), Bear Dragon (inhibition and working memory) and Gift Delay Open (inhibition)). Relationships between executive function and perinatal medical severity factors (gestational age, days on ventilation, size for gestational age, maternal steroids and number of surgeries), and chronological age were investigated by multiple linear regression and logistic regression. Different perinatal medical severity factors were predictive of executive function tasks, with gestational age predicting Bear Dragon and Gift Open; and number of surgeries and maternal steroids predicting performance on Dimensional Change Card Sort-Separated. By understanding the relationship between perinatal medical severity factors and preschool executive outcomes, we may be able to identify children at highest risk for future executive dysfunction, thereby focusing targeted early intervention services. PMID:25117418

Analysis of model development strategies: predicting ventral hernia recurrence.

PubMed

Holihan, Julie L; Li, Linda T; Askenasy, Erik P; Greenberg, Jacob A; Keith, Jerrod N; Martindale, Robert G; Roth, J Scott; Liang, Mike K

2016-11-01

There have been many attempts to identify variables associated with ventral hernia recurrence; however, it is unclear which statistical modeling approach results in models with greatest internal and external validity. We aim to assess the predictive accuracy of models developed using five common variable selection strategies to determine variables associated with hernia recurrence. Two multicenter ventral hernia databases were used. Database 1 was randomly split into "development" and "internal validation" cohorts. Database 2 was designated "external validation". The dependent variable for model development was hernia recurrence. Five variable selection strategies were used: (1) "clinical"-variables considered clinically relevant, (2) "selective stepwise"-all variables with a P value <0.20 were assessed in a step-backward model, (3) "liberal stepwise"-all variables were included and step-backward regression was performed, (4) "restrictive internal resampling," and (5) "liberal internal resampling." Variables were included with P < 0.05 for the Restrictive model and P < 0.10 for the Liberal model. A time-to-event analysis using Cox regression was performed using these strategies. The predictive accuracy of the developed models was tested on the internal and external validation cohorts using Harrell's C-statistic where C > 0.70 was considered "reasonable". The recurrence rate was 32.9% (n = 173/526; median/range follow-up, 20/1-58 mo) for the development cohort, 36.0% (n = 95/264, median/range follow-up 20/1-61 mo) for the internal validation cohort, and 12.7% (n = 155/1224, median/range follow-up 9/1-50 mo) for the external validation cohort. Internal validation demonstrated reasonable predictive accuracy (C-statistics = 0.772, 0.760, 0.767, 0.757, 0.763), while on external validation, predictive accuracy dipped precipitously (C-statistic = 0.561, 0.557, 0.562, 0.553, 0.560). Predictive accuracy was equally adequate on internal validation among

Kindergarten Students' Levels of Understanding Some Science Concepts and Scientific Inquiry Processes According to Demographic Variables (The Sampling of Kilis Province in Turkey)

ERIC Educational Resources Information Center

Ilhan, Nail; Tosun, Cemal

2016-01-01

The purpose of this study is to identify the kindergarten students' levels of understanding some science concepts (LUSSC) and scientific inquiry processes (SIP) and compare their LUSSC and SIP in terms of some demographic variables. Also, another purpose of this study is to identify the predictive power of those demographic variables over the…

Predicting Career Choice in College Women: Empirical Test of a Theory-Based Model.

ERIC Educational Resources Information Center

Eisler, Terri A.; Iverson, Barbara

While investigations of the impact of parental factors on children's career choices have identified variables that appear predictive of career choice in males, variables which influence career choice in females are less well documented. This study used social learning theory as a framework for examining the impact of parental reinforcement,…

Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

NASA Astrophysics Data System (ADS)

Rohman, Muhamad Nur; Hidayat, Mas Irfan P.; Purniawan, Agung

2018-04-01

Neural networks (NN) have been widely used in application of fatigue life prediction. In the use of fatigue life prediction for polymeric-base composite, development of NN model is necessary with respect to the limited fatigue data and applicable to be used to predict the fatigue life under varying stress amplitudes in the different stress ratios. In the present paper, Multilayer-Perceptrons (MLP) model of neural network is developed, and Genetic Algorithm was employed to optimize the respective weights of NN for prediction of polymeric-base composite materials under variable amplitude loading. From the simulation result obtained with two different composite systems, named E-glass fabrics/epoxy (layups [(±45)/(0)2]S), and E-glass/polyester (layups [90/0/±45/0]S), NN model were trained with fatigue data from two different stress ratios, which represent limited fatigue data, can be used to predict another four and seven stress ratios respectively, with high accuracy of fatigue life prediction. The accuracy of NN prediction were quantified with the small value of mean square error (MSE). When using 33% from the total fatigue data for training, the NN model able to produce high accuracy for all stress ratios. When using less fatigue data during training (22% from the total fatigue data), the NN model still able to produce high coefficient of determination between the prediction result compared with obtained by experiment.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection.

PubMed

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks.

Spatiotemporal Context Awareness for Urban Traffic Modeling and Prediction: Sparse Representation Based Variable Selection

PubMed Central

Yang, Su; Shi, Shixiong; Hu, Xiaobing; Wang, Minjie

2015-01-01

Spatial-temporal correlations among the data play an important role in traffic flow prediction. Correspondingly, traffic modeling and prediction based on big data analytics emerges due to the city-scale interactions among traffic flows. A new methodology based on sparse representation is proposed to reveal the spatial-temporal dependencies among traffic flows so as to simplify the correlations among traffic data for the prediction task at a given sensor. Three important findings are observed in the experiments: (1) Only traffic flows immediately prior to the present time affect the formation of current traffic flows, which implies the possibility to reduce the traditional high-order predictors into an 1-order model. (2) The spatial context relevant to a given prediction task is more complex than what is assumed to exist locally and can spread out to the whole city. (3) The spatial context varies with the target sensor undergoing prediction and enlarges with the increment of time lag for prediction. Because the scope of human mobility is subject to travel time, identifying the varying spatial context against time lag is crucial for prediction. Since sparse representation can capture the varying spatial context to adapt to the prediction task, it outperforms the traditional methods the inputs of which are confined as the data from a fixed number of nearby sensors. As the spatial-temporal context for any prediction task is fully detected from the traffic data in an automated manner, where no additional information regarding network topology is needed, it has good scalability to be applicable to large-scale networks. PMID:26496370

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

PubMed

Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

2015-08-01

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting climate effects on Pacific sardine

PubMed Central

Deyle, Ethan R.; Fogarty, Michael; Hsieh, Chih-hao; Kaufman, Les; MacCall, Alec D.; Munch, Stephan B.; Perretti, Charles T.; Ye, Hao; Sugihara, George

2013-01-01

For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine. PMID:23536299

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.

Meditation-induced changes in high-frequency heart rate variability predict smoking outcomes

PubMed Central

Libby, Daniel J.; Worhunsky, Patrick D.; Pilver, Corey E.; Brewer, Judson A.

2012-01-01

Background: High-frequency heart rate variability (HF-HRV) is a measure of parasympathetic nervous system (PNS) output that has been associated with enhanced self-regulation. Low resting levels of HF-HRV are associated with nicotine dependence and blunted stress-related changes in HF-HRV are associated with decreased ability to resist smoking. Meditation has been shown to increase HF-HRV. However, it is unknown whether tonic levels of HF-HRV or acute changes in HF-HRV during meditation predict treatment responses in addictive behaviors such as smoking cessation. Purpose: To investigate the relationship between HF-HRV and subsequent smoking outcomes. Methods: HF-HRV during resting baseline and during mindfulness meditation was measured within two weeks of completing a 4-week smoking cessation intervention in a sample of 31 community participants. Self-report measures of smoking were obtained at a follow up 17-weeks after the initiation of treatment. Results: Regression analyses indicated that individuals exhibiting acute increases in HF-HRV from resting baseline to meditation smoked fewer cigarettes at follow-up than those who exhibited acute decreases in HF-HRV (b = −4.89, p = 0.008). Conclusion: Acute changes in HF-HRV in response to meditation may be a useful tool to predict smoking cessation treatment response. PMID:22457646

Within-and among-year germination in Sonoran Desert winter annuals: bet hedging and predictive germination in a variable environment.

PubMed

Gremer, Jennifer R; Kimball, Sarah; Venable, D Lawrence

2016-10-01

In variable environments, organisms must have strategies to ensure fitness as conditions change. For plants, germination can time emergence with favourable conditions for later growth and reproduction (predictive germination), spread the risk of unfavourable conditions (bet hedging) or both (integrated strategies). Here we explored the adaptive value of within- and among-year germination timing for 12 species of Sonoran Desert winter annual plants. We parameterised models with long-term demographic data to predict optimal germination fractions and compared them to observed germination. At both temporal scales we found that bet hedging is beneficial and that predicted optimal strategies corresponded well with observed germination. We also found substantial fitness benefits to varying germination timing, suggesting some degree of predictive germination in nature. However, predictive germination was imperfect, calling for some degree of bet hedging. Together, our results suggest that desert winter annuals have integrated strategies combining both predictive plasticity and bet hedging. © 2016 John Wiley & Sons Ltd/CNRS.

Prediction of chronic post-operative pain: pre-operative DNIC testing identifies patients at risk.

PubMed

Yarnitsky, David; Crispel, Yonathan; Eisenberg, Elon; Granovsky, Yelena; Ben-Nun, Alon; Sprecher, Elliot; Best, Lael-Anson; Granot, Michal

2008-08-15

Surgical and medical procedures, mainly those associated with nerve injuries, may lead to chronic persistent pain. Currently, one cannot predict which patients undergoing such procedures are 'at risk' to develop chronic pain. We hypothesized that the endogenous analgesia system is key to determining the pattern of handling noxious events, and therefore testing diffuse noxious inhibitory control (DNIC) will predict susceptibility to develop chronic post-thoracotomy pain (CPTP). Pre-operative psychophysical tests, including DNIC assessment (pain reduction during exposure to another noxious stimulus at remote body area), were conducted in 62 patients, who were followed 29.0+/-16.9 weeks after thoracotomy. Logistic regression revealed that pre-operatively assessed DNIC efficiency and acute post-operative pain intensity were two independent predictors for CPTP. Efficient DNIC predicted lower risk of CPTP, with OR 0.52 (0.33-0.77 95% CI, p=0.0024), i.e., a 10-point numerical pain scale (NPS) reduction halves the chance to develop chronic pain. Higher acute pain intensity indicated OR of 1.80 (1.28-2.77, p=0.0024) predicting nearly a double chance to develop chronic pain for each 10-point increase. The other psychophysical measures, pain thresholds and supra-threshold pain magnitudes, did not predict CPTP. For prediction of acute post-operative pain intensity, DNIC efficiency was not found significant. Effectiveness of the endogenous analgesia system obtained at a pain-free state, therefore, seems to reflect the individual's ability to tackle noxious events, identifying patients 'at risk' to develop post-intervention chronic pain. Applying this diagnostic approach before procedures that might generate pain may allow individually tailored pain prevention and management, which may substantially reduce suffering.

A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

PubMed Central

Musungu, Bryan; Bhatnagar, Deepak; Brown, Robert L.; Fakhoury, Ahmad M.; Geisler, Matt

2015-01-01

Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize. PMID:26089837

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

USGS Publications Warehouse

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

2013-01-01

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

Authentic early experience in Medical Education: a socio-cultural analysis identifying important variables in learning interactions within workplaces.

PubMed

Yardley, Sarah; Brosnan, Caragh; Richardson, Jane; Hays, Richard

2013-12-01

This paper addresses the question 'what are the variables influencing social interactions and learning during Authentic Early Experience (AEE)?' AEE is a complex educational intervention for new medical students. Following critique of the existing literature, multiple qualitative methods were used to create a study framework conceptually orientated to a socio-cultural perspective. Study participants were recruited from three groups at one UK medical school: students, workplace supervisors, and medical school faculty. A series of intersecting spectra identified in the data describe dyadic variables that make explicit the parameters within which social interactions are conducted in this setting. Four of the spectra describe social processes related to being in workplaces and developing the ability to manage interactions during authentic early experiences. These are: (1) legitimacy expressed through invited participation or exclusion; (2) finding a role-a spectrum from student identity to doctor mindset; (3) personal perspectives and discomfort in transition from lay to medical; and, (4) taking responsibility for 'risk'-moving from aversion to management through graded progression of responsibility. Four further spectra describe educational consequences of social interactions. These spectra identify how the reality of learning is shaped through social interactions and are (1) generic-specific objectives, (2) parallel-integrated-learning, (3) context specific-transferable learning and (4) performing or simulating-reality. Attention to these variables is important if educators are to maximise constructive learning from AEE. Application of each of the spectra could assist workplace supervisors to maximise the positive learning potential of specific workplaces.

Predicting presidential performance in the United States: equation replication on recent survey results.

PubMed

Simonton, D K

2001-06-01

For more than 2 decades, researchers have tried to identify the variables that predict the overall performance of U.S. presidents. In 1986, there emerged a 6-variable prediction equation (D. K. Simonton, 1986c, 1987b) that has been replicated repeatedly. The predictors are years in office, war years, scandal, assassination, heroism in war, and intellectual brilliance. The author again replicated the equation on recent rankings of all presidents from George Washington through William Jefferson Clinton according to a survey of 719 experts (W. R. Ridings, Jr., & S. B. McIver, 1997). The original 6-variable equation successfully predicted both the overall rankings as well as the 5 core components of the rankings (leadership qualities, accomplishment, political skill, appointments, character and integrity). The predictive value of the equation was illustrated for the presidencies of Ronald W. Reagan, George H. W. Bush, and Clinton.

Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability

NASA Astrophysics Data System (ADS)

Liu, Sijun; Chen, Jiaping; Wang, Jianming; Wu, Zhuchao; Wu, Weihua; Xu, Zhiwei; Hu, Wenbiao; Xu, Fei; Tong, Shilu; Shen, Hongbing

2017-10-01

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.

Identifying fish diversity hot-spots in data-poor situations.

PubMed

Fonseca, Vinícius Prado; Pennino, Maria Grazia; de Nóbrega, Marcelo Francisco; Oliveira, Jorge Eduardo Lins; de Figueiredo Mendes, Liana

2017-08-01

One of the more challenging tasks in Marine Spatial Planning (MSP) is identifying critical areas for management and conservation of fish stocks. However, this objective is difficult to achieve in data-poor situations with different sources of uncertainty. In the present study we propose a combination of hierarchical Bayesian spatial models and remotely sensed estimates of environmental variables to be used as flexible and reliable statistical tools to identify and map fish species richness and abundance hot-spots. Results show higher species aggregates in areas with higher sea floor rugosity and habitat complexity, and identify clear richness hot-spots. Our findings identify sensitive habitats through essential and easy-to-use interpretation tools, such as predictive maps, which can contribute to improving management and operability of the studied data-poor situations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Examining the causes of memory strength variability: Recollection, attention failure, or encoding variability?

PubMed Central

Koen, Joshua D.; Aly, Mariam; Wang, Wei-Chun; Yonelinas, Andrew P.

2013-01-01

A prominent finding in recognition memory is that studied items are associated with more variability in memory strength than new items. Here, we test three competing theories for why this occurs - the encoding variability, attention failure, and recollection accounts. Distinguishing amongst these theories is critical because each provides a fundamentally different account of the processes underlying recognition memory. The encoding variability and attention failure accounts propose that old item variance will be unaffected by retrieval manipulations because the processes producing this effect are ascribed to encoding. The recollection account predicts that both encoding and retrieval manipulations that preferentially affect recollection will affect memory variability. These contrasting predictions were tested by examining the effect of response speeding (Experiment 1), dividing attention at retrieval (Experiment 2), context reinstatement (Experiment 3), and increased test delay (Experiment 4) on recognition performance. The results of all four experiments confirmed the predictions of the recollection account, and were inconsistent with the encoding variability account. The evidence supporting the attention failure account was mixed, with two of the four experiments confirming the account and two disconfirming the account. These results indicate that encoding variability and attention failure are insufficient accounts of memory variance, and provide support for the recollection account. Several alternative theoretical accounts of the results are also considered. PMID:23834057

Identifying public water facilities with low spatial variability of disinfection by-products for epidemiological investigations

PubMed Central

Hinckley, A; Bachand, A; Nuckols, J; Reif, J

2005-01-01

Background and Aims: Epidemiological studies of disinfection by-products (DBPs) and reproductive outcomes have been hampered by misclassification of exposure. In most epidemiological studies conducted to date, all persons living within the boundaries of a water distribution system have been assigned a common exposure value based on facility-wide averages of trihalomethane (THM) concentrations. Since THMs do not develop uniformly throughout a distribution system, assignment of facility-wide averages may be inappropriate. One approach to mitigate this potential for misclassification is to select communities for epidemiological investigations that are served by distribution systems with consistently low spatial variability of THMs. Methods and Results: A feasibility study was conducted to develop methods for community selection using the Information Collection Rule (ICR) database, assembled by the US Environmental Protection Agency. The ICR database contains quarterly DBP concentrations collected between 1997 and 1998 from the distribution systems of 198 public water facilities with minimum service populations of 100 000 persons. Facilities with low spatial variation of THMs were identified using two methods; 33 facilities were found with low spatial variability based on one or both methods. Because brominated THMs may be important predictors of risk for adverse reproductive outcomes, sites were categorised into three exposure profiles according to proportion of brominated THM species and average TTHM concentration. The correlation between THMs and haloacetic acids (HAAs) in these facilities was evaluated to see whether selection by total trihalomethanes (TTHMs) corresponds to low spatial variability for HAAs. TTHMs were only moderately correlated with HAAs (r = 0.623). Conclusions: Results provide a simple method for a priori selection of sites with low spatial variability from state or national public water facility datasets as a means to reduce exposure

Characterizing Uncertainty and Variability in PBPK Models ...

EPA Pesticide Factsheets

Mode-of-action based risk and safety assessments can rely upon tissue dosimetry estimates in animals and humans obtained from physiologically-based pharmacokinetic (PBPK) modeling. However, risk assessment also increasingly requires characterization of uncertainty and variability; such characterization for PBPK model predictions represents a continuing challenge to both modelers and users. Current practices show significant progress in specifying deterministic biological models and the non-deterministic (often statistical) models, estimating their parameters using diverse data sets from multiple sources, and using them to make predictions and characterize uncertainty and variability. The International Workshop on Uncertainty and Variability in PBPK Models, held Oct 31-Nov 2, 2006, sought to identify the state-of-the-science in this area and recommend priorities for research and changes in practice and implementation. For the short term, these include: (1) multidisciplinary teams to integrate deterministic and non-deterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through more complete documentation of the model structure(s) and parameter values, the results of sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include: (1) theoretic and practical methodological impro

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants.

PubMed

Mueller, Martina; Wagner, Carol L; Annibale, David J; Knapp, Rebecca G; Hulsey, Thomas C; Almeida, Jonas S

2006-03-01

Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data

Do drug treatment variables predict cognitive performance in multidrug-treated opioid-dependent patients? A regression analysis study

PubMed Central

2012-01-01

Background Cognitive deficits and multiple psychoactive drug regimens are both common in patients treated for opioid-dependence. Therefore, we examined whether the cognitive performance of patients in opioid-substitution treatment (OST) is associated with their drug treatment variables. Methods Opioid-dependent patients (N = 104) who were treated either with buprenorphine or methadone (n = 52 in both groups) were given attention, working memory, verbal, and visual memory tests after they had been a minimum of six months in treatment. Group-wise results were analysed by analysis of variance. Predictors of cognitive performance were examined by hierarchical regression analysis. Results Buprenorphine-treated patients performed statistically significantly better in a simple reaction time test than methadone-treated ones. No other significant differences between groups in cognitive performance were found. In each OST drug group, approximately 10% of the attention performance could be predicted by drug treatment variables. Use of benzodiazepine medication predicted about 10% of performance variance in working memory. Treatment with more than one other psychoactive drug (than opioid or BZD) and frequent substance abuse during the past month predicted about 20% of verbal memory performance. Conclusions Although this study does not prove a causal relationship between multiple prescription drug use and poor cognitive functioning, the results are relevant for psychosocial recovery, vocational rehabilitation, and psychological treatment of OST patients. Especially for patients with BZD treatment, other treatment options should be actively sought. PMID:23121989

Deep learning for predicting the monsoon over the homogeneous regions of India

NASA Astrophysics Data System (ADS)

Saha, Moumita; Mitra, Pabitra; Nanjundiah, Ravi S.

2017-06-01

Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, north-east, north-west, and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.

Powder diffraction and crystal structure prediction identify four new coumarin polymorphs

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

Shtukenberg, Alexander G.; Zhu, Qiang; Carter, Damien J.

Coumarin, a simple, commodity chemical isolated from beans in 1820, has, to date, only yielded one solid state structure. Here, we report a rich polymorphism of coumarin grown from the melt. Four new metastable forms were identified and their crystal structures were solved using a combination of computational crystal structure prediction algorithms and X-ray powder diffraction. With five crystal structures, coumarin has become one of the few rigid molecules showing extensive polymorphism at ambient conditions. We demonstrate the crucial role of advanced electronic structure calculations including many-body dispersion effects for accurate ranking of the stability of coumarin polymorphs and themore » need to account for anharmonic vibrational contributions to their free energy. As such, coumarin is a model system for studying weak intermolecular interactions, crystallization mechanisms, and kinetic effects.« less

Powder diffraction and crystal structure prediction identify four new coumarin polymorphs

DOE PAGES

Shtukenberg, Alexander G.; Zhu, Qiang; Carter, Damien J.; ...

2017-05-15

Coumarin, a simple, commodity chemical isolated from beans in 1820, has, to date, only yielded one solid state structure. Here, we report a rich polymorphism of coumarin grown from the melt. Four new metastable forms were identified and their crystal structures were solved using a combination of computational crystal structure prediction algorithms and X-ray powder diffraction. With five crystal structures, coumarin has become one of the few rigid molecules showing extensive polymorphism at ambient conditions. We demonstrate the crucial role of advanced electronic structure calculations including many-body dispersion effects for accurate ranking of the stability of coumarin polymorphs and themore » need to account for anharmonic vibrational contributions to their free energy. As such, coumarin is a model system for studying weak intermolecular interactions, crystallization mechanisms, and kinetic effects.« less

A Multi-sensor Approach to Identify Crop Sensitivity Related to Climate Variability in Central India

NASA Astrophysics Data System (ADS)

Mondal, P.; DeFries, R. S.; Jain, M.; Robertson, A. W.; Galford, G. L.; Small, C.

2012-12-01

Agriculture is a primary source of livelihood for over 70% of India's population, with staple crops (e.g. winter wheat) playing a pivotal role in satisfying an ever-increasing food-demand of a growing population. Agricultural yield in India has been reported to be highly correlated with the timing and total amount of monsoon rainfall and/or temperature depending on crop type. With expected change in future climate (temperature and precipitation), significant fluctuations in crop yields are projected for near future. To date, little work has identified the sensitivity of cropping intensity, or the number of crops planted in a given year, to climate variability. The objective of this study is to shed light on relative importance of different climate parameters through a statistical analysis of inter-annual variations in cropping intensity at a regional scale, which may help identify adaptive strategies in response to future climate anomalies. Our study focuses on a highly human-modified landscape in central India, and uses a multi-sensor approach to determine the sensitivity of agriculture to climate variability. First, we assembled the 16-day time-series of 250m Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), and applied a spline function-based smoothing algorithm to develop maps of monsoon and winter crops in Central India for a decadal time-span. A hierarchical model involving moderate resolution Landsat (30m) data was used to estimate the heterogeneity of the spectral signature within the MODIS dataset (250m). We then compared the season-specific cropping patterns with spatio-temporal variability in climate parameters derived from the Tropical Rainfall Measuring Mission (TRMM) data. Initial data indicates that the existence of a monsoon crop has moderate to strong correlation with wet season end date (ρ = .522), wet season length (ρ = .522), and the number of rainy days during wet season (ρ = .829). Existence of a winter

Prediction of Short-Distance Aerial Movement of Phakopsora pachyrhizi Urediniospores Using Machine Learning.

PubMed

Wen, L; Bowen, C R; Hartman, G L

2017-10-01

Dispersal of urediniospores by wind is the primary means of spread for Phakopsora pachyrhizi, the cause of soybean rust. Our research focused on the short-distance movement of urediniospores from within the soybean canopy and up to 61 m from field-grown rust-infected soybean plants. Environmental variables were used to develop and compare models including the least absolute shrinkage and selection operator regression, zero-inflated Poisson/regular Poisson regression, random forest, and neural network to describe deposition of urediniospores collected in passive and active traps. All four models identified distance of trap from source, humidity, temperature, wind direction, and wind speed as the five most important variables influencing short-distance movement of urediniospores. The random forest model provided the best predictions, explaining 76.1 and 86.8% of the total variation in the passive- and active-trap datasets, respectively. The prediction accuracy based on the correlation coefficient (r) between predicted values and the true values were 0.83 (P < 0.0001) and 0.94 (P < 0.0001) for the passive and active trap datasets, respectively. Overall, multiple machine learning techniques identified the most important variables to make the most accurate predictions of movement of P. pachyrhizi urediniospores short-distance.

Prediction of hypertensive crisis based on average, variability and approximate entropy of 24-h ambulatory blood pressure monitoring.

PubMed

Schoenenberger, A W; Erne, P; Ammann, S; Perrig, M; Bürgi, U; Stuck, A E

2008-01-01

Approximate entropy (ApEn) of blood pressure (BP) can be easily measured based on software analysing 24-h ambulatory BP monitoring (ABPM), but the clinical value of this measure is unknown. In a prospective study we investigated whether ApEn of BP predicts, in addition to average and variability of BP, the risk of hypertensive crisis. In 57 patients with known hypertension we measured ApEn, average and variability of systolic and diastolic BP based on 24-h ABPM. Eight of these fifty-seven patients developed hypertensive crisis during follow-up (mean follow-up duration 726 days). In bivariate regression analysis, ApEn of systolic BP (P<0.01), average of systolic BP (P=0.02) and average of diastolic BP (P=0.03) were significant predictors of hypertensive crisis. The incidence rate ratio of hypertensive crisis was 14.0 (95% confidence interval (CI) 1.8, 631.5; P<0.01) for high ApEn of systolic BP as compared to low values. In multivariable regression analysis, ApEn of systolic (P=0.01) and average of diastolic BP (P<0.01) were independent predictors of hypertensive crisis. A combination of these two measures had a positive predictive value of 75%, and a negative predictive value of 91%, respectively. ApEn, combined with other measures of 24-h ABPM, is a potentially powerful predictor of hypertensive crisis. If confirmed in independent samples, these findings have major clinical implications since measures predicting the risk of hypertensive crisis define patients requiring intensive follow-up and intensified therapy.

Validation of China-wide interpolated daily climate variables from 1960 to 2011

NASA Astrophysics Data System (ADS)

Yuan, Wenping; Xu, Bing; Chen, Zhuoqi; Xia, Jiangzhou; Xu, Wenfang; Chen, Yang; Wu, Xiaoxu; Fu, Yang

2015-02-01

Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961-2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients ( R 2) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2, and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66-95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based

Response variability in rapid automatized naming predicts reading comprehension

PubMed Central

Li, James J.; Cutting, Laurie E.; Ryan, Matthew; Zilioli, Monica; Denckla, Martha B.; Mahone, E. Mark

2009-01-01

A total of 37 children ages 8 to 14 years, screened for word-reading difficulties (23 with attention-deficit/hyperactivity disorder, ADHD; 14 controls) completed oral reading and rapid automatized naming (RAN) tests. RAN trials were segmented into pause and articulation time and intraindividual variability. There were no group differences on reading or RAN variables. Color- and letter-naming pause times and number-naming articulation time were significant predictors of reading fluency. In contrast, number and letter pause variability were predictors of comprehension. Results support analysis of subcomponents of RAN and add to literature emphasizing intraindividual variability as a marker for response preparation, which has relevance to reading comprehension. PMID:19221923

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.

Impact of Preadmission Variables on USMLE Step 1 and Step 2 Performance

ERIC Educational Resources Information Center

Kleshinski, James; Khuder, Sadik A.; Shapiro, Joseph I.; Gold, Jeffrey P.

2009-01-01

Purpose: To examine the predictive ability of preadmission variables on United States Medical Licensing Examinations (USMLE) step 1 and step 2 performance, incorporating the use of a neural network model. Method: Preadmission data were collected on matriculants from 1998 to 2004. Linear regression analysis was first used to identify predictors of…

An examination of predictive variables toward graduation of minority students in science at a selected urban university

NASA Astrophysics Data System (ADS)

Hunter, Evelyn M. Irving

1998-12-01

The purpose of this study was to examine the relationship and predictive power of the variables gender, high school GPA, class rank, SAT scores, ACT scores, and socioeconomic status on the graduation rates of minority college students majoring in the sciences at a selected urban university. Data was examined on these variables as they related to minority students majoring in science. The population consisted of 101 minority college students who had majored in the sciences from 1986 to 1996 at an urban university in the southwestern region of Texas. A non-probability sampling procedure was used in this study. The non-probability sampling procedure in this investigation was incidental sampling technique. A profile sheet was developed to record the information regarding the variables. The composite scores from SAT and ACT testing were used in the study. The dichotomous variables gender and socioeconomic status were dummy coded for analysis. For the gender variable, zero (0) indicated male, and one (1) indicated female. Additionally, zero (0) indicated high SES, and one (1) indicated low SES. Two parametric procedures were used to analyze the data in this investigation. They were the multiple correlation and multiple regression procedures. Multiple correlation is a statistical technique that indicates the relationship between one variable and a combination of two other variables. The variables socioeconomic status and GPA were found to contribute significantly to the graduation rates of minority students majoring in all sciences when combined with chemistry (Hypotheses Two and Four). These variables accounted for 7% and 15% of the respective variance in the graduation rates of minority students in the sciences and in chemistry. Hypotheses One and Three, the predictor variables gender, high school GPA, SAT Total Scores, class rank, and socioeconomic status did not contribute significantly to the graduation rates of minority students in biology and pharmacy.

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

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

2013-01-01

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

Predicting discharge mortality after acute ischemic stroke using balanced data.

PubMed

Ho, King Chung; Speier, William; El-Saden, Suzie; Liebeskind, David S; Saver, Jeffery L; Bui, Alex A T; Arnold, Corey W

2014-01-01

Several models have been developed to predict stroke outcomes (e.g., stroke mortality, patient dependence, etc.) in recent decades. However, there is little discussion regarding the problem of between-class imbalance in stroke datasets, which leads to prediction bias and decreased performance. In this paper, we demonstrate the use of the Synthetic Minority Over-sampling Technique to overcome such problems. We also compare state of the art machine learning methods and construct a six-variable support vector machine (SVM) model to predict stroke mortality at discharge. Finally, we discuss how the identification of a reduced feature set allowed us to identify additional cases in our research database for validation testing. Our classifier achieved a c-statistic of 0.865 on the cross-validated dataset, demonstrating good classification performance using a reduced set of variables.

Assessment of general movements and heart rate variability in prediction of neurodevelopmental outcome in preterm infants.

PubMed

Dimitrijević, Lidija; Bjelaković, Bojko; Čolović, Hristina; Mikov, Aleksandra; Živković, Vesna; Kocić, Mirjana; Lukić, Stevo

2016-08-01

Adverse neurologic outcome in preterm infants could be associated with abnormal heart rate (HR) characteristics as well as with abnormal general movements (GMs) in the 1st month of life. To demonstrate to what extent GMs assessment can predict neurological outcome in preterm infants in our clinical setting; and to assess the clinical usefulness of time-domain indices of heart rate variability (HRV) in improving predictive value of poor repertoire (PR) GMs in writhing period. Qualitative assessment of GMs at 1 and 3 months corrected age; 24h electrocardiography (ECG) recordings and analyzing HRV at 1 month corrected age. Seventy nine premature infants at risk of neurodevelopmental impairments were included prospectively. Neurodevelopmental outcome was assessed at the age of 2 years corrected. Children were classified as having normal neurodevelopmental status, minor neurologic dysfunction (MND), or cerebral palsy (CP). We found that GMs in writhing period (1 month corrected age) predicted CP at 2 years with sensitivity of 100%, and specificity of 72.1%. Our results demonstrated the excellent predictive value of cramped synchronized (CS) GMs, but not of PR pattern. Analyzing separately a group of infants with PR GMs we found significantly lower values of HRV parameters in infants who later developed CP or MND vs. infants with PR GMs who had normal outcome. The quality of GMs was predictive for neurodevelopmental outcome at 2 years. Prediction of PR GMs was significantly enhanced with analyzing HRV parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Identifying and Assessing Gaps in Subseasonal to Seasonal Prediction Skill using the North American Multi-model Ensemble

NASA Astrophysics Data System (ADS)

Pegion, K.; DelSole, T. M.; Becker, E.; Cicerone, T.

2016-12-01

Predictability represents the upper limit of prediction skill if we had an infinite member ensemble and a perfect model. It is an intrinsic limit of the climate system associated with the chaotic nature of the atmosphere. Producing a forecast system that can make predictions very near to this limit is the ultimate goal of forecast system development. Estimates of predictability together with calculations of current prediction skill are often used to define the gaps in our prediction capabilities on subseasonal to seasonal timescales and to inform the scientific issues that must be addressed to build the next forecast system. Quantification of the predictability is also important for providing a scientific basis for relaying to stakeholders what kind of climate information can be provided to inform decision-making and what kind of information is not possible given the intrinsic predictability of the climate system. One challenge with predictability estimates is that different prediction systems can give different estimates of the upper limit of skill. How do we know which estimate of predictability is most representative of the true predictability of the climate system? Previous studies have used the spread-error relationship and the autocorrelation to evaluate the fidelity of the signal and noise estimates. Using a multi-model ensemble prediction system, we can quantify whether these metrics accurately indicate an individual model's ability to properly estimate the signal, noise, and predictability. We use this information to identify the best estimates of predictability for 2-meter temperature, precipitation, and sea surface temperature from the North American Multi-model Ensemble and compare with current skill to indicate the regions with potential for improving skill.

Application of Wavelet Analysis on Variability, Teleconnectivity, and Predictability November-January Taiwan rainfall

NASA Astrophysics Data System (ADS)

T.; Gan, Y.

2009-04-01

First the wavelet analysis was used to analyze the variability of winter (November-January) rainfall (1974-2006) of Taiwan and seasonal sea surface temperature (SST) in selected domains of the Pacific Ocean. From the scale average wavelet power (SAWP) computed for the seasonal rainfall and seasonal SST, it seems that these data exhibit interannual oscillations at 2-4-year period. Correlations between rainfall and SST SAWP were further estimated. Next the SST in selected sectors of the western Pacific Ocean (around 5°N-30°N, 120°E-150°E) was used as predictors to predict the winter rainfall of Taiwan at one season lead time using an Artificial Neural Network calibrated by Genetic Algorithm (ANN-GA). The ANN-GA was first calibrated using the 1974-1998 data and independently validated using 1999-2005 data. In terms of summary statistics such as the correlation coefficient, root-mean-square errors (RMSE), and Hansen-Kuipers (HK) scores, the seasonal prediction for northern and western Taiwan are generally good for both calibration and validation stages, but not so in some stations located in southeast Taiwan and Central Mountain.

Dynamic Network Communication in the Human Functional Connectome Predicts Perceptual Variability in Visual Illusion.

PubMed

Wang, Zhiwei; Zeljic, Kristina; Jiang, Qinying; Gu, Yong; Wang, Wei; Wang, Zheng

2018-01-01

Ubiquitous variability between individuals in visual perception is difficult to standardize and has thus essentially been ignored. Here we construct a quantitative psychophysical measure of illusory rotary motion based on the Pinna-Brelstaff figure (PBF) in 73 healthy volunteers and investigate the neural circuit mechanisms underlying perceptual variation using functional magnetic resonance imaging (fMRI). We acquired fMRI data from a subset of 42 subjects during spontaneous and 3 stimulus conditions: expanding PBF, expanding modified-PBF (illusion-free) and expanding modified-PBF with physical rotation. Brain-wide graph analysis of stimulus-evoked functional connectivity patterns yielded a functionally segregated architecture containing 3 discrete hierarchical networks, commonly shared between rest and stimulation conditions. Strikingly, communication efficiency and strength between 2 networks predominantly located in visual areas robustly predicted individual perceptual differences solely in the illusory stimulus condition. These unprecedented findings demonstrate that stimulus-dependent, not spontaneous, dynamic functional integration between distributed brain networks contributes to perceptual variability in humans. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification.