Third molar development: measurements versus scores as age predictor.

PubMed

Thevissen, P W; Fieuws, S; Willems, G

2011-10-01

Human third molar development is widely used to predict chronological age of sub adult individuals with unknown or doubted age. For these predictions, classically, the radiologically observed third molar growth and maturation is registered using a staging and related scoring technique. Measures of lengths and widths of the developing wisdom tooth and its adjacent second molar can be considered as an alternative registration. The aim of this study was to verify relations between mandibular third molar developmental stages or measurements of mandibular second molar and third molars and age. Age related performance of stages and measurements were compared to assess if measurements added information to age predictions from third molar formation stage. The sample was 340 orthopantomograms (170 females, 170 males) of individuals homogenously distributed in age between 7 and 24 years. Mandibular lower right, third and second molars, were staged following Gleiser and Hunt, length and width measurements were registered, and various ratios of these measurements were calculated. Univariable regression models with age as response and third molar stage, measurements and ratios of second and third molars as predictors, were considered. Multivariable regression models assessed if measurements or ratios added information to age prediction from third molar stage. Coefficients of determination (R(2)) and root mean squared errors (RMSE) obtained from all regression models were compared. The univariable regression model using stages as predictor yielded most accurate age predictions (males: R(2) 0.85, RMSE between 0.85 and 1.22 year; females: R(2) 0.77, RMSE between 1.19 and 2.11 year) compared to all models including measurements and ratios. The multivariable regression models indicated that measurements and ratios added no clinical relevant information to the age prediction from third molar stage. Ratios and measurements of second and third molars are less accurate age predictors than stages of developing third molars. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predicting Age-Appropriate Pharmacokinetics of Six Volatile Organic Compounds in the Rat Utilizing Physiologically Based Pharmacokinetic Modeling

EPA Science Inventory

The capability of physiologically based pharmacokinetic models to incorporate age-appropriate physiological and chemical-specific parameters was utilized to predict changes in internal dosimetry for six volatile organic compounds (VOCs) across different ages of rats.

A novel strategy for forensic age prediction by DNA methylation and support vector regression model

PubMed Central

Xu, Cheng; Qu, Hongzhu; Wang, Guangyu; Xie, Bingbing; Shi, Yi; Yang, Yaran; Zhao, Zhao; Hu, Lan; Fang, Xiangdong; Yan, Jiangwei; Feng, Lei

2015-01-01

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. PMID:26635134

Age at exposure and attained age variations of cancer risk in the Japanese A-bomb and radiotherapy cohorts

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

Schneider, Uwe, E-mail: uwe.schneider@uzh.ch; Walsh, Linda

Purpose: Phenomenological risk models for radiation-induced cancer are frequently applied to estimate the risk of radiation-induced cancers at radiotherapy doses. Such models often include the effect modification, of the main risk to radiation dose response, by age at exposure and attained age. The aim of this paper is to compare the patterns in risk effect modification by age, between models obtained from the Japanese atomic-bomb (A-bomb) survivor data and models for cancer risks previously reported for radiotherapy patients. Patterns in risk effect modification by age from the epidemiological studies of radiotherapy patients were also used to refine and extend themore » risk effect modification by age obtained from the A-bomb survivor data, so that more universal models can be presented here. Methods: Simple log-linear and power functions of age for the risk effect modification applied in models of the A-bomb survivor data are compared to risks from epidemiological studies of second cancers after radiotherapy. These functions of age were also refined and fitted to radiotherapy risks. The resulting age models provide a refined and extended functional dependence of risk with age at exposure and attained age especially beyond 40 and 65 yr, respectively, and provide a better representation than the currently available simple age functions. Results: It was found that the A-bomb models predict risk similarly to the outcomes of testicular cancer survivors. The survivors of Hodgkin’s disease show steeper variations of risk with both age at exposure and attained age. The extended models predict solid cancer risk increase as a function of age at exposure beyond 40 yr and the risk decrease as a function of attained age beyond 65 yr better than the simple models. Conclusions: The standard functions for risk effect modification by age, based on the A-bomb survivor data, predict second cancer risk in radiotherapy patients for ages at exposure prior to 40 yr and attained ages before 55 yr reasonably well. However, for larger ages, the refined and extended models can be applied to predict the risk as a function of age.« less

Age at exposure and attained age variations of cancer risk in the Japanese A-bomb and radiotherapy cohorts.

PubMed

Schneider, Uwe; Walsh, Linda

2015-08-01

Phenomenological risk models for radiation-induced cancer are frequently applied to estimate the risk of radiation-induced cancers at radiotherapy doses. Such models often include the effect modification, of the main risk to radiation dose response, by age at exposure and attained age. The aim of this paper is to compare the patterns in risk effect modification by age, between models obtained from the Japanese atomic-bomb (A-bomb) survivor data and models for cancer risks previously reported for radiotherapy patients. Patterns in risk effect modification by age from the epidemiological studies of radiotherapy patients were also used to refine and extend the risk effect modification by age obtained from the A-bomb survivor data, so that more universal models can be presented here. Simple log-linear and power functions of age for the risk effect modification applied in models of the A-bomb survivor data are compared to risks from epidemiological studies of second cancers after radiotherapy. These functions of age were also refined and fitted to radiotherapy risks. The resulting age models provide a refined and extended functional dependence of risk with age at exposure and attained age especially beyond 40 and 65 yr, respectively, and provide a better representation than the currently available simple age functions. It was found that the A-bomb models predict risk similarly to the outcomes of testicular cancer survivors. The survivors of Hodgkin's disease show steeper variations of risk with both age at exposure and attained age. The extended models predict solid cancer risk increase as a function of age at exposure beyond 40 yr and the risk decrease as a function of attained age beyond 65 yr better than the simple models. The standard functions for risk effect modification by age, based on the A-bomb survivor data, predict second cancer risk in radiotherapy patients for ages at exposure prior to 40 yr and attained ages before 55 yr reasonably well. However, for larger ages, the refined and extended models can be applied to predict the risk as a function of age.

Comparison of Two Predictive Models for Short-Term Mortality in Patients after Severe Traumatic Brain Injury.

PubMed

Kesmarky, Klara; Delhumeau, Cecile; Zenobi, Marie; Walder, Bernhard

2017-07-15

The Glasgow Coma Scale (GCS) and the Abbreviated Injury Score of the head region (HAIS) are validated prognostic factors in traumatic brain injury (TBI). The aim of this study was to compare the prognostic performance of an alternative predictive model including motor GCS, pupillary reactivity, age, HAIS, and presence of multi-trauma for short-term mortality with a reference predictive model including motor GCS, pupil reaction, and age (IMPACT core model). A secondary analysis of a prospective epidemiological cohort study in Switzerland including patients after severe TBI (HAIS >3) with the outcome death at 14 days was performed. Performance of prediction, accuracy of discrimination (area under the receiver operating characteristic curve [AUROC]), calibration, and validity of the two predictive models were investigated. The cohort included 808 patients (median age, 56; interquartile range, 33-71), median GCS at hospital admission 3 (3-14), abnormal pupil reaction 29%, with a death rate of 29.7% at 14 days. The alternative predictive model had a higher accuracy of discrimination to predict death at 14 days than the reference predictive model (AUROC 0.852, 95% confidence interval [CI] 0.824-0.880 vs. AUROC 0.826, 95% CI 0.795-0.857; p < 0.0001). The alternative predictive model had an equivalent calibration, compared with the reference predictive model Hosmer-Lemeshow p values (Chi2 8.52, Hosmer-Lemeshow p = 0.345 vs. Chi2 8.66, Hosmer-Lemeshow p = 0.372). The optimism-corrected value of AUROC for the alternative predictive model was 0.845. After severe TBI, a higher performance of prediction for short-term mortality was observed with the alternative predictive model, compared with the reference predictive model.

Ontogenetic loss of phenotypic plasticity of age at metamorphosis in tadpoles

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

Hensley, F.R.

1993-12-01

Amphibian larvae exhibit phenotypic plasticity in size at metamorphosis and duration of the larval period. I used Pseudacris crucifer tadpoles to test two models for predicting tadpole age and size at metamorphosis under changing environmental conditions. The Wilbur-Collins model states that metamorphosis is initiated as a function of a tadpole's size and relative growth rate, and predicts that changes in growth rate throughout the larval period affect age and size at metamorphosis. An alternative model, the fixed-rate model, states that age at metamorphosis is fixed early in larval life, and subsequent changes in growth rate will have no effect onmore » the length of the larval period. My results confirm that food supplies affect both age and size at metamorphosis, but developmental rates became fixed at approximately Gosner (1960) stages 35-37. Neither model completely predicted these results. I suggest that the generally accepted Wilbur-Collins model is improved by incorporating a point of fixed developmental timing. Growth trajectories predicted from this modified model fit the results of this study better than trajectories based on either of the original models. The results of this study suggests a constraint that limits the simultaneous optimization of age and size at metamorphosis. 32 refs., 5 figs., 1 tab.« less

Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use

NASA Astrophysics Data System (ADS)

Barré, Anthony; Suard, Frédéric; Gérard, Mathias; Montaru, Maxime; Riu, Delphine

2014-01-01

This paper describes the statistical analysis of recorded data parameters of electrical battery ageing during electric vehicle use. These data permit traditional battery ageing investigation based on the evolution of the capacity fade and resistance raise. The measured variables are examined in order to explain the correlation between battery ageing and operating conditions during experiments. Such study enables us to identify the main ageing factors. Then, detailed statistical dependency explorations present the responsible factors on battery ageing phenomena. Predictive battery ageing models are built from this approach. Thereby results demonstrate and quantify a relationship between variables and battery ageing global observations, and also allow accurate battery ageing diagnosis through predictive models.

Modeling and prediction of relaxation of polar order in high-activity nonlinear optical polymers

NASA Astrophysics Data System (ADS)

Guenthner, Andrew J.; Lindsay, Geoffrey A.; Wright, Michael E.; Fallis, Stephen; Ashley, Paul R.; Sanghadasa, Mohan

2007-09-01

Mach-Zehnder optical modulators were fabricated using the CLD and FTC chromophores in polymer-on-silicon optical waveguides. Up to 17 months of oven-ageing stability are reported for the poled polymer films. Modulators containing an FTC-polyimide had the best over all aging performance. To model and extrapolate the ageing data, a relaxation correlation function attributed to A. K. Jonscher was compared to the well-established stretched exponential correlation function. Both models gave a good fit to the data. The Jonscher model predicted a slower relaxation rate in the out years. Analysis showed that collecting data for a longer period relative to the relaxation time was more important for generating useful predictions than the precision with which individual model parameters could be estimated. Thus from a practical standpoint, time-temperature superposition must be assumed in order to generate meaningful predictions. For this purpose, Arrhenius-type expressions were found to relate the model time constants to the ageing temperatures.

[Establishment of the prediction model for ischemic cardiovascular disease of elderly male population under current health care program].

PubMed

Chen, Jin-hong; Wu, Hai-yun; He, Kun-lun; He, Yao; Qin, Yin-he

2010-10-01

To establish and verify the prediction model for ischemic cardiovascular disease (ICVD) among the elderly population who were under the current health care programs. Statistical analysis on data from physical examination, hospitalization of the past years, from questionnaire and telephone interview was carried out in May, 2003. Data was from a hospital which implementing a health care program. Baseline population with a proportion of 4:1 was randomly selected to generate both module group and verification group. Baseline data was induced to make the verification group into regression model of module group and to generate the predictive value. Distinguished ability with area under ROC curve and the predictive veracity were verified through comparing the predictive incidence rate and actual incidence rate of every deciles group by Hosmer-Lemeshow test. Predictive veracity of the prediction model at population level was verified through comparing the predictive 6-year incidence rates of ICVD with actual 6-year accumulative incidence rates of ICVD with error rate calculated. The samples included 2271 males over the age of 65 with 1817 people for modeling population and 454 for verified population. All of the samples were stratified into two layers to establish hierarchical Cox proportional hazard regression model, including one advanced age group (greater than or equal to 75 years old), and another elderly group (less than 75 years old). Data from the statically analysis showed that the risk factors in aged group were age, systolic blood pressure, serum creatinine level, fasting blood glucose level, while protective factor was high density lipoprotein;in advanced age group, the risk factors were body weight index, systolic blood pressure, serum total cholesterol level, serum creatinine level, fasting blood glucose level, while protective factor was HDL-C. The area under the ROC curve (AUC) and 95%CI were 0.723 and 0.687 - 0.759 respectively. Discriminating power was good. All individual predictive ICVD cumulative incidence and actual incidence were analyzed using Hosmer-Lemeshow test, χ(2) = 1.43, P = 0.786, showing that the predictive veracity was good. The stratified Cox Hazards Regression model was used to establish prediction model of the aged male population under a certain health care program. The common prediction factor of the two age groups were: systolic blood pressure, serum creatinine level, fasting blood glucose level and HDL-C. The area under the ROC curve of the verification group was 0.723, showing that the distinguished ability was good and the predict ability at the individual level and at the group level were also satisfactory. It was feasible to using Cox Proportional Hazards Regression Model for predicting the population groups.

A Theory of Age-Dependent Mutation and Senescence

PubMed Central

Moorad, Jacob A.; Promislow, Daniel E. L.

2008-01-01

Laboratory experiments show us that the deleterious character of accumulated novel age-specific mutations is reduced and made less variable with increased age. While theories of aging predict that the frequency of deleterious mutations at mutation–selection equilibrium will increase with the mutation's age of effect, they do not account for these age-related changes in the distribution of de novo mutational effects. Furthermore, no model predicts why this dependence of mutational effects upon age exists. Because the nature of mutational distributions plays a critical role in shaping patterns of senescence, we need to develop aging theory that explains and incorporates these effects. Here we propose a model that explains the age dependency of mutational effects by extending Fisher's geometrical model of adaptation to include a temporal dimension. Using a combination of simple analytical arguments and simulations, we show that our model predicts age-specific mutational distributions that are consistent with observations from mutation-accumulation experiments. Simulations show us that these age-specific mutational effects may generate patterns of senescence at mutation–selection equilibrium that are consistent with observed demographic patterns that are otherwise difficult to explain. PMID:18660535

Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age

PubMed Central

Strath, Scott J; Kate, Rohit J; Keenan, Kevin G; Welch, Whitney A; Swartz, Ann M

2016-01-01

To develop and test time series single site and multi-site placement models, we used wrist, hip and ankle processed accelerometer data to estimate energy cost and type of physical activity in adults. Ninety-nine subjects in three age groups (18–39, 40–64, 65 + years) performed 11 activities while wearing three triaxial accelereometers: one each on the non-dominant wrist, hip, and ankle. During each activity net oxygen cost (METs) was assessed. The time series of accelerometer signals were represented in terms of uniformly discretized values called bins. Support Vector Machine was used for activity classification with bins and every pair of bins used as features. Bagged decision tree regression was used for net metabolic cost prediction. To evaluate model performance we employed the jackknife leave-one-out cross validation method. Single accelerometer and multi-accelerometer site model estimates across and within age group revealed similar accuracy, with a bias range of −0.03 to 0.01 METs, bias percent of −0.8 to 0.3%, and a rMSE range of 0.81–1.04 METs. Multi-site accelerometer location models improved activity type classification over single site location models from a low of 69.3% to a maximum of 92.8% accuracy. For each accelerometer site location model, or combined site location model, percent accuracy classification decreased as a function of age group, or when young age groups models were generalized to older age groups. Specific age group models on average performed better than when all age groups were combined. A time series computation show promising results for predicting energy cost and activity type. Differences in prediction across age group, a lack of generalizability across age groups, and that age group specific models perform better than when all ages are combined needs to be considered as analytic calibration procedures to detect energy cost and type are further developed. PMID:26449155

Predicting age groups of Twitter users based on language and metadata features

PubMed Central

Morgan-Lopez, Antonio A.; Chew, Robert F.; Ruddle, Paul

2017-01-01

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles’ metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen’s d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as “school” for youth and “college” for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research. PMID:28850620

Predicting age groups of Twitter users based on language and metadata features.

PubMed

Morgan-Lopez, Antonio A; Kim, Annice E; Chew, Robert F; Ruddle, Paul

2017-01-01

Health organizations are increasingly using social media, such as Twitter, to disseminate health messages to target audiences. Determining the extent to which the target audience (e.g., age groups) was reached is critical to evaluating the impact of social media education campaigns. The main objective of this study was to examine the separate and joint predictive validity of linguistic and metadata features in predicting the age of Twitter users. We created a labeled dataset of Twitter users across different age groups (youth, young adults, adults) by collecting publicly available birthday announcement tweets using the Twitter Search application programming interface. We manually reviewed results and, for each age-labeled handle, collected the 200 most recent publicly available tweets and user handles' metadata. The labeled data were split into training and test datasets. We created separate models to examine the predictive validity of language features only, metadata features only, language and metadata features, and words/phrases from another age-validated dataset. We estimated accuracy, precision, recall, and F1 metrics for each model. An L1-regularized logistic regression model was conducted for each age group, and predicted probabilities between the training and test sets were compared for each age group. Cohen's d effect sizes were calculated to examine the relative importance of significant features. Models containing both Tweet language features and metadata features performed the best (74% precision, 74% recall, 74% F1) while the model containing only Twitter metadata features were least accurate (58% precision, 60% recall, and 57% F1 score). Top predictive features included use of terms such as "school" for youth and "college" for young adults. Overall, it was more challenging to predict older adults accurately. These results suggest that examining linguistic and Twitter metadata features to predict youth and young adult Twitter users may be helpful for informing public health surveillance and evaluation research.

Integrating Growth Variability of the Ilium, Fifth Lumbar Vertebra, and Clavicle with Multivariate Adaptive Regression Splines Models for Subadult Age Estimation.

PubMed

Corron, Louise; Marchal, François; Condemi, Silvana; Telmon, Norbert; Chaumoitre, Kathia; Adalian, Pascal

2018-05-31

Subadult age estimation should rely on sampling and statistical protocols capturing development variability for more accurate age estimates. In this perspective, measurements were taken on the fifth lumbar vertebrae and/or clavicles of 534 French males and females aged 0-19 years and the ilia of 244 males and females aged 0-12 years. These variables were fitted in nonparametric multivariate adaptive regression splines (MARS) models with 95% prediction intervals (PIs) of age. The models were tested on two independent samples from Marseille and the Luis Lopes reference collection from Lisbon. Models using ilium width and module, maximum clavicle length, and lateral vertebral body heights were more than 92% accurate. Precision was lower for postpubertal individuals. Integrating punctual nonlinearities of the relationship between age and the variables and dynamic prediction intervals incorporated the normal increase in interindividual growth variability (heteroscedasticity of variance) with age for more biologically accurate predictions. © 2018 American Academy of Forensic Sciences.

DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing.

PubMed

Vidaki, Athina; Ballard, David; Aliferi, Anastasia; Miller, Thomas H; Barron, Leon P; Syndercombe Court, Denise

2017-05-01

The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person's lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2-90 years) analysed with Illumina's genome-wide methylation platforms (27K/450K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R 2 =0.92, mean absolute error (MAE)=4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R 2 =0.96) with a MAE=3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE=7.1 years) and a cohort of 1011 disease state individuals (MAE=7.2 years). Furthermore, we highlighted the age markers' potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R 2 =0.96) with a MAE=3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq ® platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE=7.5years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

An age-specific biokinetic model for iodine

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

Leggett, Richard Wayne

This study reviews age-specific biokinetic data for iodine in humans and extends to pre-adult ages the baseline parameter values of the author’s previously published model for systemic iodine in adult humans. Compared with the ICRP’s current age-specific model for iodine introduced in Publication 56 (1989), the present model provides a more detailed description of the behavior of iodine in the human body; predicts greater cumulative (integrated) activity in the thyroid for short-lived isotopes of iodine; predicts similar cumulative activity in the thyroid for isotopes with half-time greater than a few hours; and, for most iodine isotopes, predicts much greater cumulativemore » activity in salivary glands, stomach wall, liver, and kidneys.« less

An age-specific biokinetic model for iodine

DOE PAGES

Leggett, Richard Wayne

2017-10-26

This study reviews age-specific biokinetic data for iodine in humans and extends to pre-adult ages the baseline parameter values of the author’s previously published model for systemic iodine in adult humans. Compared with the ICRP’s current age-specific model for iodine introduced in Publication 56 (1989), the present model provides a more detailed description of the behavior of iodine in the human body; predicts greater cumulative (integrated) activity in the thyroid for short-lived isotopes of iodine; predicts similar cumulative activity in the thyroid for isotopes with half-time greater than a few hours; and, for most iodine isotopes, predicts much greater cumulativemore » activity in salivary glands, stomach wall, liver, and kidneys.« less

Ages and transit times as important diagnostics of model performance for predicting C allocation in ecosystem models

NASA Astrophysics Data System (ADS)

Ceballos-Núñez, Verónika; Richardson, Andrew; Sierra, Carlos

2017-04-01

The global carbon cycle is strongly controlled by the source/sink strength of vegetation as well as the capacity of terrestrial ecosystems to retain this carbon. However, it is uncertain how some vegetation dynamics such as the allocation of carbon to different ecosystem compartments should be represented in models. The assumptions behind model structures may result in highly divergent model predictions. Here, we asses model performance by calculating the age of the carbon in the system and in each compartment, and the overall transit time of C in the system. We used these diagnostics to assess the influence of three different carbon allocation schemes on the rates of C cycling in vegetation. First, we used published measurements of ecosystem C compartments from the Harvard Forest Environmental Measurement Site to find the best set of parameters for the different model structures. Second, we calculated C stocks, respiration fluxes, radiocarbon values, ages, and transit times. We found a good fit of the three model structures to the available data, but the time series of C in foliage and wood need to be complemented with other ecosystem compartments in order to reduce the high parameter collinearity that we observed and reduce model equifinality. Differences in model structures had a small impact on predicting ecosystem C compartments, but overall they resulted in very different predictions of age and transit time distributions. In particular, the inclusion of a storage compartment had an important impact on predicting system ages and transit times. In the case of the models with 1 or 2 storage compartments, the age of carbon in the system and in each of the compartments was distributed more towards younger ages than in the model that had no storage; the mean system age of these two models with storage was 80 years younger than in the model without storage. As expected from these age distributions, the mean transit time for the two models with storage compartments was 50 years faster than for the model without storage. These results suggest that ages and transit times, which can be indirectly measured using isotope tracers, serve as important diagnostics of model structure and could largely help to reduce uncertainties in model predictions. Furthermore, by considering age and transit times of C in vegetation compartments as distributions, not only their mean values, we obtain additional insights on the temporal dynamics of carbon use, storage, and allocation to plant parts, which not only depends on the rate at which this C is transferred in and out of the compartments, but also on the stochastic nature of the process itself.

How long will my mouse live? Machine learning approaches for prediction of mouse life span.

PubMed

Swindell, William R; Harper, James M; Miller, Richard A

2008-09-01

Prediction of individual life span based on characteristics evaluated at middle-age represents a challenging objective for aging research. In this study, we used machine learning algorithms to construct models that predict life span in a stock of genetically heterogeneous mice. Life-span prediction accuracy of 22 algorithms was evaluated using a cross-validation approach, in which models were trained and tested with distinct subsets of data. Using a combination of body weight and T-cell subset measures evaluated before 2 years of age, we show that the life-span quartile to which an individual mouse belongs can be predicted with an accuracy of 35.3% (+/-0.10%). This result provides a new benchmark for the development of life-span-predictive models, but improvement can be expected through identification of new predictor variables and development of computational approaches. Future work in this direction can provide tools for aging research and will shed light on associations between phenotypic traits and longevity.

Quantifying the dynamics of field cancerization in tobacco-related head and neck cancer: a multi-scale modeling approach

PubMed Central

Ryser, Marc D.; Lee, Walter T.; Readyz, Neal E.; Leder, Kevin Z.; Foo, Jasmine

2017-01-01

High rates of local recurrence in tobacco-related head and neck squamous cell carcinoma (HNSCC) are commonly attributed to unresected fields of precancerous tissue. Since they are not easily detectable at the time of surgery without additional biopsies, there is a need for non-invasive methods to predict the extent and dynamics of these fields. Here we developed a spatial stochastic model of tobacco-related HNSCC at the tissue level and calibrated the model using a Bayesian framework and population-level incidence data from the Surveillance, Epidemiology, and End Results (SEER) registry. Probabilistic model analyses were performed to predict the field geometry at time of diagnosis, and model predictions of age-specific recurrence risks were tested against outcome data from SEER. The calibrated models predicted a strong dependence of the local field size on age at diagnosis, with a doubling of the expected field diameter between ages at diagnosis of 50 and 90 years, respectively. Similarly, the probability of harboring multiple, clonally unrelated fields at the time of diagnosis were found to increase substantially with patient age. Based on these findings, we hypothesized a higher recurrence risk in older compared to younger patients when treated by surgery alone; we successfully tested this hypothesis using age-stratified outcome data. Further clinical studies are needed to validate the model predictions in a patient-specific setting. This work highlights the importance of spatial structure in models of epithelial carcinogenesis, and suggests that patient age at diagnosis may be a critical predictor of the size and multiplicity of precancerous lesions. Major Findings Patient age at diagnosis was found to be a critical predictor of the size and multiplicity of precancerous lesions. This finding challenges the current one-size-fits-all approach to surgical excision margins. PMID:27913438

Ages and transit times as important diagnostics of model performance for predicting carbon dynamics in terrestrial vegetation models

NASA Astrophysics Data System (ADS)

Ceballos-Núñez, Verónika; Richardson, Andrew D.; Sierra, Carlos A.

2018-03-01

The global carbon cycle is strongly controlled by the source/sink strength of vegetation as well as the capacity of terrestrial ecosystems to retain this carbon. These dynamics, as well as processes such as the mixing of old and newly fixed carbon, have been studied using ecosystem models, but different assumptions regarding the carbon allocation strategies and other model structures may result in highly divergent model predictions. We assessed the influence of three different carbon allocation schemes on the C cycling in vegetation. First, we described each model with a set of ordinary differential equations. Second, we used published measurements of ecosystem C compartments from the Harvard Forest Environmental Measurement Site to find suitable parameters for the different model structures. And third, we calculated C stocks, release fluxes, radiocarbon values (based on the bomb spike), ages, and transit times. We obtained model simulations in accordance with the available data, but the time series of C in foliage and wood need to be complemented with other ecosystem compartments in order to reduce the high parameter collinearity that we observed, and reduce model equifinality. Although the simulated C stocks in ecosystem compartments were similar, the different model structures resulted in very different predictions of age and transit time distributions. In particular, the inclusion of two storage compartments resulted in the prediction of a system mean age that was 12-20 years older than in the models with one or no storage compartments. The age of carbon in the wood compartment of this model was also distributed towards older ages, whereas fast cycling compartments had an age distribution that did not exceed 5 years. As expected, models with C distributed towards older ages also had longer transit times. These results suggest that ages and transit times, which can be indirectly measured using isotope tracers, serve as important diagnostics of model structure and could largely help to reduce uncertainties in model predictions. Furthermore, by considering age and transit times of C in vegetation compartments as distributions, not only their mean values, we obtain additional insights into the temporal dynamics of carbon use, storage, and allocation to plant parts, which not only depends on the rate at which this C is transferred in and out of the compartments but also on the stochastic nature of the process itself.

Population specific biomarkers of human aging: a big data study using South Korean, Canadian and Eastern European patient populations.

PubMed

Mamoshina, Polina; Kochetov, Kirill; Putin, Evgeny; Cortese, Franco; Aliper, Alexander; Lee, Won-Suk; Ahn, Sung-Min; Uhn, Lee; Skjodt, Neil; Kovalchuk, Olga; Scheibye-Knudsen, Morten; Zhavoronkov, Alex

2018-01-11

Accurate and physiologically meaningful biomarkers for human aging are key to assessing anti-aging therapies. Given ethnic differences in health, diet, lifestyle, behaviour, environmental exposures and even average rate of biological aging, it stands to reason that aging clocks trained on datasets obtained from specific ethnic populations are more likely to account for these potential confounding factors, resulting in an enhanced capacity to predict chronological age and quantify biological age. Here we present a deep learning-based hematological aging clock modeled using the large combined dataset of Canadian, South Korean and Eastern European population blood samples that show increased predictive accuracy in individual populations compared to population-specific hematologic aging clocks. The performance of models was also evaluated on publicly-available samples of the American population from the National Health and Nutrition Examination Survey (NHANES). In addition, we explored the association between age predicted by both population-specific and combined hematological clocks and all-cause mortality. Overall, this study suggests a) the population-specificity of aging patterns and b) hematologic clocks predicts all-cause mortality. Proposed models added to the freely available Aging.AI system allowing improved ability to assess human aging. © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America.

Variation and Grey GM(1, 1) Prediction of Melting Peak Temperature of Polypropylene During Ultraviolet Radiation Aging

NASA Astrophysics Data System (ADS)

Chen, K.; Y Zhang, T.; Zhang, F.; Zhang, Z. R.

2017-12-01

Grey system theory regards uncertain system in which information is known partly and unknown partly as research object, extracts useful information from part known, and thereby revealing the potential variation rule of the system. In order to research the applicability of data-driven modelling method in melting peak temperature (T m) fitting and prediction of polypropylene (PP) during ultraviolet radiation aging, the T m of homo-polypropylene after different ultraviolet radiation exposure time investigated by differential scanning calorimeter was fitted and predicted by grey GM(1, 1) model based on grey system theory. The results show that the T m of PP declines with the prolong of aging time, and fitting and prediction equation obtained by grey GM(1, 1) model is T m = 166.567472exp(-0.00012t). Fitting effect of the above equation is excellent and the maximum relative error between prediction value and actual value of T m is 0.32%. Grey system theory needs less original data, has high prediction accuracy, and can be used to predict aging behaviour of PP.

Testing the effects of topography, geometry, and kinematics on modeled thermochronometer cooling ages in the eastern Bhutan Himalaya

NASA Astrophysics Data System (ADS)

Gilmore, Michelle E.; McQuarrie, Nadine; Eizenhöfer, Paul R.; Ehlers, Todd A.

2018-05-01

In this study, reconstructions of a balanced geologic cross section in the Himalayan fold-thrust belt of eastern Bhutan are used in flexural-kinematic and thermokinematic models to understand the sensitivity of predicted cooling ages to changes in fault kinematics, geometry, topography, and radiogenic heat production. The kinematics for each scenario are created by sequentially deforming the cross section with ˜ 10 km deformation steps while applying flexural loading and erosional unloading at each step to develop a high-resolution evolution of deformation, erosion, and burial over time. By assigning ages to each increment of displacement, we create a suite of modeled scenarios that are input into a 2-D thermokinematic model to predict cooling ages. Comparison of model-predicted cooling ages to published thermochronometer data reveals that cooling ages are most sensitive to (1) the location and size of fault ramps, (2) the variable shortening rates between 68 and 6.4 mm yr-1, and (3) the timing and magnitude of out-of-sequence faulting. The predicted ages are less sensitive to (4) radiogenic heat production and (5) estimates of topographic evolution. We used the observed misfit of predicted to measured cooling ages to revise the cross section geometry and separate one large ramp previously proposed for the modern décollement into two smaller ramps. The revised geometry results in an improved fit to observed ages, particularly young AFT ages (2-6 Ma) located north of the Main Central Thrust. This study presents a successful approach for using thermochronometer data to test the viability of a proposed cross section geometry and kinematics and describes a viable approach to estimating the first-order topographic evolution of a compressional orogen.

Uncertainty in age-specific harvest estimates and consequences for white-tailed deer management

USGS Publications Warehouse

Collier, B.A.; Krementz, D.G.

2007-01-01

Age structure proportions (proportion of harvested individuals within each age class) are commonly used as support for regulatory restrictions and input for deer population models. Such use requires critical evaluation when harvest regulations force hunters to selectively harvest specific age classes, due to impact on the underlying population age structure. We used a stochastic population simulation model to evaluate the impact of using harvest proportions to evaluate changes in population age structure under a selective harvest management program at two scales. Using harvest proportions to parameterize the age-specific harvest segment of the model for the local scale showed that predictions of post-harvest age structure did not vary dependent upon whether selective harvest criteria were in use or not. At the county scale, yearling frequency in the post-harvest population increased, but model predictions indicated that post-harvest population size of 2.5 years old males would decline below levels found before implementation of the antler restriction, reducing the number of individuals recruited into older age classes. Across the range of age-specific harvest rates modeled, our simulation predicted that underestimation of age-specific harvest rates has considerable influence on predictions of post-harvest population age structure. We found that the consequence of uncertainty in harvest rates corresponds to uncertainty in predictions of residual population structure, and this correspondence is proportional to scale. Our simulations also indicate that regardless of use of harvest proportions or harvest rates, at either the local or county scale the modeled SHC had a high probability (>0.60 and >0.75, respectively) of eliminating recruitment into >2.5 years old age classes. Although frequently used to increase population age structure, our modeling indicated that selective harvest criteria can decrease or eliminate the number of white-tailed deer recruited into older age classes. Thus, we suggest that using harvest proportions for management planning and evaluation should be viewed with caution. In addition, we recommend that managers focus more attention on estimation of age-specific harvest rates, and modeling approaches which combine harvest rates with information from harvested individuals to further increase their ability to effectively manage deer populations under selective harvest programs. ?? 2006 Elsevier B.V. All rights reserved.

Development of an aerobic capacity prediction model from one-mile run/walk performance in adolescents aged 13-16 years.

PubMed

Burns, Ryan D; Hannon, James C; Brusseau, Timothy A; Eisenman, Patricia A; Shultz, Barry B; Saint-Maurice, Pedro F; Welk, Gregory J; Mahar, Matthew T

2016-01-01

A popular algorithm to predict VO2Peak from the one-mile run/walk test (1MRW) includes body mass index (BMI), which manifests practical issues in school settings. The purpose of this study was to develop an aerobic capacity model from 1MRW in adolescents independent of BMI. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years. The 1MRW was administered on an outside track and a laboratory VO2Peak test was conducted using a maximal treadmill protocol. Multiple linear regression was employed to develop the prediction model. Results yielded the following algorithm: VO2Peak = 7.34 × (1MRW speed in m s(-1)) + 0.23 × (age × sex) + 17.75. The New Model displayed a multiple correlation and prediction error of R = 0.81, standard error of the estimate = 4.78 ml kg(-1) · min(-1), with measured VO2Peak and good criterion-referenced (CR) agreement into FITNESSGRAM's Healthy Fitness Zone (Kappa = 0.62; percentage agreement = 84.4%; Φ = 0.62). The New Model was validated using k-fold cross-validation and showed homoscedastic residuals across the range of predicted scores. The omission of BMI did not compromise accuracy of the model. In conclusion, the New Model displayed good predictive accuracy and good CR agreement with measured VO2Peak in adolescents aged 13-16 years.

Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation.

PubMed

Cook, Sarah F; Roberts, Jessica K; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D; Deutsch, Nina; Williams, Elaine F; Allegaert, Karel; Wilkins, Diana G; Sherwin, Catherine M T; van den Anker, John N

2016-01-01

The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Nonlinear mixed-effects models were constructed from paracetamol concentration-time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1-14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1-28.1). Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations.

Population Pharmacokinetics of Intravenous Paracetamol (Acetaminophen) in Preterm and Term Neonates: Model Development and External Evaluation

PubMed Central

Cook, Sarah F.; Roberts, Jessica K.; Samiee-Zafarghandy, Samira; Stockmann, Chris; King, Amber D.; Deutsch, Nina; Williams, Elaine F.; Allegaert, Karel; Sherwin, Catherine M. T.; van den Anker, John N.

2017-01-01

Objectives The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Methods Nonlinear mixed-effects models were constructed from paracetamol concentration–time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. Results The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1–14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1–28.1). Conclusions Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations. PMID:26201306

Impact of Age on the Risk of Advanced Colorectal Neoplasia in a Young Population: An Analysis Using the Predicted Probability Model.

PubMed

Jung, Yoon Suk; Park, Chan Hyuk; Kim, Nam Hee; Lee, Mi Yeon; Park, Dong Il

2017-09-01

The incidence of colorectal cancer is decreasing in adults aged ≥50 years and increasing in those aged <50 years. We aimed to establish risk stratification model for advanced colorectal neoplasia (ACRN) in persons aged <50 years. We reviewed the records of participants who had undergone a colonoscopy as part of a health examination at two large medical examination centers in Korea. By using logistic regression analysis, we developed predicted probability models for ACRN in a population aged 30-49 years. Of 96,235 participants, 57,635 and 38,600 were included in the derivation and validation cohorts, respectively. The predicted probability model considered age, sex, body mass index, family history of colorectal cancer, and smoking habits, as follows: Y ACRN  = -8.755 + 0.080·X age  - 0.055·X male  + 0.041·X BMI  + 0.200·X family_history_of_CRC  + 0.218·X former_smoker  + 0.644·X current_smoker . The optimal cutoff value for the predicted probability of ACRN by Youden index was 1.14%. The area under the receiver-operating characteristic curve (AUROC) values of our model for ACRN were higher than those of the previously established Asia-Pacific Colorectal Screening (APCS), Korean Colorectal Screening (KCS), and Kaminski's scoring models [AUROC (95% confidence interval): model in the current study, 0.673 (0.648-0.697); vs. APCS, 0.588 (0.564-0.611), P < 0.001; vs. KCS, 0.602 (0.576-0.627), P < 0.001; and vs. Kaminski's model, 0.586 (0.560-0.612), P < 0.001]. In a young population, a predicted probability model can assess the risk of ACRN more accurately than existing models, including the APCS, KCS, and Kaminski's scoring models.

Sociodemographic Factors Associated With Changes in Successful Aging in Spain: A Follow-Up Study.

PubMed

Domènech-Abella, Joan; Perales, Jaime; Lara, Elvira; Moneta, Maria Victoria; Izquierdo, Ana; Rico-Uribe, Laura Alejandra; Mundó, Jordi; Haro, Josep Maria

2017-06-01

Successful aging (SA) refers to maintaining well-being in old age. Several definitions or models of SA exist (biomedical, psychosocial, and mixed). We examined the longitudinal association between various SA models and sociodemographic factors, and analyzed the patterns of change within these models. This was a nationally representative follow-up in Spain including 3,625 individuals aged ≥50 years. Some 1,970 individuals were interviewed after 3 years. Linear regression models were used to analyze the survey data. Age, sex, and occupation predicted SA in the biomedical model, while marital status, educational level, and urbanicity predicted SA in the psychosocial model. The remaining models included different sets of these predictors as significant. In the psychosocial model, individuals tended to improve over time but this was not the case in the biomedical model. The biomedical and psychosocial components of SA need to be addressed specifically to achieve the best aging trajectories.

Proton exchange membrane fuel cell model for aging predictions: Simulated equivalent active surface area loss and comparisons with durability tests

NASA Astrophysics Data System (ADS)

Robin, C.; Gérard, M.; Quinaud, M.; d'Arbigny, J.; Bultel, Y.

2016-09-01

The prediction of Proton Exchange Membrane Fuel Cell (PEMFC) lifetime is one of the major challenges to optimize both material properties and dynamic control of the fuel cell system. In this study, by a multiscale modeling approach, a mechanistic catalyst dissolution model is coupled to a dynamic PEMFC cell model to predict the performance loss of the PEMFC. Results are compared to two 2000-h experimental aging tests. More precisely, an original approach is introduced to estimate the loss of an equivalent active surface area during an aging test. Indeed, when the computed Electrochemical Catalyst Surface Area profile is fitted on the experimental measures from Cyclic Voltammetry, the computed performance loss of the PEMFC is underestimated. To be able to predict the performance loss measured by polarization curves during the aging test, an equivalent active surface area is obtained by a model inversion. This methodology enables to successfully find back the experimental cell voltage decay during time. The model parameters are fitted from the polarization curves so that they include the global degradation. Moreover, the model captures the aging heterogeneities along the surface of the cell observed experimentally. Finally, a second 2000-h durability test in dynamic operating conditions validates the approach.

Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination.

PubMed

Majnarić-Trtica, Ljiljana; Vitale, Branko

2011-10-01

To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling. Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses. The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression. A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.

A feature-based developmental model of the infant brain in structural MRI.

PubMed

Toews, Matthew; Wells, William M; Zöllei, Lilla

2012-01-01

In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days.

Quantifying the Dynamics of Field Cancerization in Tobacco-Related Head and Neck Cancer: A Multiscale Modeling Approach.

PubMed

Ryser, Marc D; Lee, Walter T; Ready, Neal E; Leder, Kevin Z; Foo, Jasmine

2016-12-15

High rates of local recurrence in tobacco-related head and neck squamous cell carcinoma (HNSCC) are commonly attributed to unresected fields of precancerous tissue. Because they are not easily detectable at the time of surgery without additional biopsies, there is a need for noninvasive methods to predict the extent and dynamics of these fields. Here, we developed a spatial stochastic model of tobacco-related HNSCC at the tissue level and calibrated the model using a Bayesian framework and population-level incidence data from the Surveillance, Epidemiology, and End Results (SEER) registry. Probabilistic model analyses were performed to predict the field geometry at time of diagnosis, and model predictions of age-specific recurrence risks were tested against outcome data from SEER. The calibrated models predicted a strong dependence of the local field size on age at diagnosis, with a doubling of the expected field diameter between ages at diagnosis of 50 and 90 years, respectively. Similarly, the probability of harboring multiple, clonally unrelated fields at the time of diagnosis was found to increase substantially with patient age. On the basis of these findings, we hypothesized a higher recurrence risk in older than in younger patients when treated by surgery alone; we successfully tested this hypothesis using age-stratified outcome data. Further clinical studies are needed to validate the model predictions in a patient-specific setting. This work highlights the importance of spatial structure in models of epithelial carcinogenesis and suggests that patient age at diagnosis may be a critical predictor of the size and multiplicity of precancerous lesions. Cancer Res; 76(24); 7078-88. ©2016 AACR. ©2016 American Association for Cancer Research.

Using the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) to predict 1-year mortality in population-based cohorts of patients with diabetes in Ontario, Canada.

PubMed

Austin, P C; Shah, B R; Newman, A; Anderson, G M

2012-09-01

There are limited validated methods to ascertain comorbidities for risk adjustment in ambulatory populations of patients with diabetes using administrative health-care databases. The objective was to examine the ability of the Johns Hopkins' Aggregated Diagnosis Groups to predict mortality in population-based ambulatory samples of both incident and prevalent subjects with diabetes. Retrospective cohorts constructed using population-based administrative data. The incident cohort consisted of all 346,297 subjects diagnosed with diabetes between 1 April 2004 and 31 March 2008. The prevalent cohort consisted of all 879,849 subjects with pre-existing diabetes on 1 January, 2007. The outcome was death within 1 year of the subject's index date. A logistic regression model consisting of age, sex and indicator variables for 22 of the 32 Johns Hopkins' Aggregated Diagnosis Group categories had excellent discrimination for predicting mortality in incident diabetes patients: the c-statistic was 0.87 in an independent validation sample. A similar model had excellent discrimination for predicting mortality in prevalent diabetes patients: the c-statistic was 0.84 in an independent validation sample. Both models demonstrated very good calibration, denoting good agreement between observed and predicted mortality across the range of predicted mortality in which the large majority of subjects lay. For comparative purposes, regression models incorporating the Charlson comorbidity index, age and sex, age and sex, and age alone had poorer discrimination than the model that incorporated the Johns Hopkins' Aggregated Diagnosis Groups. Logistical regression models using age, sex and the John Hopkins' Aggregated Diagnosis Groups were able to accurately predict 1-year mortality in population-based samples of patients with diabetes. © 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.

Predicting Commitment in Adult and Traditional-Age Students: Applying Rusbult's Investment Model to the Study of Retention.

ERIC Educational Resources Information Center

Cini, Marie A.; Fritz, Janie M. Harden

Rusbult's Investment Model, a theoretical model of commitment based on notions of social exchange and interdependence theory, was used to predict college commitment in traditional-age and adult college students. A questionnaire assessing rewards, costs, investments, alternatives, and commitment to college was administered to 216 traditional-age…

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

PubMed

Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

2017-05-22

PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer.

A test of the vulnerability model: temperament and temperament change as predictors of future mental disorders - the TRAILS study.

PubMed

Laceulle, Odilia M; Ormel, Johan; Vollebergh, Wilma A M; van Aken, Marcel A G; Nederhof, Esther

2014-03-01

This study aimed to test the vulnerability model of the relationship between temperament and mental disorders using a large sample of adolescents from the TRacking Adolescents Individual Lives' Survey (TRAILS). The vulnerability model argues that particular temperaments can place individuals at risk for the development of mental health problems. Importantly, the model may imply that not only baseline temperament predicts mental health problems prospectively, but additionally, that changes in temperament predict corresponding changes in risk for mental health problems. Data were used from 1195 TRAILS participants. Adolescent temperament was assessed both at age 11 and at age 16. Onset of mental disorders between age 16 and 19 was assessed at age 19, by means of the World Health Organization Composite International Diagnostic Interview (WHO CIDI). Results showed that temperament at age 11 predicted future mental disorders, thereby providing support for the vulnerability model. Moreover, temperament change predicted future mental disorders above and beyond the effect of basal temperament. For example, an increase in frustration increased the risk of mental disorders proportionally. This study confirms, and extends, the vulnerability model. Consequences of both temperament and temperament change were general (e.g., changes in frustration predicted both internalizing and externalizing disorders) as well as dimension specific (e.g., changes in fear predicted internalizing but not externalizing disorders). These findings confirm previous studies, which showed that mental disorders have both unique and shared underlying temperamental risk factors. © 2013 The Authors. Journal of Child Psychology and Psychiatry © 2013 Association for Child and Adolescent Mental Health.

A human life-stage physiologically based pharmacokinetic and pharmacodynamic model for chlorpyrifos: development and validation.

PubMed

Smith, Jordan Ned; Hinderliter, Paul M; Timchalk, Charles; Bartels, Michael J; Poet, Torka S

2014-08-01

Sensitivity to some chemicals in animals and humans are known to vary with age. Age-related changes in sensitivity to chlorpyrifos have been reported in animal models. A life-stage physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed to predict disposition of chlorpyrifos and its metabolites, chlorpyrifos-oxon (the ultimate toxicant) and 3,5,6-trichloro-2-pyridinol (TCPy), as well as B-esterase inhibition by chlorpyrifos-oxon in humans. In this model, previously measured age-dependent metabolism of chlorpyrifos and chlorpyrifos-oxon were integrated into age-related descriptions of human anatomy and physiology. The life-stage PBPK/PD model was calibrated and tested against controlled adult human exposure studies. Simulations suggest age-dependent pharmacokinetics and response may exist. At oral doses ⩾0.6mg/kg of chlorpyrifos (100- to 1000-fold higher than environmental exposure levels), 6months old children are predicted to have higher levels of chlorpyrifos-oxon in blood and higher levels of red blood cell cholinesterase inhibition compared to adults from equivalent doses. At lower doses more relevant to environmental exposures, simulations predict that adults will have slightly higher levels of chlorpyrifos-oxon in blood and greater cholinesterase inhibition. This model provides a computational framework for age-comparative simulations that can be utilized to predict chlorpyrifos disposition and biological response over various postnatal life stages. Copyright © 2013 Elsevier Inc. All rights reserved.

Estimation and application of a growth and yield model for uneven-aged mixed conifer stands in California.

Treesearch

Jingjing Liang; J. Buongiorno; R.A. Monserud

2005-01-01

A growth model for uneven-aged mixed-conifer stands in California was developed with data from 205 permanent plots. The model predicts the number of softwood and hardwood trees in nineteen diameter classes, based on equations for diameter growth rates, mortality arid recruitment. The model gave unbiased predictions of the expected number of trees by diameter class and...

Predation rates by North Sea cod (Gadus morhua) - Predictions from models on gastric evacuation and bioenergetics

USGS Publications Warehouse

Hansson, S.; Rudstam, L. G.; Kitchell, J.F.; Hilden, M.; Johnson, B.L.; Peppard, P.E.

1996-01-01

We compared four different methods for estimating predation rates by North Sea cod (Gadus moi hua). Three estimates, based on gastric evacuation rates, came from an ICES multispecies working group and the fourth from a bioenergetics model. The bioenergetics model was developed from a review of literature on cod physiology. The three gastric evacuation rate models produced very different prey consumption estimates for small (2 kg) fish. For most size and age classes, the bioenergetics model predicted food consumption rates intermediate to those predicted by the gastric evacuation models. Using the standard ICES model and the average population abundance and age structure for 1974-1989, annual, prey consumption by the North Sea cod population (age greater than or equal to 1) was 840 kilotons. The other two evacuation rate models produced estimates of 1020 and 1640 kilotons, respectively. The bioenergetics model estimate was 1420 kilotons. The major differences between models were due to consumption rate estimates for younger age groups of cod. (C) 1996 International Council for the Exploration of the Sea

Accelerated Changes in Cortical Thickness Measurements with Age in Military Service Members with Traumatic Brain Injury.

PubMed

Savjani, Ricky R; Taylor, Brian A; Acion, Laura; Wilde, Elisabeth A; Jorge, Ricardo E

2017-11-15

Finding objective and quantifiable imaging markers of mild traumatic brain injury (TBI) has proven challenging, especially in the military population. Changes in cortical thickness after injury have been reported in animals and in humans, but it is unclear how these alterations manifest in the chronic phase, and it is difficult to characterize accurately with imaging. We used cortical thickness measures derived from Advanced Normalization Tools (ANTs) to predict a continuous demographic variable: age. We trained four different regression models (linear regression, support vector regression, Gaussian process regression, and random forests) to predict age from healthy control brains from publicly available datasets (n = 762). We then used these models to predict brain age in military Service Members with TBI (n = 92) and military Service Members without TBI (n = 34). Our results show that all four models overpredicted age in Service Members with TBI, and the predicted age difference was significantly greater compared with military controls. These data extend previous civilian findings and show that cortical thickness measures may reveal an association of accelerated changes over time with military TBI.

Predicting Age-appropriate Pharmacokinetics of Six Volatile Organic Compounds in the Rat Utilizing Physiologically-based Pharmacokinetic Modeling (T)

EPA Science Inventory

The capability of physiologically-based pharmacokinetic (PBPK) models to incorporate ageappropriate physiological and chemical-specific parameters was utilized in this study to predict changes in internal dosimetry for six volatile organic compounds (VOCs) across different ages o...

Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks.

PubMed

Solis-Paredes, Mario; Estrada-Gutierrez, Guadalupe; Perichart-Perera, Otilia; Montoya-Estrada, Araceli; Guzmán-Huerta, Mario; Borboa-Olivares, Héctor; Bravo-Flores, Eyerahi; Cardona-Pérez, Arturo; Zaga-Clavellina, Veronica; Garcia-Latorre, Ethel; Gonzalez-Perez, Gabriela; Hernández-Pérez, José Alfredo; Irles, Claudine

2017-12-28

Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2'-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R² = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2'-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.

Age-dependent prevalence of nasopharyngeal carriage of streptococcus pneumoniae before conjugate vaccine introduction: a prediction model based on a meta-analysis.

PubMed

Le Polain de Waroux, Olivier; Flasche, Stefan; Prieto-Merino, David; Edmunds, W John

2014-01-01

Data on the prevalence of nasopharyngeal carriage of S.pneumoniae in all age groups are important to help predict the impact of introducing pneumococcal conjugate vaccines (PCV) into routine infant immunization, given the important indirect effect of the vaccine. Yet most carriage studies are limited to children under five years of age. We here explore the association between carriage prevalence and serotype distribution in children aged ≥5 years and in adults compared to children. We conducted a systematic review of studies providing carriage estimates across age groups in healthy populations not previously exposed to PCV, using MEDLINE and Embase. We used Bayesian linear meta-regression models to predict the overall carriage prevalence as well as the prevalence and distribution of vaccine and nonvaccine type (VT and NVT) serotypes in older age groups as a function of that in <5 y olds. Twenty-nine studies compromising of 20,391 individuals were included in the analysis. In all studies nasopharyngeal carriage decreased with increasing age. We found a strong positive linear association between the carriage prevalence in pre-school childen (<5 y) and both that in school aged children (5-17 y olds) and in adults. The proportion of VT serotypes isolated from carriers was consistently lower in older age groups and on average about 73% that of children <5 y among 5-17 y olds and adults respectively. We provide a prediction model to infer the carriage prevalence and serotype distribution in 5-17 y olds and adults as a function of that in children <5 years of age. Such predictions are helpful for assessing the potential population-wide effects of vaccination programmes, e.g. via transmission models, and thus assist in the design of future pneumococcal conjugate vaccination strategies.

Validation of maturity offset in a longitudinal sample of Polish girls.

PubMed

Malina, Robert M; Kozieł, Sławomir M

2014-01-01

This study attempted to validate an anthropometric equation for predicting age at peak height velocity (PHV) in 198 Polish girls followed longitudinally from 8 to 18 years. Maturity offset (years before or after PHV) was predicted from chronological age, mass, stature, sitting height and estimated leg length at each observation; predicted age at PHV was the difference between age and maturity offset. Actual age at PHV for each girl was derived with Preece-Baines Model 1. Predicted ages at PHV increased from 8 to16 years and varied relative to time before and after actual age at PHV. Predicted and actual ages at PHV did not differ at 9 years, but predicted overestimated actual age at PHV from 10 to 16 years. Girls of contrasting maturity status differed in predicted age at PHV from 8 to 14 years. In conclusion, predicted age at PHV is dependent upon age at prediction and individual differences in actual age at PHV, which limits its utility as an indicator of maturity timing in general and in sport talent programmes. It may have limited applicability as a categorical variable (pre-, post-PHV) among average maturing girls during the interval of the growth spurt, ~11.0-13.0 years.

Predicting gestational age using neonatal metabolic markers

PubMed Central

Ryckman, Kelli K.; Berberich, Stanton L.; Dagle, John M.

2016-01-01

Background Accurate gestational age estimation is extremely important for clinical care decisions of the newborn as well as for perinatal health research. Although prenatal ultrasound dating is one of the most accurate methods for estimating gestational age, it is not feasible in all settings. Identifying novel and accurate methods for gestational age estimation at birth is important, particularly for surveillance of preterm birth rates in areas without routine ultrasound dating. Objective We hypothesized that metabolic and endocrine markers captured by routine newborn screening could improve gestational age estimation in the absence of prenatal ultrasound technology. Study Design This is a retrospective analysis of 230,013 newborn metabolic screening records collected by the Iowa Newborn Screening Program between 2004 and 2009. The data were randomly split into a model-building dataset (n = 153,342) and a model-testing dataset (n = 76,671). We performed multiple linear regression modeling with gestational age, in weeks, as the outcome measure. We examined 44 metabolites, including biomarkers of amino acid and fatty acid metabolism, thyroid-stimulating hormone, and 17-hydroxyprogesterone. The coefficient of determination (R2) and the root-mean-square error were used to evaluate models in the model-building dataset that were then tested in the model-testing dataset. Results The newborn metabolic regression model consisted of 88 parameters, including the intercept, 37 metabolite measures, 29 squared metabolite measures, and 21 cubed metabolite measures. This model explained 52.8% of the variation in gestational age in the model-testing dataset. Gestational age was predicted within 1 week for 78% of the individuals and within 2 weeks of gestation for 95% of the individuals. This model yielded an area under the curve of 0.899 (95% confidence interval 0.895−0.903) in differentiating those born preterm (<37 weeks) from those born term (≥37 weeks). In the subset of infants born small-for-gestational age, the average difference between gestational ages predicted by the newborn metabolic model and the recorded gestational age was 1.5 weeks. In contrast, the average difference between gestational ages predicted by the model including only newborn weight and the recorded gestational age was 1.9 weeks. The estimated prevalence of preterm birth <37 weeks’ gestation in the subset of infants that were small for gestational age was 18.79% when the model including only newborn weight was used, over twice that of the actual prevalence of 9.20%. The newborn metabolic model underestimated the preterm birth prevalence at 6.94% but was closer to the prevalence based on the recorded gestational age than the model including only newborn weight. Conclusions The newborn metabolic profile, as derived from routine newborn screening markers, is an accurate method for estimating gestational age. In small-for-gestational age neonates, the newborn metabolic model predicts gestational age to a better degree than newborn weight alone. Newborn metabolic screening is a potentially effective method for population surveillance of preterm birth in the absence of prenatal ultrasound measurements or newborn weight. PMID:26645954

Predicting gestational age using neonatal metabolic markers.

PubMed

Ryckman, Kelli K; Berberich, Stanton L; Dagle, John M

2016-04-01

Accurate gestational age estimation is extremely important for clinical care decisions of the newborn as well as for perinatal health research. Although prenatal ultrasound dating is one of the most accurate methods for estimating gestational age, it is not feasible in all settings. Identifying novel and accurate methods for gestational age estimation at birth is important, particularly for surveillance of preterm birth rates in areas without routine ultrasound dating. We hypothesized that metabolic and endocrine markers captured by routine newborn screening could improve gestational age estimation in the absence of prenatal ultrasound technology. This is a retrospective analysis of 230,013 newborn metabolic screening records collected by the Iowa Newborn Screening Program between 2004 and 2009. The data were randomly split into a model-building dataset (n = 153,342) and a model-testing dataset (n = 76,671). We performed multiple linear regression modeling with gestational age, in weeks, as the outcome measure. We examined 44 metabolites, including biomarkers of amino acid and fatty acid metabolism, thyroid-stimulating hormone, and 17-hydroxyprogesterone. The coefficient of determination (R(2)) and the root-mean-square error were used to evaluate models in the model-building dataset that were then tested in the model-testing dataset. The newborn metabolic regression model consisted of 88 parameters, including the intercept, 37 metabolite measures, 29 squared metabolite measures, and 21 cubed metabolite measures. This model explained 52.8% of the variation in gestational age in the model-testing dataset. Gestational age was predicted within 1 week for 78% of the individuals and within 2 weeks of gestation for 95% of the individuals. This model yielded an area under the curve of 0.899 (95% confidence interval 0.895-0.903) in differentiating those born preterm (<37 weeks) from those born term (≥37 weeks). In the subset of infants born small-for-gestational age, the average difference between gestational ages predicted by the newborn metabolic model and the recorded gestational age was 1.5 weeks. In contrast, the average difference between gestational ages predicted by the model including only newborn weight and the recorded gestational age was 1.9 weeks. The estimated prevalence of preterm birth <37 weeks' gestation in the subset of infants that were small for gestational age was 18.79% when the model including only newborn weight was used, over twice that of the actual prevalence of 9.20%. The newborn metabolic model underestimated the preterm birth prevalence at 6.94% but was closer to the prevalence based on the recorded gestational age than the model including only newborn weight. The newborn metabolic profile, as derived from routine newborn screening markers, is an accurate method for estimating gestational age. In small-for-gestational age neonates, the newborn metabolic model predicts gestational age to a better degree than newborn weight alone. Newborn metabolic screening is a potentially effective method for population surveillance of preterm birth in the absence of prenatal ultrasound measurements or newborn weight. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Why abundant tropical tree species are phylogenetically old.

PubMed

Wang, Shaopeng; Chen, Anping; Fang, Jingyun; Pacala, Stephen W

2013-10-01

Neutral models of species diversity predict patterns of abundance for communities in which all individuals are ecologically equivalent. These models were originally developed for Panamanian trees and successfully reproduce observed distributions of abundance. Neutral models also make macroevolutionary predictions that have rarely been evaluated or tested. Here we show that neutral models predict a humped or flat relationship between species age and population size. In contrast, ages and abundances of tree species in the Panamanian Canal watershed are found to be positively correlated, which falsifies the models. Speciation rates vary among phylogenetic lineages and are partially heritable from mother to daughter species. Variable speciation rates in an otherwise neutral model lead to a demographic advantage for species with low speciation rate. This demographic advantage results in a positive correlation between species age and abundance, as found in the Panamanian tropical forest community.

A Feature-based Developmental Model of the Infant Brain in Structural MRI

PubMed Central

Toews, Matthew; Wells, William M.; Zöllei, Lilla

2014-01-01

In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days. PMID:23286050

How age and gender predict illness course in a first-episode nonaffective psychosis cohort.

PubMed

Drake, Richard J; Addington, Jean; Viswanathan, Ananth C; Lewis, Shôn W; Cotter, Jack; Yung, Alison R; Abel, Kathryn M

2016-03-01

Male gender and young age at onset of schizophrenia are traditionally associated with poor treatment outcome and often used to determine prognosis. However, many studies use nonincident samples and fail to adjust for symptom severity at onset. We hypothesized that age and gender would influence severity of presentation but would not predict outcome after adjustment for symptoms at presentation. 628 people with first-episode ICD-9 and DSM-IV nonaffective psychosis from 2 historical cohorts recruited from sequential presentations in Canada and the United Kingdom (1996-1998) were assessed prospectively at presentation and over 12-18 months using the Positive and Negative Syndrome Scale (PANSS). Models of the age-at-onset distributions with 2 underlying modes at similar ages in women (ages 23 years and 47 years) and men (ages 22 years and 46 years) had relatively good fits compared to single-mode models (χ(2)1 better by 9.2 for females, 8.0 for males, both P < .05). At presentation, scores for negative symptoms were 1.84 points worse for males (95% CI, 1.05 to 2.58; P < .001) in a mixed effects model. Younger age also predicted higher negative scores at presentation (partial correlation r = -0.18, P < .01; P < .001 in the mixed effects model). Findings were similar for cognitive-disorganized symptoms. However, after controlling for baseline symptoms, age at onset and gender did not significantly predict subsequent symptom course in the mixed effects models. Gender and age at onset are independently associated with symptoms at presentation but not with medium-term course of schizophrenia. This finding reinforces the importance of early identification and prevention of severe negative symptoms at first episode, whatever an individual's age and gender. © Copyright 2016 Physicians Postgraduate Press, Inc.

Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients

PubMed Central

Pfeiffer, Ruth M.; Miglioretti, Diana L.; Kerlikowske, Karla; Tice, Jeffery; Vacek, Pamela M.; Gierach, Gretchen L.

2016-01-01

Purpose Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown. Methods Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996–2007), we estimated 5-year breast cancer risk (<1%; 1–1.66%; ≥1.67%) with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions); Breast Cancer Risk Assessment Tool (BCRAT); and BCSC 5-year risk model (BCSC-5). Breast cancer-specific mortality post-diagnosis (range: 1–13 years; median: 5.4–5.6 years) was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35–44; 45–54; 55–69; 70–89 years) models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years. Results Of 6,021 deaths, 2,993 (49.7%) were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR) = 0.82 (95% CI = 0.75–0.90); BCRAT: HR = 0.72 (95% CI = 0.65–0.81) and BCSC-5: HR = 0.84 (95% CI = 0.75–0.94). Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55–69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35–44 years. Conclusions Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering women counseling, it may be useful to note that high predicted risk of developing breast cancer does not imply that if cancer develops it will behave aggressively. PMID:27560501

Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment

NASA Astrophysics Data System (ADS)

Zell, Wesley O.; Culver, Teresa B.; Sanford, Ward E.

2018-06-01

Uncertainties about the age of base-flow discharge can have serious implications for the management of degraded environmental systems where subsurface pathways, and the ongoing release of pollutants that accumulated in the subsurface during past decades, dominate the water quality signal. Numerical groundwater models may be used to estimate groundwater return times and base-flow ages and thus predict the time required for stakeholders to see the results of improved agricultural management practices. However, the uncertainty inherent in the relationship between (i) the observations of atmospherically-derived tracers that are required to calibrate such models and (ii) the predictions of system age that the observations inform have not been investigated. For example, few if any studies have assessed the uncertainty of numerically-simulated system ages or evaluated the uncertainty reductions that may result from the expense of collecting additional subsurface tracer data. In this study we combine numerical flow and transport modeling of atmospherically-derived tracers with prediction uncertainty methods to accomplish four objectives. First, we show the relative importance of head, discharge, and tracer information for characterizing response times in a uniquely data rich catchment that includes 266 age-tracer measurements (SF6, CFCs, and 3H) in addition to long term monitoring of water levels and stream discharge. Second, we calculate uncertainty intervals for model-simulated base-flow ages using both linear and non-linear methods, and find that the prediction sensitivity vector used by linear first-order second-moment methods results in much larger uncertainties than non-linear Monte Carlo methods operating on the same parameter uncertainty. Third, by combining prediction uncertainty analysis with multiple models of the system, we show that data-worth calculations and monitoring network design are sensitive to variations in the amount of water leaving the system via stream discharge and irrigation withdrawals. Finally, we demonstrate a novel model-averaged computation of potential data worth that can account for these uncertainties in model structure.

Empirically Based Composite Fracture Prediction Model From the Global Longitudinal Study of Osteoporosis in Postmenopausal Women (GLOW)

PubMed Central

Compston, Juliet E.; Chapurlat, Roland D.; Pfeilschifter, Johannes; Cooper, Cyrus; Hosmer, David W.; Adachi, Jonathan D.; Anderson, Frederick A.; Díez-Pérez, Adolfo; Greenspan, Susan L.; Netelenbos, J. Coen; Nieves, Jeri W.; Rossini, Maurizio; Watts, Nelson B.; Hooven, Frederick H.; LaCroix, Andrea Z.; March, Lyn; Roux, Christian; Saag, Kenneth G.; Siris, Ethel S.; Silverman, Stuart; Gehlbach, Stephen H.

2014-01-01

Context: Several fracture prediction models that combine fractures at different sites into a composite outcome are in current use. However, to the extent individual fracture sites have differing risk factor profiles, model discrimination is impaired. Objective: The objective of the study was to improve model discrimination by developing a 5-year composite fracture prediction model for fracture sites that display similar risk profiles. Design: This was a prospective, observational cohort study. Setting: The study was conducted at primary care practices in 10 countries. Patients: Women aged 55 years or older participated in the study. Intervention: Self-administered questionnaires collected data on patient characteristics, fracture risk factors, and previous fractures. Main Outcome Measure: The main outcome is time to first clinical fracture of hip, pelvis, upper leg, clavicle, or spine, each of which exhibits a strong association with advanced age. Results: Of four composite fracture models considered, model discrimination (c index) is highest for an age-related fracture model (c index of 0.75, 47 066 women), and lowest for Fracture Risk Assessment Tool (FRAX) major fracture and a 10-site model (c indices of 0.67 and 0.65). The unadjusted increase in fracture risk for an additional 10 years of age ranges from 80% to 180% for the individual bones in the age-associated model. Five other fracture sites not considered for the age-associated model (upper arm/shoulder, rib, wrist, lower leg, and ankle) have age associations for an additional 10 years of age from a 10% decrease to a 60% increase. Conclusions: After examining results for 10 different bone fracture sites, advanced age appeared the single best possibility for uniting several different sites, resulting in an empirically based composite fracture risk model. PMID:24423345

Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks.

PubMed

Sun, X; Chen, K J; Berg, E P; Newman, D J; Schwartz, C A; Keller, W L; Maddock Carlin, K R

2014-02-01

The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n = 102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat. © 2013.

US/UK second level panel discussions on the health and value of: Ageing and lifetime predictions (u)

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

Castro, Richard G

2011-01-18

Many healthy physics, engineering, and materials exchanges are being accomplished in ageing and lifetime prediction that directly supports US and UK Stockpile Management Programs. Lifetime assessment studies of silicon foams under compression - Joint AWE/LANLlLLNL study of compression set in stress cushions completed. Provides phenomenological prediction out to 50 years. Polymer volatile out-gassing studies - New exchange on the out-gassing of Ethylene Vinyl Acetate (EVA) using isotopic {sup 13}C labeling studies to interrogate mechanistic processes. Infra-red (IR) gas cell analytical capabilities developed by AWE will be used to monitor polymer out-gassing profiles. Pu Strength ageing Experiments and Constitutive Modeling -more » In recently compared modeling strategies for ageing effects on Pu yield strength at high strain rates, a US/UK consensus was reached on the general principle that the ageing effect is additive and not multiplicative. The fundamental mechanisms for age-strengthening in Pu remains unknown. Pu Surface and Interface Reactions - (1) US/UK secondment resulted in developing a metal-metal oxide model for radiation damaged studies consistent with a Modified Embedded Atom Method (MEAM) potential; and (2) Joint US/UK collaboration to study the role of impurities in hydride initiation. Detonator Ageing (wide range of activities) - (1) Long-term ageing study with field trials at Pantex incorporating materials from LANL, LLNL, SNL and AWE; (2) Characterization of PETN growth to detonation process; (3) Detonator performance modeling; and (4) Performance fault tree analysis. Benefits are a unified approach to lifetime prediction that Includes: materials characterization and the development of ageing models through improved understanding of the relationship between materials properties, ageing properties and detonator performance.« less

Examination of DNA methylation status of the ELOVL2 marker may be useful for human age prediction in forensic science.

PubMed

Zbieć-Piekarska, Renata; Spólnicka, Magdalena; Kupiec, Tomasz; Makowska, Żanetta; Spas, Anna; Parys-Proszek, Agnieszka; Kucharczyk, Krzysztof; Płoski, Rafał; Branicki, Wojciech

2015-01-01

Age estimation in forensic investigations may complement the prediction of externally visible characteristics and the inference of biogeographical ancestry, thus allowing a better description of an unknown individual. Multiple CpG sites that show linear correlation between age and degree of DNA methylation have been identified in the human genome, providing a selection of candidates for age prediction. In this study, we optimized an assay based on bisulfite conversion and pyrosequencing of 7 CpG sites located in the ELOVL2 gene. Examination of 303 blood samples collected from individuals aged 2-75 years allowed selection of the most informative site, explaining 83% of variation in age. The final linear regression model included two CpG sites in ELOVL2 and enabled age prediction with R(2)=0.859, prediction error=6.85 and mean absolute deviation MAD=5.03. Examination of a testing set of 124 blood samples (MAD=5.75) showed that 68.5% of samples were correctly predicted, assuming that chronological and predicted ages matched ± 7 years. It was found that the ELOVL2 methylation status in bloodstains had not changed significantly after 4 weeks of storage in room temperature conditions. Analysis of 45 bloodstains deposited on tissue paper after 5, 10 and 15 years of storage in room conditions indicated that although a gradual decrease of positive PCR results was observed, the general age prediction success rate remained similar and equaled 60-78%. The obtained results show that the ELOVL2 locus provides a very good source of information about human chronological age based on analysis of blood, including bloodstains, and it may constitute a powerful and reliable predictor in future forensic age estimation models. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Intensity level for exercise training in fibromyalgia by using mathematical models.

PubMed

Lemos, Maria Carolina D; Valim, Valéria; Zandonade, Eliana; Natour, Jamil

2010-03-22

It has not been assessed before whether mathematical models described in the literature for prescriptions of exercise can be used for fibromyalgia syndrome patients. The objective of this paper was to determine how age-predicted heart rate formulas can be used with fibromyalgia syndrome populations as well as to find out which mathematical models are more accurate to control exercise intensity. A total of 60 women aged 18-65 years with fibromyalgia syndrome were included; 32 were randomized to walking training at anaerobic threshold. Age-predicted formulas to maximum heart rate ("220 minus age" and "208 minus 0.7 x age") were correlated with achieved maximum heart rate (HRMax) obtained by spiroergometry. Subsequently, six mathematical models using heart rate reserve (HRR) and age-predicted HRMax formulas were studied to estimate the intensity level of exercise training corresponding to heart rate at anaerobic threshold (HRAT) obtained by spiroergometry. Linear and nonlinear regression models were used for correlations and residues analysis for the adequacy of the models. Age-predicted HRMax and HRAT formulas had a good correlation with achieved heart rate obtained in spiroergometry (r = 0.642; p < 0.05). For exercise prescription in the anaerobic threshold intensity, the percentages were 52.2-60.6% HRR and 75.5-80.9% HRMax. Formulas using HRR and the achieved HRMax showed better correlation. Furthermore, the percentages of HRMax and HRR were significantly higher for the trained individuals (p < 0.05). Age-predicted formulas can be used for estimating HRMax and for exercise prescriptions in women with fibromyalgia syndrome. Karnoven's formula using heart rate achieved in ergometric test showed a better correlation. For the prescription of exercises in the threshold intensity, 52% to 60% HRR or 75% to 80% HRMax must be used in sedentary women with fibromyalgia syndrome and these values are higher and must be corrected for trained patients.

Intensity level for exercise training in fibromyalgia by using mathematical models

PubMed Central

2010-01-01

Background It has not been assessed before whether mathematical models described in the literature for prescriptions of exercise can be used for fibromyalgia syndrome patients. The objective of this paper was to determine how age-predicted heart rate formulas can be used with fibromyalgia syndrome populations as well as to find out which mathematical models are more accurate to control exercise intensity. Methods A total of 60 women aged 18-65 years with fibromyalgia syndrome were included; 32 were randomized to walking training at anaerobic threshold. Age-predicted formulas to maximum heart rate ("220 minus age" and "208 minus 0.7 × age") were correlated with achieved maximum heart rate (HRMax) obtained by spiroergometry. Subsequently, six mathematical models using heart rate reserve (HRR) and age-predicted HRMax formulas were studied to estimate the intensity level of exercise training corresponding to heart rate at anaerobic threshold (HRAT) obtained by spiroergometry. Linear and nonlinear regression models were used for correlations and residues analysis for the adequacy of the models. Results Age-predicted HRMax and HRAT formulas had a good correlation with achieved heart rate obtained in spiroergometry (r = 0.642; p < 0.05). For exercise prescription in the anaerobic threshold intensity, the percentages were 52.2-60.6% HRR and 75.5-80.9% HRMax. Formulas using HRR and the achieved HRMax showed better correlation. Furthermore, the percentages of HRMax and HRR were significantly higher for the trained individuals (p < 0.05). Conclusion Age-predicted formulas can be used for estimating HRMax and for exercise prescriptions in women with fibromyalgia syndrome. Karnoven's formula using heart rate achieved in ergometric test showed a better correlation. For the prescription of exercises in the threshold intensity, 52% to 60% HRR or 75% to 80% HRMax must be used in sedentary women with fibromyalgia syndrome and these values are higher and must be corrected for trained patients. PMID:20307323

[Risk factor analysis of the patients with solitary pulmonary nodules and establishment of a prediction model for the probability of malignancy].

PubMed

Wang, X; Xu, Y H; Du, Z Y; Qian, Y J; Xu, Z H; Chen, R; Shi, M H

2018-02-23

Objective: This study aims to analyze the relationship among the clinical features, radiologic characteristics and pathological diagnosis in patients with solitary pulmonary nodules, and establish a prediction model for the probability of malignancy. Methods: Clinical data of 372 patients with solitary pulmonary nodules who underwent surgical resection with definite postoperative pathological diagnosis were retrospectively analyzed. In these cases, we collected clinical and radiologic features including gender, age, smoking history, history of tumor, family history of cancer, the location of lesion, ground-glass opacity, maximum diameter, calcification, vessel convergence sign, vacuole sign, pleural indentation, speculation and lobulation. The cases were divided to modeling group (268 cases) and validation group (104 cases). A new prediction model was established by logistic regression analying the data from modeling group. Then the data of validation group was planned to validate the efficiency of the new model, and was compared with three classical models(Mayo model, VA model and LiYun model). With the calculated probability values for each model from validation group, SPSS 22.0 was used to draw the receiver operating characteristic curve, to assess the predictive value of this new model. Results: 112 benign SPNs and 156 malignant SPNs were included in modeling group. Multivariable logistic regression analysis showed that gender, age, history of tumor, ground -glass opacity, maximum diameter, and speculation were independent predictors of malignancy in patients with SPN( P <0.05). We calculated a prediction model for the probability of malignancy as follow: p =e(x)/(1+ e(x)), x=-4.8029-0.743×gender+ 0.057×age+ 1.306×history of tumor+ 1.305×ground-glass opacity+ 0.051×maximum diameter+ 1.043×speculation. When the data of validation group was added to the four-mathematical prediction model, The area under the curve of our mathematical prediction model was 0.742, which is greater than other models (Mayo 0.696, VA 0.634, LiYun 0.681), while the differences between any two of the four models were not significant ( P >0.05). Conclusions: Age of patient, gender, history of tumor, ground-glass opacity, maximum diameter and speculation are independent predictors of malignancy in patients with solitary pulmonary nodule. This logistic regression prediction mathematic model is not inferior to those classical models in estimating the prognosis of SPNs.

Application of First Principles Model to Spacecraft Operations

NASA Technical Reports Server (NTRS)

Timmerman, Paul; Bugga, Ratnakumar; DiStefano, Salvidor

1996-01-01

Previous models use a single phase reaction; cycled cell predicts cannot be met with a single phase; interphase conversion provides means for film aging; aging cells predictions display typical behaviors: pressure changes in NiH² cells; voltage fading upon cycling; second plateau on discharge of cycled cells; negative limited behavior for Ni-Cds.

Depression and Delinquency Covariation in an Accelerated Longitudinal Sample of Adolescents

PubMed Central

Kofler, Michael J.; McCart, Michael R.; Zajac, Kristyn; Ruggiero, Kenneth J.; Saunders, Benjamin E.; Kilpatrick, Dean G.

2015-01-01

Objectives The current study tested opposing predictions stemming from the failure and acting out theories of depression-delinquency covariation. Methods Participants included a nationwide longitudinal sample of adolescents (N = 3,604) ages 12 to 17. Competing models were tested using cohort-sequential latent growth curve modeling to determine whether depressive symptoms at age 12 (baseline) predicted concurrent and age-related changes in delinquent behavior, whether the opposite pattern was apparent (delinquency predicting depression), and whether initial levels of depression predict changes in delinquency significantly better than vice versa. Results Early depressive symptoms predicted age-related changes in delinquent behavior significantly better than early delinquency predicted changes in depressive symptoms. In addition, the impact of gender on age-related changes in delinquent symptoms was mediated by gender differences in depressive symptom changes, indicating that depressive symptoms are a particularly salient risk factor for delinquent behavior in girls. Conclusion Early depressive symptoms represent a significant risk factor for later delinquent behavior – especially for girls – and appear to be a better predictor of later delinquency than early delinquency is of later depression. These findings provide support for the acting out theory and contradict failure theory predictions. PMID:21787049

A Comparison of Tension and Compression Creep in a Polymeric Composite and the Effects of Physical Aging on Creep Behavior

NASA Technical Reports Server (NTRS)

Gates, Thomas S.; Veazie, David R.; Brinson, L. Catherine

1996-01-01

Experimental and analytical methods were used to investigate the similarities and differences of the effects of physical aging on creep compliance of IM7/K3B composite loaded in tension and compression. Two matrix dominated loading modes, shear and transverse, were investigated for two load cases, tension and compression. The tests, run over a range of sub-glass transition temperatures, provided material constants, material master curves and aging related parameters. Comparing results from the short-term data indicated that although trends in the data with respect to aging time and aging temperature are similar, differences exist due to load direction and mode. The analytical model used for predicting long-term behavior using short-term data as input worked equally as well for the tension or compression loaded cases. Comparison of the loading modes indicated that the predictive model provided more accurate long term predictions for the shear mode as compared to the transverse mode. Parametric studies showed the usefulness of the predictive model as a tool for investigating long-term performance and compliance acceleration due to temperature.

Risk Factors in Preschool Children for Predicting Asthma During the Preschool Age and the Early School Age: a Systematic Review and Meta-Analysis.

PubMed

Bao, Yixia; Chen, Zhimin; Liu, Enmei; Xiang, Li; Zhao, Deyu; Hong, Jianguo

2017-11-18

The aim of this study was to identify risk factors of asthma among children < 6 years old (preschool age) for predicting asthma during the preschool age and early school age (≤ 10 years of age). MEDLINE, Cochrane, EMBASE, and Google Scholar databases were searched until June 30, 2017. Prospective or retrospective cohort and case-control studies were included. Studies had to have evaluated risk factors or a predictive model for developing asthma in children ≤ 6 years of age or persistent asthma in early school age. A total of 17 studies were included in the analysis. Factors associated with developing asthma in children ≤ 10 years of age (both pre-school and early school age) included male gender (pooled OR = 1.70, P < 0.001), atopic dermatitis (pooled OR = 2.02, P < 0.001), a family history of asthma (pooled OR = 2.20, P < 0.001), and serum IgE levels ≥ 60 kU/l or having specific IgE (pooled OR = 2.36, P < 0.001). A history of exposure to smoke or wheezing was also associated with persistent asthma in early school age (pooled OR = 1.51, P = 0.030 and pooled OR = 2.59, P < 0.001, respectively). In general, asthma predictive models (e.g., API, PIAMA, PAPS) had relatively low sensitivity (range, 21% to 71.4%) but high specificity (range, 69% to 98%). The study found that male gender, exposure to smoke, atopic dermatitis, family history of asthma, history of wheezing, and serum IgE level ≥ 60 kU/l or having specific IgE were significantly associated with developing asthma by either preschool or early school age. Asthma predictive models can be developed by those risk factors.

Prostate health index (PHI) and prostate-specific antigen (PSA) predictive models for prostate cancer in the Chinese population and the role of digital rectal examination-estimated prostate volume.

PubMed

Chiu, Peter K F; Roobol, Monique J; Teoh, Jeremy Y; Lee, Wai-Man; Yip, Siu-Ying; Hou, See-Ming; Bangma, Chris H; Ng, Chi-Fai

2016-10-01

To investigate PSA- and PHI (prostate health index)-based models for prediction of prostate cancer (PCa) and the feasibility of using DRE-estimated prostate volume (DRE-PV) in the models. This study included 569 Chinese men with PSA 4-10 ng/mL and non-suspicious DRE with transrectal ultrasound (TRUS) 10-core prostate biopsies performed between April 2008 and July 2015. DRE-PV was estimated using 3 pre-defined classes: 25, 40, or 60 ml. The performance of PSA-based and PHI-based predictive models including age, DRE-PV, and TRUS prostate volume (TRUS-PV) was analyzed using logistic regression and area under the receiver operating curves (AUC), in both the whole cohort and the screening age group of 55-75. PCa and high-grade PCa (HGPCa) was diagnosed in 10.9 % (62/569) and 2.8 % (16/569) men, respectively. The performance of DRE-PV-based models was similar to TRUS-PV-based models. In the age group 55-75, the AUCs for PCa of PSA alone, PSA with DRE-PV and age, PHI alone, PHI with DRE-PV and age, and PHI with TRUS-PV and age were 0.54, 0.71, 0.76, 0.78, and 0.78, respectively. The corresponding AUCs for HGPCa were higher (0.60, 0.70, 0.85, 0.83, and 0.83). At 10 and 20 % risk threshold for PCa, 38.4 and 55.4 % biopsies could be avoided in the PHI-based model, respectively. PHI had better performance over PSA-based models and could reduce unnecessary biopsies. A DRE-assessed PV can replace TRUS-assessed PV in multivariate prediction models to facilitate clinical use.

Experimental characterization and modeling of isothermal and nonisothermal physical aging in glassy polymer films

NASA Astrophysics Data System (ADS)

Guo, Yunlong

This dissertation focuses on nonisothermal physical aging of polymers from both experimental and theoretical aspects. The study concentrates on pure polymers rather than fiber-reinforced composites; this step removes several complicating factors to simplify the study. It is anticipated that the findings of this work can then be applied to composite materials applications. The physical aging tests in this work are performed using a dynamic mechanical analyzer (DMA). The viscoelastic response of glassy polymers under various loading and thermal histories are observed as stress-strain data at a series of time points. The first stage of the experimental work involves the characterization of the isothermal physical aging behavior of two advanced thermoplastics. The second stage conducts tests on the same materials with varying thermal histories and with long-term test duration. This forms the basis to assess and modify a nonisothermal physical aging model (KAHR-ate model). Based on the experimental findings, the KAHR-ate model has been revised by new correlations between aging shift factors and volume response; this revised model performed well in predicting the nonisothermal physical aging behavior of glassy polymers. In the work on isothermal physical aging, short-term creep and stress relaxation tests were performed at several temperatures within 15-35°C below the glass transition temperature (Tg) at various aging times, using the short-term test method established by Struik. Stress and strain levels were such that the materials remained in the linear viscoelastic regime. These curves were then shifted together to determine momentary master curves and shift rates. In order to validate the obtained isothermal physical aging behavior, the results of creep and stress relaxation testing were compared and shown to be consistent with one another using appropriate interconversion of the viscoelastic material functions. Time-temperature superposition of the master curves was also performed. The temperature shift factors and aging shift rates for both PEEK and PPS were consistent for both creep and stress relaxation test results. Nonisothermal physical aging was monitored by sequential short-term creep tests after a series of temperature jumps; the resulting strain histories were analyzed to determine aging shift factors (ate) for each of the creep tests. The nonisothermal aging response was predicted using the KAHR-ate model, which combines the KAHR model of volume recovery with a suitable linear relationship between aging shift factors and specific volume. The KAHR-ate model can be utilized to both predict aging response and to determine necessary model parameters from a set of aging shift factor data. For the PEEK and PPS materials considered in the current study, predictions of mechanical response were demonstrated to be in good agreement with the experimental results for several complicated thermal histories. In addition to short-term nonisothermal aging, long-term creep tests under identical thermal conditions were also analyzed. Effective time theory was unitized to predict long-term response under both isothermal and nonisothermal temperature histories. The long-term compliance after a series of temperature changes was predicted by the KAHR- ate model, and the theoretical predictions and experimental data showed good agreement for various thermal histories. Lastly, physical aging behavior of PPS near the glass transition temperature was investigated, in order to observe the mechanical response in the process of the evolution of the material into equilibrium. At several temperatures near Tg, the time need to reach equilibrium were determined by the creep test results at various aging times. In addition to isothermal physical aging, mechanical shift factors in the period of approaching equilibrium at a common temperature after temperature up-jumps and down-jumps are monitored from creep tests; prior to these temperature jumps, the materials were aged to reach equilibrium states. From these tests, asymmetry of approaching equilibrium phenomenon in ate was observed, which is first-time reported in the literature. This finding shows the similarity between the thermodynamic and mechanical properties during structural relaxation. This work will lead to improved understanding of the viscoelastic behavior of glassy polymers, which is important for better understanding and design of PMCs in elevated temperature applications. With the above findings, this dissertation deals with nonisothermal physical aging of glassy polymers, including both experimental characterization and constructing a framework for predictions of mechanical behavior of polymeric materials under complicated thermal conditions. (Abstract shortened by UMI.)

Prediction of battery storage ageing and solid electrolyte interphase property estimation using an electrochemical model

NASA Astrophysics Data System (ADS)

Ashwin, T. R.; Barai, A.; Uddin, K.; Somerville, L.; McGordon, A.; Marco, J.

2018-05-01

Ageing prediction is often complicated due to the interdependency of ageing mechanisms. Research has highlighted that storage ageing is not linear with time. Capacity loss due to storing the battery at constant temperature can shed more light on parametrising the properties of the Solid Electrolyte Interphase (SEI); the identification of which, using an electrochemical model, is systematically addressed in this work. A new methodology is proposed where any one of the available storage ageing datasets can be used to find the property of the SEI layer. A sensitivity study is performed with different molecular mass and densities which are key parameters in modelling the thickness of the SEI deposit. The conductivity is adjusted to fine tune the rate of capacity fade to match experimental results. A correlation is fitted for the side reaction variation to capture the storage ageing in the 0%-100% SoC range. The methodology presented in this paper can be used to predict the unknown properties of the SEI layer which is difficult to measure experimentally. The simulation and experimental results show that the storage ageing model shows good accuracy for the cases at 50% and 90% and an acceptable agreement at 20% SoC.

Anthropometric predictors of body fat in a large population of 9-year-old school-aged children.

PubMed

Almeida, Sílvia M; Furtado, José M; Mascarenhas, Paulo; Ferraz, Maria E; Silva, Luís R; Ferreira, José C; Monteiro, Mariana; Vilanova, Manuel; Ferraz, Fernando P

2016-09-01

To develop and cross-validate predictive models for percentage body fat (%BF) from anthropometric measurements [including BMI z -score (zBMI) and calf circumference (CC)] excluding skinfold thickness. A descriptive study was carried out in 3,084 pre-pubertal children. Regression models and neural network were developed with %BF measured by Bioelectrical Impedance Analysis (BIA) as the dependent variables and age, sex and anthropometric measurements as independent predictors. All %BF grade predictive models presented a good global accuracy (≥91.3%) for obesity discrimination. Both overfat/obese and obese prediction models presented respectively good sensitivity (78.6% and 71.0%), specificity (98.0% and 99.2%) and reliability for positive or negative test results (≥82% and ≥96%). For boys, the order of parameters, by relative weight in the predictive model, was zBMI, height, waist-circumference-to-height-ratio (WHtR) squared variable (_Q), age, weight, CC_Q and hip circumference (HC)_Q (adjusted r 2  = 0.847 and RMSE = 2.852); for girls it was zBMI, WHtR_Q, height, age, HC_Q and CC_Q (adjusted r 2  = 0.872 and RMSE = 2.171). %BF can be graded and predicted with relative accuracy from anthropometric measurements excluding skinfold thickness. Fitness and cross-validation results showed that our multivariable regression model performed better in this population than did some previously published models.

Random glucose is useful for individual prediction of type 2 diabetes: results of the Study of Health in Pomerania (SHIP).

PubMed

Kowall, Bernd; Rathmann, Wolfgang; Giani, Guido; Schipf, Sabine; Baumeister, Sebastian; Wallaschofski, Henri; Nauck, Matthias; Völzke, Henry

2013-04-01

Random glucose is widely used in routine clinical practice. We investigated whether this non-standardized glycemic measure is useful for individual diabetes prediction. The Study of Health in Pomerania (SHIP), a population-based cohort study in north-east Germany, included 3107 diabetes-free persons aged 31-81 years at baseline in 1997-2001. 2475 persons participated at 5-year follow-up and gave self-reports of incident diabetes. For the total sample and for subjects aged ≥50 years, statistical properties of prediction models with and without random glucose were compared. A basic model (including age, sex, diabetes of parents, hypertension and waist circumference) and a comprehensive model (additionally including various lifestyle variables and blood parameters, but not HbA1c) performed statistically significantly better after adding random glucose (e.g., the area under the receiver-operating curve (AROC) increased from 0.824 to 0.856 after adding random glucose to the comprehensive model in the total sample). Likewise, adding random glucose to prediction models which included HbA1c led to significant improvements of predictive ability (e.g., for subjects ≥50 years, AROC increased from 0.824 to 0.849 after adding random glucose to the comprehensive model+HbA1c). Random glucose is useful for individual diabetes prediction, and improves prediction models including HbA1c. Copyright © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Modeling the formation and aging of secondary organic aerosols during CalNex 2010

NASA Astrophysics Data System (ADS)

Hayes, P. L.; Ortega, A. M.; Ahmadov, R.; McKeen, S. A.; Washenfelder, R. A.; Alvarez, S.; Rappenglueck, B.; Holloway, J. S.; Gilman, J. B.; Kuster, W. C.; De Gouw, J. A.; Zotter, P.; Prevot, A. S.; Kleindienst, T. E.; Offenberg, J. H.; Jimenez, J. L.

2012-12-01

Several traditional and recently proposed models are applied to predict the concentrations and properties of secondary organic aerosols (SOA) and organic gases at the Pasadena ground site during the CalNex campaign. The models are constrained with and compared against results from available observations. The CalNex campaign and specifically the Pasadena ground site featured a large and sophisticated suite of aerosol and gas phase instrumentation, and thus, it provides a unique opportunity to test SOA models under conditions of strong urban emissions at a range of low photochemical ages. The oxidation of volatile organic compounds (VOCs) using an updated traditional model cannot explain the observed ambient SOA, and under-predicts the measurements by a factor of ~40. Similarly, after accounting for the multi-generation oxidation of VOCs using a volatility basis set (VBS) approach as described by Tsimpidi et al. (2010), SOA is still under-predicted by a factor of ~8. For SOA formed from VOCs (V-SOA) the dominant precursors are aromatics (xylenes, toluene, and trimethylbenzenes). The model SOA formed from the oxidation of primary semivolatile and intermediate volatility organic compounds (P-S/IVOCs, producing SI-SOA) is also predicted using the parameterizations of Robinson et al. (2007) and Grieshop et al. (2009), and the properties of V-SOA + SI-SOA are compared against the measured O:C and volatility. We also compare the results of the different models against fossil/non-fossil carbon measurements as well as tracers of different SOA precursors. Potential Aerosol Mass (PAM) measurements of the SOA forming potential of the Pasadena air masses are also compared against that predicted by the models. The PAM analysis allows for model/measurement comparisons of SOA properties over a range of photochemical ages spanning almost two weeks. Using the V-SOA model, at low photochemical ages (< 1 day) the modeled PAM V-SOA is less than the measured PAM SOA, similar to the ambient results. In contrast, at high photochemical ages (i.e., more than about three days) the modeled PAM V-SOA is substantially greater than that measured, which is likely due fragmentation reactions that are not included in that model. We derive a parameterization of the measured PAM SOA as a function of the input photochemical age and the PAM photochemical age that serves as a comparison with other SOA models.

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

Smith, Jordan N.; Hinderliter, Paul M.; Timchalk, Charles

Sensitivity to chemicals in animals and humans are known to vary with age. Age-related changes in sensitivity to chlorpyrifos have been reported in animal models. A life-stage physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model was developed to computationally predict disposition of CPF and its metabolites, chlorpyrifos-oxon (the ultimate toxicant) and 3,5,6-trichloro-2-pyridinol (TCPy), as well as B-esterase inhibition by chlorpyrifos-oxon in humans. In this model, age-dependent body weight was calculated from a generalized Gompertz function, and compartments (liver, brain, fat, blood, diaphragm, rapid, and slow) were scaled based on body weight from polynomial functions on a fractional body weight basis. Bloodmore » flows among compartments were calculated as a constant flow per compartment volume. The life-stage PBPK/PD model was calibrated and tested against controlled adult human exposure studies. Model simulations suggest age-dependent pharmacokinetics and response may exist. At oral doses ≥ 0.55 mg/kg of chlorpyrifos (significantly higher than environmental exposure levels), 6 mo old children are predicted to have higher levels of chlorpyrifos-oxon in blood and higher levels of red blood cell cholinesterase inhibition compared to adults from equivalent oral doses of chlorpyrifos. At lower doses that are more relevant to environmental exposures, the model predicts that adults will have slightly higher levels of chlorpyrifos-oxon in blood and greater cholinesterase inhibition. This model provides a computational framework for age-comparative simulations that can be utilized to predict CPF disposition and biological response over various postnatal life-stages.« less

Age-related DNA methylation changes for forensic age-prediction.

PubMed

Yi, Shao Hua; Jia, Yun Shu; Mei, Kun; Yang, Rong Zhi; Huang, Dai Xin

2015-03-01

There is no available method of age-prediction for biological samples. The accumulating evidences indicate that DNA methylation patterns change with age. Aging resembles a developmentally regulated process that is tightly controlled by specific epigenetic modifications and age-associated methylation changes exist in human genome. In this study, three age-related methylation fragments were isolated and identified in blood of 40 donors. Age-related methylation changes with each fragment was validated and replicated in a general population sample of 65 donors over a wide age range (11-72 years). Methylation of these fragments is linearly correlated with age over a range of six decades (r = 0.80-0.88). Using average methylation of CpG sites of three fragments, a regression model that explained 95 % of the variance in age was built and is able to predict an individual's age with great accuracy (R (2 )= 0.93). The predicted value is highly correlated with the observed age in the sample (r = 0.96) and has great accuracy of average 4 years difference between predicted age and true age. This study implicates that DNA methylation can be an available biological marker of age-prediction. Further measurement of relevant markers in the genome could be a tool in routine screening to predict age of forensic biological samples.

The Addicted-Self Model of addictive behavior cessation: does it predict recovery for gender, ethnic, age and drug preference populations?

PubMed

Fiorentine, Robert; Hillhouse, Maureen P

2004-01-01

Although previous research provided empirical support for the main assumptions of the Addicted-Self (A-S) Model of recovery, it is not known whether the model predicts recovery for various gender, ethnic, age, and drug preference populations. It may be that the model predicts recovery only for some groups of addicts and should not be viewed as a general theory of the recovery process. Addressing this concern using data from the Los Angeles Target Cities Drug Treatment Enhancement Project, it was determined that only trivial population differences exist in the primary variables associated with the A-S Model. The A-S Model predicts abstinence with about the same degree of accuracy and parsimony for all populations. The findings indicate that the A-S Model is a general theory of drug and alcohol addictive behavior cessation.

Gamma Prime Precipitate Evolution During Aging of a Model Nickel-Based Superalloy

NASA Astrophysics Data System (ADS)

Goodfellow, A. J.; Galindo-Nava, E. I.; Christofidou, K. A.; Jones, N. G.; Martin, T.; Bagot, P. A. J.; Boyer, C. D.; Hardy, M. C.; Stone, H. J.

2018-03-01

The microstructural stability of nickel-based superalloys is critical for maintaining alloy performance during service in gas turbine engines. In this study, the precipitate evolution in a model polycrystalline Ni-based superalloy during aging to 1000 hours has been studied via transmission electron microscopy, atom probe tomography, and neutron diffraction. Variations in phase composition and precipitate morphology, size, and volume fraction were observed during aging, while the constrained lattice misfit remained constant at approximately zero. The experimental composition of the γ matrix phase was consistent with thermodynamic equilibrium predictions, while significant differences were identified between the experimental and predicted results from the γ' phase. These results have implications for the evolution of mechanical properties in service and their prediction using modeling methods.

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

NASA Astrophysics Data System (ADS)

Kruse Christensen, Nikolaj; Ferre, Ty Paul A.; Fiandaca, Gianluca; Christensen, Steen

2017-03-01

We present a workflow for efficient construction and calibration of large-scale groundwater models that includes the integration of airborne electromagnetic (AEM) data and hydrological data. In the first step, the AEM data are inverted to form a 3-D geophysical model. In the second step, the 3-D geophysical model is translated, using a spatially dependent petrophysical relationship, to form a 3-D hydraulic conductivity distribution. The geophysical models and the hydrological data are used to estimate spatially distributed petrophysical shape factors. The shape factors primarily work as translators between resistivity and hydraulic conductivity, but they can also compensate for structural defects in the geophysical model. The method is demonstrated for a synthetic case study with sharp transitions among various types of deposits. Besides demonstrating the methodology, we demonstrate the importance of using geophysical regularization constraints that conform well to the depositional environment. This is done by inverting the AEM data using either smoothness (smooth) constraints or minimum gradient support (sharp) constraints, where the use of sharp constraints conforms best to the environment. The dependency on AEM data quality is also tested by inverting the geophysical model using data corrupted with four different levels of background noise. Subsequently, the geophysical models are used to construct competing groundwater models for which the shape factors are calibrated. The performance of each groundwater model is tested with respect to four types of prediction that are beyond the calibration base: a pumping well's recharge area and groundwater age, respectively, are predicted by applying the same stress as for the hydrologic model calibration; and head and stream discharge are predicted for a different stress situation. As expected, in this case the predictive capability of a groundwater model is better when it is based on a sharp geophysical model instead of a smoothness constraint. This is true for predictions of recharge area, head change, and stream discharge, while we find no improvement for prediction of groundwater age. Furthermore, we show that the model prediction accuracy improves with AEM data quality for predictions of recharge area, head change, and stream discharge, while there appears to be no accuracy improvement for the prediction of groundwater age.

Can we predict 4-year graduation in podiatric medical school using admission data?

PubMed

Sesodia, Sanjay; Molnar, David; Shaw, Graham P

2012-01-01

This study examined the predictive ability of educational background and demographic variables, available at the admission stage, to identify applicants who will graduate in 4 years from podiatric medical school. A logistic regression model was used to identify two predictors of 4-year graduation: age at matriculation and total Medical College Admission Test score. The model was cross-validated using a second independent sample from the same population. Cross-validation gives greater confidence that the results could be more generally applied. Total Medical College Admission Test score was the strongest predictor of 4-year graduation, with age at matriculation being a statistically significant but weaker predictor. Despite the model's capacity to predict 4-year graduation better than random assignment, a sufficient amount of error in prediction remained, suggesting that important predictors are missing from the model. Furthermore, the high rate of false-positives makes it inappropriate to use age and Medical College Admission Test score as admission screens in an attempt to eliminate attrition by not accepting at-risk students.

Using Functional Data Analysis Models to Estimate Future Time Trends in Age-Specific Breast Cancer Mortality for the United States and England–Wales

PubMed Central

Erbas, Bircan; Akram, Muhammed; Gertig, Dorota M; English, Dallas; Hopper, John L.; Kavanagh, Anne M; Hyndman, Rob

2010-01-01

Background Mortality/incidence predictions are used for allocating public health resources and should accurately reflect age-related changes through time. We present a new forecasting model for estimating future trends in age-related breast cancer mortality for the United States and England–Wales. Methods We used functional data analysis techniques both to model breast cancer mortality-age relationships in the United States from 1950 through 2001 and England–Wales from 1950 through 2003 and to estimate 20-year predictions using a new forecasting method. Results In the United States, trends for women aged 45 to 54 years have continued to decline since 1980. In contrast, trends in women aged 60 to 84 years increased in the 1980s and declined in the 1990s. For England–Wales, trends for women aged 45 to 74 years slightly increased before 1980, but declined thereafter. The greatest age-related changes for both regions were during the 1990s. For both the United States and England–Wales, trends are expected to decline and then stabilize, with the greatest decline in women aged 60 to 70 years. Forecasts suggest relatively stable trends for women older than 75 years. Conclusions Prediction of age-related changes in mortality/incidence can be used for planning and targeting programs for specific age groups. Currently, these models are being extended to incorporate other variables that may influence age-related changes in mortality/incidence trends. In their current form, these models will be most useful for modeling and projecting future trends of diseases for which there has been very little advancement in treatment and minimal cohort effects (eg. lethal cancers). PMID:20139657

Epidemiology of Plasmodium falciparum gametocytemia in India: prevalence, age structure, risk factors and the role of a predictive score for detection.

PubMed

Shah, Naman K; Poole, Charles; MacDonald, Pia D M; Srivastava, Bina; Schapira, Allan; Juliano, Jonathan J; Anvikar, Anup; Meshnick, Steven R; Valecha, Neena; Mishra, Neelima

2013-07-01

To characterise the epidemiology of Plasmodium falciparum gametocytemia and determine the prevalence, age structure and the viability of a predictive model for detection. We collected data from 21 therapeutic efficacy trials conducted in India during 2009-2010 and estimated the contribution of each age group to the reservoir of transmission. We built a predictive model for gametocytemia and calculated the diagnostic utility of different score cut-offs from our risk score. Gametocytemia was present in 18% (248/1 335) of patients and decreased with age. Adults constituted 43%, school-age children 45% and under fives 12% of the reservoir for potential transmission. Our model retained age, sex, region and previous antimalarial drug intake as predictors of gametocytemia. The area under the receiver operator characteristic curve was 0.76 (95%CI:0.73,0.78), and a cut-off of 14 or more on a risk score ranging from 0 to 46 provided 91% (95%CI:88,95) sensitivity and 33% (95%CI:31,36) specificity for detecting gametocytemia. Gametocytemia was common in India and varied by region. Notably, adults contributed substantially to the reservoir for potential transmission. Predictive modelling to generate a clinical algorithm for detecting gametocytemia did not provide sufficient discrimination for targeting interventions. © 2013 Blackwell Publishing Ltd.

Quantifying the influence of sediment source area sampling on detrital thermochronometer data

NASA Astrophysics Data System (ADS)

Whipp, D. M., Jr.; Ehlers, T. A.; Coutand, I.; Bookhagen, B.

2014-12-01

Detrital thermochronology offers a unique advantage over traditional bedrock thermochronology because of its sensitivity to sediment production and transportation to sample sites. In mountainous regions, modern fluvial sediment is often collected and dated to determine the past (105 to >107 year) exhumation history of the upstream drainage area. Though potentially powerful, the interpretation of detrital thermochronometer data derived from modern fluvial sediment is challenging because of spatial and temporal variations in sediment production and transport, and target mineral concentrations. Thermochronometer age prediction models provide a quantitative basis for data interpretation, but it can be difficult to separate variations in catchment bedrock ages from the effects of variable basin denudation and sediment transport. We present two examples of quantitative data interpretation using detrital thermochronometer data from the Himalaya, focusing on the influence of spatial and temporal variations in basin denudation on predicted age distributions. We combine age predictions from the 3D thermokinematic numerical model Pecube with simple models for sediment sampling in the upstream drainage basin area to assess the influence of variations in sediment production by different geomorphic processes or scaled by topographic metrics. We first consider a small catchment from the central Himalaya where bedrock landsliding appears to have affected the observed muscovite 40Ar/39Ar age distributions. Using a simple model of random landsliding with a power-law landslide frequency-area relationship we find that the sediment residence time in the catchment has a major influence on predicted age distributions. In the second case, we compare observed detrital apatite fission-track age distributions from 16 catchments in the Bhutan Himalaya to ages predicted using Pecube and scaled by various topographic metrics. Preliminary results suggest that predicted age distributions scaled by the rock uplift rate in Pecube are statistically equivalent to the observed age distributions for ~75% of the catchments, but may improve when scaled by local relief or specific stream power weighted by satellite-derived precipitation. Ongoing work is exploring the effect of scaling by other topographic metrics.

The Relationship Between Social Support and Subjective Well-Being Across Age

PubMed Central

Salthouse, Timothy A.; Oishi, Shigehiro; Jeswani, Sheena

2014-01-01

The relationships among types of social support and different facets of subjective well-being (i.e., life satisfaction, positive affect, and negative affect) were examined in a sample of 1,111 individuals between the ages of 18 and 95. Using structural equation modeling we found that life satisfaction was predicted by enacted and perceived support, positive affect was predicted by family embeddedness and provided support, and negative affect was predicted by perceived support. When personality variables were included in a subsequent model, the influence of the social support variables were generally reduced. Invariance analyses conducted across age groups indicated that there were no substantial differences in predictors of the different types of subjective well-being across age. PMID:25045200

Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males.

PubMed

Feng, Lei; Peng, Fuduan; Li, Shanfei; Jiang, Li; Sun, Hui; Ji, Anquan; Zeng, Changqing; Li, Caixia; Liu, Fan

2018-03-23

Estimating individual age from biomarkers may provide key information facilitating forensic investigations. Recent progress has shown DNA methylation at age-associated CpG sites as the most informative biomarkers for estimating the individual age of an unknown donor. Optimal feature selection plays a critical role in determining the performance of the final prediction model. In this study we investigate methylation levels at 153 age-associated CpG sites from 21 previously reported genomic regions using the EpiTYPER system for their predictive power on individual age in 390 Han Chinese males ranging from 15 to 75 years of age. We conducted a systematic feature selection using a stepwise backward multiple linear regression analysis as well as an exhaustive searching algorithm. Both approaches identified the same subset of 9 CpG sites, which in linear combination provided the optimal model fitting with mean absolute deviation (MAD) of 2.89 years of age and explainable variance (R 2 ) of 0.92. The final model was validated in two independent Han Chinese male samples (validation set 1, N = 65, MAD = 2.49, R 2  = 0.95, and validation set 2, N = 62, MAD = 3.36, R 2  = 0.89). Other competing models such as support vector machine and artificial neural network did not outperform the linear model to any noticeable degree. The validation set 1 was additionally analyzed using Pyrosequencing technology for cross-platform validation and was termed as validation set 3. Directly applying our model, in which the methylation levels were detected by the EpiTYPER system, to the data from pyrosequencing technology showed, however, less accurate results in terms of MAD (validation set 3, N = 65 Han Chinese males, MAD = 4.20, R 2  = 0.93), suggesting the presence of a batch effect between different data generation platforms. This batch effect could be partially overcome by a z-score transformation (MAD = 2.76, R 2  = 0.93). Overall, our systematic feature selection identified 9 CpG sites as the optimal subset for forensic age estimation and the prediction model consisting of these 9 markers demonstrated high potential in forensic practice. An age estimator implementing our prediction model allowing missing markers is freely available at http://liufan.big.ac.cn/AgePrediction. Copyright © 2018 Elsevier B.V. All rights reserved.

External validation of anti-Müllerian hormone based prediction of live birth in assisted conception

PubMed Central

2013-01-01

Background Chronological age and oocyte yield are independent determinants of live birth in assisted conception. Anti-Müllerian hormone (AMH) is strongly associated with oocyte yield after controlled ovarian stimulation. We have previously assessed the ability of AMH and age to independently predict live birth in an Italian assisted conception cohort. Herein we report the external validation of the nomogram in 822 UK first in vitro fertilization (IVF) cycles. Methods Retrospective cohort consisting of 822 patients undergoing their first IVF treatment cycle at Glasgow Centre for Reproductive Medicine. Analyses were restricted to women aged between 25 and 42 years of age. All women had an AMH measured prior to commencing their first IVF cycle. The performance of the model was assessed; discrimination by the area under the receiver operator curve (ROCAUC) and model calibration by the predicted probability versus observed probability. Results Live births occurred in 29.4% of the cohort. The observed and predicted outcomes showed no evidence of miscalibration (p = 0.188). The ROCAUC was 0.64 (95% CI: 0.60, 0.68), suggesting moderate and similar discrimination to the original model. The ROCAUC for a continuous model of age and AMH was 0.65 (95% CI 0.61, 0.69), suggesting that the original categories of AMH were appropriate. Conclusions We confirm by external validation that AMH and age are independent predictors of live birth. Although the confidence intervals for each category are wide, our results support the assessment of AMH in larger cohorts with detailed baseline phenotyping for live birth prediction. PMID:23294733

Development and evaluation of height diameter at breast models for native Chinese Metasequoia.

PubMed

Liu, Mu; Feng, Zhongke; Zhang, Zhixiang; Ma, Chenghui; Wang, Mingming; Lian, Bo-Ling; Sun, Renjie; Zhang, Li

2017-01-01

Accurate tree height and diameter at breast height (dbh) are important input variables for growth and yield models. A total of 5503 Chinese Metasequoia trees were used in this study. We studied 53 fitted models, of which 7 were linear models and 46 were non-linear models. These models were divided into two groups of single models and multivariate models according to the number of independent variables. The results show that the allometry equation of tree height which has diameter at breast height as independent variable can better reflect the change of tree height; in addition the prediction accuracy of the multivariate composite models is higher than that of the single variable models. Although tree age is not the most important variable in the study of the relationship between tree height and dbh, the consideration of tree age when choosing models and parameters in model selection can make the prediction of tree height more accurate. The amount of data is also an important parameter what can improve the reliability of models. Other variables such as tree height, main dbh and altitude, etc can also affect models. In this study, the method of developing the recommended models for predicting the tree height of native Metasequoias aged 50-485 years is statistically reliable and can be used for reference in predicting the growth and production of mature native Metasequoia.

Development and evaluation of height diameter at breast models for native Chinese Metasequoia

PubMed Central

Feng, Zhongke; Zhang, Zhixiang; Ma, Chenghui; Wang, Mingming; Lian, Bo-ling; Sun, Renjie; Zhang, Li

2017-01-01

Accurate tree height and diameter at breast height (dbh) are important input variables for growth and yield models. A total of 5503 Chinese Metasequoia trees were used in this study. We studied 53 fitted models, of which 7 were linear models and 46 were non-linear models. These models were divided into two groups of single models and multivariate models according to the number of independent variables. The results show that the allometry equation of tree height which has diameter at breast height as independent variable can better reflect the change of tree height; in addition the prediction accuracy of the multivariate composite models is higher than that of the single variable models. Although tree age is not the most important variable in the study of the relationship between tree height and dbh, the consideration of tree age when choosing models and parameters in model selection can make the prediction of tree height more accurate. The amount of data is also an important parameter what can improve the reliability of models. Other variables such as tree height, main dbh and altitude, etc can also affect models. In this study, the method of developing the recommended models for predicting the tree height of native Metasequoias aged 50–485 years is statistically reliable and can be used for reference in predicting the growth and production of mature native Metasequoia. PMID:28817600

Learning-based prediction of gestational age from ultrasound images of the fetal brain.

PubMed

Namburete, Ana I L; Stebbing, Richard V; Kemp, Bryn; Yaqub, Mohammad; Papageorghiou, Aris T; Alison Noble, J

2015-04-01

We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction with clinical measurements to develop, for the first time, a predictive age model which improves on the GA-prediction potential of US images. The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position. This allows for fast and efficient sampling of anatomically-corresponding brain regions to achieve like-for-like structural comparison of different developmental stages. We develop bespoke features which capture neurosonographic patterns in 3D images, and using a regression forest classifier, we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population (N=447) over an age range of active brain maturation (18-34weeks). On a routine clinical dataset (N=187) our age prediction results strongly correlate with true GA (r=0.98,accurate within±6.10days), confirming the link between maturational progression and neurosonographic activity observable across gestation. Our model also outperforms current clinical methods by ±4.57 days in the third trimester-a period complicated by biological variations in the fetal population. Through feature selection, the model successfully identified the most age-discriminating anatomies over this age range as being the Sylvian fissure, cingulate, and callosal sulci. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

The creation and evaluation of a model to simulate the probability of conception in seasonal-calving pasture-based dairy heifers.

PubMed

Fenlon, Caroline; O'Grady, Luke; Butler, Stephen; Doherty, Michael L; Dunnion, John

2017-01-01

Herd fertility in pasture-based dairy farms is a key driver of farm economics. Models for predicting nulliparous reproductive outcomes are rare, but age, genetics, weight, and BCS have been identified as factors influencing heifer conception. The aim of this study was to create a simulation model of heifer conception to service with thorough evaluation. Artificial Insemination service records from two research herds and ten commercial herds were provided to build and evaluate the models. All were managed as spring-calving pasture-based systems. The factors studied were related to age, genetics, and time of service. The data were split into training and testing sets and bootstrapping was used to train the models. Logistic regression (with and without random effects) and generalised additive modelling were selected as the model-building techniques. Two types of evaluation were used to test the predictive ability of the models: discrimination and calibration. Discrimination, which includes sensitivity, specificity, accuracy and ROC analysis, measures a model's ability to distinguish between positive and negative outcomes. Calibration measures the accuracy of the predicted probabilities with the Hosmer-Lemeshow goodness-of-fit, calibration plot and calibration error. After data cleaning and the removal of services with missing values, 1396 services remained to train the models and 597 were left for testing. Age, breed, genetic predicted transmitting ability for calving interval, month and year were significant in the multivariate models. The regression models also included an interaction between age and month. Year within herd was a random effect in the mixed regression model. Overall prediction accuracy was between 77.1% and 78.9%. All three models had very high sensitivity, but low specificity. The two regression models were very well-calibrated. The mean absolute calibration errors were all below 4%. Because the models were not adept at identifying unsuccessful services, they are not suggested for use in predicting the outcome of individual heifer services. Instead, they are useful for the comparison of services with different covariate values or as sub-models in whole-farm simulations. The mixed regression model was identified as the best model for prediction, as the random effects can be ignored and the other variables can be easily obtained or simulated.

A model for predicting life expectancy of children with cystic fibrosis.

PubMed

Aurora, P; Wade, A; Whitmore, P; Whitehead, B

2000-12-01

In this study the authors aimed to produce a model for predicting the life expectancy of children with severe cystic fibrosis (CF) lung disease. The survival of 181 children with severe CF lung disease referred for transplantation assessment 1988-1998 (mean age 11.5 yrs, median survival without transplant 1.9 yrs from date of assessment) were studied. Proportional hazards modelling was used to identify assessment measurements that are of value in predicting longevity. The resultant model included low height predicted forced expiratory volume in one second (FEV1), low minimum oxygen saturation (Sa,O2min) during a 12-min walk, high age adjusted resting heart rate, young age, female sex, low plasma albumin, and low blood haemoglobin as predictors for poor prognosis. Extrapolation from the model suggests that a 12-yr old male child with an FEV1 of 30% pred and a Sa,O2min of 85% has a 44% risk of death within 2 yrs (95% confidence interval (CI) 35-54%), whilst a female child with the same measurements has a 63% risk of death (95% CI 52-73%) within the same period. The model produced may be of value in predicting the life expectancy of children with severe cystic fibrosis lung disease and in optimizing the timing of lung transplantation.

A conceptual model predicting internalizing problems in middle childhood among children of alcoholic and nonalcoholic fathers: the role of marital aggression.

PubMed

Eiden, Rina D; Molnar, Danielle S; Colder, Craig; Edwards, Ellen P; Leonard, Kenneth E

2009-09-01

The purpose of this study was to test a conceptual model predicting children's anxiety/depression in middle childhood in a community sample of children with parents who had alcohol problems (n = 112) and those without alcohol problems (n = 101). The conceptual model examined the role of parents' alcohol diagnoses, depression, and antisocial behavior among parents of children ages 12 months to kindergarten age in predicting marital aggression and parental aggravation. Higher levels of marital aggression and parental aggravation were hypothesized to predict children's depression/anxiety within time (18 months to kindergarten age and, prospectively, to age during fourth grade). The sample was recruited from New York State birth records when the children were 12 months old. Assessments were conducted at 12, 18, 24, and 36 months; at kindergarten age; and during fourth grade. Children with alcoholic fathers had higher depression/anxiety scores according to parental reports but not self-reports. Structural equations modeling was largely supportive of the conceptual model. Fathers' alcoholism was associated with higher child anxiety via greater levels of marital aggression among families with alcohol problems. Results also indicated that there was a significant indirect association between parents' depression symptoms and child anxiety via marital aggression. The results highlight the nested nature of risk characteristics in alcoholic families and the important role of marital aggression in predicting children's anxiety/depression. Interventions targeting both parents' alcohol problems and associated marital aggression are likely to provide the dual benefits of improving family interactions and lowering risk of children's internalizing behavior problems.

Preterm or not--an evaluation of estimates of gestational age in a cohort of women from Rural Papua New Guinea.

PubMed

Karl, Stephan; Li Wai Suen, Connie S N; Unger, Holger W; Ome-Kaius, Maria; Mola, Glen; White, Lisa; Wangnapi, Regina A; Rogerson, Stephen J; Mueller, Ivo

2015-01-01

Knowledge of accurate gestational age is required for comprehensive pregnancy care and is an essential component of research evaluating causes of preterm birth. In industrialised countries gestational age is determined with the help of fetal biometry in early pregnancy. Lack of ultrasound and late presentation to antenatal clinic limits this practice in low-resource settings. Instead, clinical estimators of gestational age are used, but their accuracy remains a matter of debate. In a cohort of 688 singleton pregnancies from rural Papua New Guinea, delivery gestational age was calculated from Ballard score, last menstrual period, symphysis-pubis fundal height at first visit and quickening as well as mid- and late pregnancy fetal biometry. Published models using sequential fundal height measurements and corrected last menstrual period to estimate gestational age were also tested. Novel linear models that combined clinical measurements for gestational age estimation were developed. Predictions were compared with the reference early pregnancy ultrasound (<25 gestational weeks) using correlation, regression and Bland-Altman analyses and ranked for their capability to predict preterm birth using the harmonic mean of recall and precision (F-measure). Average bias between reference ultrasound and clinical methods ranged from 0-11 days (95% confidence levels: 14-42 days). Preterm birth was best predicted by mid-pregnancy ultrasound (F-measure: 0.72), and neuromuscular Ballard score provided the least reliable preterm birth prediction (F-measure: 0.17). The best clinical methods to predict gestational age and preterm birth were last menstrual period and fundal height (F-measures 0.35). A linear model combining both measures improved prediction of preterm birth (F-measure: 0.58). Estimation of gestational age without ultrasound is prone to significant error. In the absence of ultrasound facilities, last menstrual period and fundal height are among the more reliable clinical measures. This study underlines the importance of strengthening ultrasound facilities and developing novel ways to estimate gestational age.

Sex-Specific Prediction Models for Sleep Apnea From the Hispanic Community Health Study/Study of Latinos.

PubMed

Shah, Neomi; Hanna, David B; Teng, Yanping; Sotres-Alvarez, Daniela; Hall, Martica; Loredo, Jose S; Zee, Phyllis; Kim, Mimi; Yaggi, H Klar; Redline, Susan; Kaplan, Robert C

2016-06-01

We developed and validated the first-ever sleep apnea (SA) risk calculator in a large population-based cohort of Hispanic/Latino subjects. Cross-sectional data on adults from the Hispanic Community Health Study/Study of Latinos (2008-2011) were analyzed. Subjective and objective sleep measurements were obtained. Clinically significant SA was defined as an apnea-hypopnea index ≥ 15 events per hour. Using logistic regression, four prediction models were created: three sex-specific models (female-only, male-only, and a sex × covariate interaction model to allow differential predictor effects), and one overall model with sex included as a main effect only. Models underwent 10-fold cross-validation and were assessed by using the C statistic. SA and its predictive variables; a total of 17 variables were considered. A total of 12,158 participants had complete sleep data available; 7,363 (61%) were women. The population-weighted prevalence of SA (apnea-hypopnea index ≥ 15 events per hour) was 6.1% in female subjects and 13.5% in male subjects. Male-only (C statistic, 0.808) and female-only (C statistic, 0.836) prediction models had the same predictor variables (ie, age, BMI, self-reported snoring). The sex-interaction model (C statistic, 0.836) contained sex, age, age × sex, BMI, BMI × sex, and self-reported snoring. The final overall model (C statistic, 0.832) contained age, BMI, snoring, and sex. We developed two websites for our SA risk calculator: one in English (https://www.montefiore.org/sleepapneariskcalc.html) and another in Spanish (http://www.montefiore.org/sleepapneariskcalc-es.html). We created an internally validated, highly discriminating, well-calibrated, and parsimonious prediction model for SA. Contrary to the study hypothesis, the variables did not have different predictive magnitudes in male and female subjects. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Development of a Pavement Maintenance Management System. Volume 9. Development of Airfield Pavement Performance Prediction Models.

DTIC Science & Technology

1984-05-01

materials, traffic, and climate, were used to develop PCI and key distress prediction models for both asphalt-concrete- and jointed-concrete- surfaced...Predicted PCI for PCC and AC/PCC Pavements Using Model Presented in Section III ...... 35 31 Effect of PCC Thickness on the PCI as a Function of Age...of Corner Breaking Observed vs Predicted Percent of Corner Breaking Using Model Presented in Section III

Prediction of Precipitation Strengthening in the Commercial Mg Alloy AZ91 Using Dislocation Dynamics

DOE PAGES

Aagesen, L. K.; Miao, J.; Allison, J. E.; ...

2018-03-05

In this paper, dislocation dynamics simulations were used to predict the strengthening of a commercial magnesium alloy, AZ91, due to β-Mg 17Al 12 formed in the continuous precipitation mode. The precipitate distributions used in simulations were determined based on experimental characterization of the sizes, shapes, and number densities of the precipitates for 10-hour aging and 50-hour aging. For dislocations gliding on the basal plane, which is expected to be the dominant contributor to plastic deformation at room temperature, the critical resolved shear stress to bypass the precipitate distribution was 3.5 MPa for the 10-hour aged sample and 16.0 MPa formore » the 50-hour aged sample. The simulation results were compared to an analytical model of strengthening in this alloy, and the analytical model was found to predict critical resolved shear stresses that were approximately 30 pct lower. A model for the total yield strength was developed and compared with experiment for the 50-hour aged sample. Finally, the predicted yield strength, which included the precipitate strengthening contribution from the DD simulations, was 132.0 MPa, in good agreement with the measured yield strength of 141 MPa.« less

Prediction of Precipitation Strengthening in the Commercial Mg Alloy AZ91 Using Dislocation Dynamics

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

Aagesen, L. K.; Miao, J.; Allison, J. E.

In this paper, dislocation dynamics simulations were used to predict the strengthening of a commercial magnesium alloy, AZ91, due to β-Mg 17Al 12 formed in the continuous precipitation mode. The precipitate distributions used in simulations were determined based on experimental characterization of the sizes, shapes, and number densities of the precipitates for 10-hour aging and 50-hour aging. For dislocations gliding on the basal plane, which is expected to be the dominant contributor to plastic deformation at room temperature, the critical resolved shear stress to bypass the precipitate distribution was 3.5 MPa for the 10-hour aged sample and 16.0 MPa formore » the 50-hour aged sample. The simulation results were compared to an analytical model of strengthening in this alloy, and the analytical model was found to predict critical resolved shear stresses that were approximately 30 pct lower. A model for the total yield strength was developed and compared with experiment for the 50-hour aged sample. Finally, the predicted yield strength, which included the precipitate strengthening contribution from the DD simulations, was 132.0 MPa, in good agreement with the measured yield strength of 141 MPa.« less

Prediction of Precipitation Strengthening in the Commercial Mg Alloy AZ91 Using Dislocation Dynamics

NASA Astrophysics Data System (ADS)

Aagesen, L. K.; Miao, J.; Allison, J. E.; Aubry, S.; Arsenlis, A.

2018-03-01

Dislocation dynamics simulations were used to predict the strengthening of a commercial magnesium alloy, AZ91, due to β-Mg17Al12 formed in the continuous precipitation mode. The precipitate distributions used in simulations were determined based on experimental characterization of the sizes, shapes, and number densities of the precipitates for 10-hour aging and 50-hour aging. For dislocations gliding on the basal plane, which is expected to be the dominant contributor to plastic deformation at room temperature, the critical resolved shear stress to bypass the precipitate distribution was 3.5 MPa for the 10-hour aged sample and 16.0 MPa for the 50-hour aged sample. The simulation results were compared to an analytical model of strengthening in this alloy, and the analytical model was found to predict critical resolved shear stresses that were approximately 30 pct lower. A model for the total yield strength was developed and compared with experiment for the 50-hour aged sample. The predicted yield strength, which included the precipitate strengthening contribution from the DD simulations, was 132.0 MPa, in good agreement with the measured yield strength of 141 MPa.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States–Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci

PubMed Central

Clyde, Merlise A.; Palmieri Weber, Rachel; Iversen, Edwin S.; Poole, Elizabeth M.; Doherty, Jennifer A.; Goodman, Marc T.; Ness, Roberta B.; Risch, Harvey A.; Rossing, Mary Anne; Terry, Kathryn L.; Wentzensen, Nicolas; Whittemore, Alice S.; Anton-Culver, Hoda; Bandera, Elisa V.; Berchuck, Andrew; Carney, Michael E.; Cramer, Daniel W.; Cunningham, Julie M.; Cushing-Haugen, Kara L.; Edwards, Robert P.; Fridley, Brooke L.; Goode, Ellen L.; Lurie, Galina; McGuire, Valerie; Modugno, Francesmary; Moysich, Kirsten B.; Olson, Sara H.; Pearce, Celeste Leigh; Pike, Malcolm C.; Rothstein, Joseph H.; Sellers, Thomas A.; Sieh, Weiva; Stram, Daniel; Thompson, Pamela J.; Vierkant, Robert A.; Wicklund, Kristine G.; Wu, Anna H.; Ziogas, Argyrios; Tworoger, Shelley S.; Schildkraut, Joellen M.

2016-01-01

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted. PMID:27698005

A Point System for Predicting 10-Year Risk of Developing Type 2 Diabetes Mellitus in Japanese Men: Aichi Workers' Cohort Study.

PubMed

Yatsuya, Hiroshi; Li, Yuanying; Hirakawa, Yoshihisa; Ota, Atsuhiko; Matsunaga, Masaaki; Haregot, Hilawe Esayas; Chiang, Chifa; Zhang, Yan; Tamakoshi, Koji; Toyoshima, Hideaki; Aoyama, Atsuko

2018-03-17

Relatively little evidence exists for type 2 diabetes mellitus (T2DM) prediction models from long-term follow-up studies in East Asians. This study aims to develop a point-based prediction model for 10-year risk of developing T2DM in middle-aged Japanese men. We followed 3,540 male participants of Aichi Workers' Cohort Study, who were aged 35-64 years and were free of diabetes in 2002, until March 31, 2015. Baseline age, body mass index (BMI), smoking status, alcohol consumption, regular exercise, medication for dyslipidemia, diabetes family history, and blood levels of triglycerides (TG), high density lipoprotein cholesterol (HDLC) and fasting blood glucose (FBG) were examined using Cox proportional hazard model. Variables significantly associated with T2DM in univariable models were simultaneously entered in a multivariable model for determination of the final model using backward variable selection. Performance of an existing T2DM model when applied to the current dataset was compared to that obtained in the present study's model. During the median follow-up of 12.2 years, 342 incident T2DM cases were documented. The prediction system using points assigned to age, BMI, smoking status, diabetes family history, and TG and FBG showed reasonable discrimination (c-index: 0.77) and goodness-of-fit (Hosmer-Lemeshow test, P = 0.22). The present model outperformed the previous one in the present subjects. The point system, once validated in the other populations, could be applied to middle-aged Japanese male workers to identify those at high risk of developing T2DM. In addition, further investigation is also required to examine whether the use of this system will reduce incidence.

Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements.

PubMed

Chavana-Bryant, Cecilia; Malhi, Yadvinder; Wu, Jin; Asner, Gregory P; Anastasiou, Athanasios; Enquist, Brian J; Cosio Caravasi, Eric G; Doughty, Christopher E; Saleska, Scott R; Martin, Roberta E; Gerard, France F

2017-05-01

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (P mass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (N mass ) and carbon (C mass ) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R 2  = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R 2  = 0.07-0.73; %RMSE = 7-38) and multiple (R 2  = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

MR brain volumetric measurements are predictive of neurobehavioral impairment in the HIV-1 transgenic rat.

PubMed

Casas, Rafael; Muthusamy, Siva; Wakim, Paul G; Sinharay, Sanhita; Lentz, Margaret R; Reid, William C; Hammoud, Dima A

2018-01-01

HIV infection is known to be associated with brain volume loss, even in optimally treated patients. In this study, we assessed whether dynamic brain volume changes over time are predictive of neurobehavorial performance in the HIV-1 transgenic (Tg) rat, a model of treated HIV-positive patients. Cross-sectional brain MRI imaging was first performed comparing Tg and wild type (WT) rats at 3 and 19 months of age. Longitudinal MRI and neurobehavioral testing of another group of Tg and WT rats was then performed from 5 to 23 weeks of age. Whole brain and subregional image segmentation was used to assess the rate of brain growth over time. We used repeated-measures mixed models to assess differences in brain volumes and to establish how predictive the volume differences are of specific neurobehavioral deficits. Cross-sectional imaging showed smaller whole brain volumes in Tg compared to WT rats at 3 and at 19 months of age. Longitudinally, Tg brain volumes were smaller than age-matched WT rats at all time points, starting as early as 5 weeks of age. The Tg striatal growth rate delay between 5 and 9 weeks of age was greater than that of the whole brain. Striatal volume in combination with genotype was the most predictive of rota-rod scores and in combination with genotype and age was the most predictive of total exploratory activity scores in the Tg rats. The disproportionately delayed striatal growth compared to whole brain between 5 and 9 weeks of age and the role of striatal volume in predicting neurobehavioral deficits suggest an important role of the dopaminergic system in HIV associated neuropathology. This might explain problems with motor coordination and executive decisions in this animal model. Smaller brain and subregional volumes and neurobehavioral deficits were seen as early as 5 weeks of age, suggesting an early brain insult in the Tg rat. Neuroprotective therapy testing in this model should thus target this early stage of development, before brain damage becomes irreversible.

Interactive effects of aging parameters of AA6056

NASA Astrophysics Data System (ADS)

Dehghani, Kamran; Nekahi, Atiye

2012-10-01

The effect of thermomechanical treatment on the aging behavior of AA6056 aluminum alloy was modeled using response surface methodology (RSM). Two models were developed to predict the final yield stress (FYS) and elongation amounts as well as the optimum conditions of aging process. These were done based on the interactive effects of applied thermomechanical parameters. The optimum condition predicted by the model to attain the maximum strength was pre-aging at 80 °C for 15 h, followed by 70% cold work and subsequent final aging at 165 °C for 4 h, which resulted in the FYS of about 480 MPa. As for the elongation, the optimum condition was pre-aging at 80 °C for 15 h, followed by 30% cold work and final-aging at 165 °C for 4 h, which led to 21% elongation. To verify the suggested optimum conditions, the tests were carried out confirming the high accuracy (above 94%) of the RSM technique as well as the developed models. It is shown that the RSM can be used successfully to optimize the aging process, to determine the significance of aging parameters and to model the combination effect of process variables on the aging behavior of AA6056.

Validation of Maturity Offset in the Fels Longitudinal Study.

PubMed

Malina, Robert M; Choh, Audrey C; Czerwinski, Stefan A; Chumlea, Wm Cameron

2016-08-01

Sex-specific equations for predicting maturity offset, time before or after peak height velocity (PHV), were evaluated in 63 girls and 74 boys from the Fels Longitudinal Study. Serially measured heights (0.1 cm), sitting heights (0.1 cm), weights (0.1 kg), and estimated leg lengths (0.1 cm) from 8 to 18 years were used. Predicted age at PHV (years) was calculated as the difference between chronological age (CA) and maturity offset. Actual age at PHV for each child was derived with a triple logistic model (Bock-Thissen-du Toit). Mean predicted maturity offset was negative and lowest at 8 years and increased linearly with increasing CA. Predicted ages at PHV increased linearly with CA from 8 to 18 years in girls and from 8 to 13 years in boys; predictions varied within relatively narrow limits from 12 to 15 years and then increased to 18 years in boys. Differences between predicted and actual ages at PHV among youth of contrasting maturity status were significant across the age range in both sexes. Dependence of predicted age at PHV upon CA at prediction and on actual age at PHV limits its utility as an indicator of maturity timing and in sport talent programs.

Analysis of near infrared spectra for age-grading of wild populations of Anopheles gambiae.

PubMed

Krajacich, Benjamin J; Meyers, Jacob I; Alout, Haoues; Dabiré, Roch K; Dowell, Floyd E; Foy, Brian D

2017-11-07

Understanding the age-structure of mosquito populations, especially malaria vectors such as Anopheles gambiae, is important for assessing the risk of infectious mosquitoes, and how vector control interventions may impact this risk. The use of near-infrared spectroscopy (NIRS) for age-grading has been demonstrated previously on laboratory and semi-field mosquitoes, but to date has not been utilized on wild-caught mosquitoes whose age is externally validated via parity status or parasite infection stage. In this study, we developed regression and classification models using NIRS on datasets of wild An. gambiae (s.l.) reared from larvae collected from the field in Burkina Faso, and two laboratory strains. We compared the accuracy of these models for predicting the ages of wild-caught mosquitoes that had been scored for their parity status as well as for positivity for Plasmodium sporozoites. Regression models utilizing variable selection increased predictive accuracy over the more common full-spectrum partial least squares (PLS) approach for cross-validation of the datasets, validation, and independent test sets. Models produced from datasets that included the greatest range of mosquito samples (i.e. different sampling locations and times) had the highest predictive accuracy on independent testing sets, though overall accuracy on these samples was low. For classification, we found that intramodel accuracy ranged between 73.5-97.0% for grouping of mosquitoes into "early" and "late" age classes, with the highest prediction accuracy found in laboratory colonized mosquitoes. However, this accuracy was decreased on test sets, with the highest classification of an independent set of wild-caught larvae reared to set ages being 69.6%. Variation in NIRS data, likely from dietary, genetic, and other factors limits the accuracy of this technique with wild-caught mosquitoes. Alternative algorithms may help improve prediction accuracy, but care should be taken to either maximize variety in models or minimize confounders.

A model combining age, equivalent uniform dose and IL-8 may predict radiation esophagitis in patients with non-small cell lung cancer.

PubMed

Wang, Shulian; Campbell, Jeff; Stenmark, Matthew H; Stanton, Paul; Zhao, Jing; Matuszak, Martha M; Ten Haken, Randall K; Kong, Feng-Ming

2018-03-01

To study whether cytokine markers may improve predictive accuracy of radiation esophagitis (RE) in non-small cell lung cancer (NSCLC) patients. A total of 129 patients with stage I-III NSCLC treated with radiotherapy (RT) from prospective studies were included. Thirty inflammatory cytokines were measured in platelet-poor plasma samples. Logistic regression was performed to evaluate the risk factors of RE. Stepwise Akaike information criterion (AIC) and likelihood ratio test were used to assess model predictions. Forty-nine of 129 patients (38.0%) developed grade ≥2 RE. Univariate analysis showed that age, stage, concurrent chemotherapy, and eight dosimetric parameters were significantly associated with grade ≥2 RE (p < 0.05). IL-4, IL-5, IL-8, IL-13, IL-15, IL-1α, TGFα and eotaxin were also associated with grade ≥2 RE (p < 0.1). Age, esophagus generalized equivalent uniform dose (EUD), and baseline IL-8 were independently associated grade ≥2 RE. The combination of these three factors had significantly higher predictive power than any single factor alone. Addition of IL-8 to toxicity model significantly improves RE predictive accuracy (p = 0.019). Combining baseline level of IL-8, age and esophagus EUD may predict RE more accurately. Refinement of this model with larger sample sizes and validation from multicenter database are warranted. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Pharmacokinetic modeling: Prediction and evaluation of route dependent dosimetry of bisphenol A in monkeys with extrapolation to humans

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

Fisher, Jeffrey W., E-mail: jeffrey.fisher@fda.hhs.gov; Twaddle, Nathan C.; Vanlandingham, Michelle

A physiologically based pharmacokinetic (PBPK) model was developed for bisphenol A (BPA) in adult rhesus monkeys using intravenous (iv) and oral bolus doses of 100 {mu}g d6-BPA/kg (). This calibrated PBPK adult monkey model for BPA was then evaluated against published monkey kinetic studies with BPA. Using two versions of the adult monkey model based on monkey BPA kinetic data from and , the aglycone BPA pharmacokinetics were simulated for human oral ingestion of 5 mg d16-BPA per person (Voelkel et al., 2002). Voelkel et al. were unable to detect the aglycone BPA in plasma, but were able to detectmore » BPA metabolites. These human model predictions of the aglycone BPA in plasma were then compared to previously published PBPK model predictions obtained by simulating the Voelkel et al. kinetic study. Our BPA human model, using two parameter sets reflecting two adult monkey studies, both predicted lower aglycone levels in human serum than the previous human BPA PBPK model predictions. BPA was metabolized at all ages of monkey (PND 5 to adult) by the gut wall and liver. However, the hepatic metabolism of BPA and systemic clearance of its phase II metabolites appear to be slower in younger monkeys than adults. The use of the current non-human primate BPA model parameters provides more confidence in predicting the aglycone BPA in serum levels in humans after oral ingestion of BPA. -- Highlights: Black-Right-Pointing-Pointer A bisphenol A (BPA) PBPK model for the infant and adult monkey was constructed. Black-Right-Pointing-Pointer The hepatic metabolic rate of BPA increased with age of the monkey. Black-Right-Pointing-Pointer The systemic clearance rate of metabolites increased with age of the monkey. Black-Right-Pointing-Pointer Gut wall metabolism of orally administered BPA was substantial across all ages of monkeys. Black-Right-Pointing-Pointer Aglycone BPA plasma concentrations were predicted in humans orally given oral doses of deuterated BPA.« less

Development of internal models and predictive abilities for visual tracking during childhood

PubMed Central

Ego, Caroline; Yüksel, Demet

2015-01-01

The prediction of the consequences of our own actions through internal models is an essential component of motor control. Previous studies showed improvement of anticipatory behaviors with age for grasping, drawing, and postural control. Since these actions require visual and proprioceptive feedback, these improvements might reflect both the development of internal models and the feedback control. In contrast, visual tracking of a temporarily invisible target gives specific markers of prediction and internal models for eye movements. Therefore, we recorded eye movements in 50 children (aged 5–19 yr) and in 10 adults, who were asked to pursue a visual target that is temporarily blanked. Results show that the youngest children (5–7 yr) have a general oculomotor behavior in this task, qualitatively similar to the one observed in adults. However, the overall performance of older subjects in terms of accuracy at target reappearance and variability in their behavior was much better than the youngest children. This late maturation of predictive mechanisms with age was reflected into the development of the accuracy of the internal models governing the synergy between the saccadic and pursuit systems with age. Altogether, we hypothesize that the maturation of the interaction between smooth pursuit and saccades that relies on internal models of the eye and target displacement is related to the continuous maturation of the cerebellum. PMID:26510757

Development of internal models and predictive abilities for visual tracking during childhood.

PubMed

Ego, Caroline; Yüksel, Demet; Orban de Xivry, Jean-Jacques; Lefèvre, Philippe

2016-01-01

The prediction of the consequences of our own actions through internal models is an essential component of motor control. Previous studies showed improvement of anticipatory behaviors with age for grasping, drawing, and postural control. Since these actions require visual and proprioceptive feedback, these improvements might reflect both the development of internal models and the feedback control. In contrast, visual tracking of a temporarily invisible target gives specific markers of prediction and internal models for eye movements. Therefore, we recorded eye movements in 50 children (aged 5-19 yr) and in 10 adults, who were asked to pursue a visual target that is temporarily blanked. Results show that the youngest children (5-7 yr) have a general oculomotor behavior in this task, qualitatively similar to the one observed in adults. However, the overall performance of older subjects in terms of accuracy at target reappearance and variability in their behavior was much better than the youngest children. This late maturation of predictive mechanisms with age was reflected into the development of the accuracy of the internal models governing the synergy between the saccadic and pursuit systems with age. Altogether, we hypothesize that the maturation of the interaction between smooth pursuit and saccades that relies on internal models of the eye and target displacement is related to the continuous maturation of the cerebellum. Copyright © 2016 the American Physiological Society.

Validation of maturity offset in a longitudinal sample of Polish boys.

PubMed

Malina, Robert M; Kozieł, Sławomir M

2014-01-01

Abstract This study attempted to validate an anthropometric equation for predicting age at peak height velocity (APHV) in 193 Polish boys followed longitudinally 8-18 years (1961-1972). Actual APHV was derived with Preece-Baines Model 1. Predicted APHV was estimated at each observation using chronological age (CA), stature, mass, sitting height and estimated leg length. Mean predicted APHV increased from 8 to 18 years. Actual APHV was underestimated at younger ages and overestimated at older ages. Mean differences between predicted and actual APHV were reasonably stable between 13 and 15 years. Predicted APHV underestimated actual APHV 3 years before, was almost identical with actual age 2 years before, and then overestimated actual age through 3 years after PHV. Predicted APHV did not differ among boys of contrasting maturity status 8-11 years, but diverged among groups 12-15 years. In conclusion, predicted APHV is influenced by CA and by early and late timing of actual PHV. Predicted APHV has applicability among average maturing boys 12-16 years in contrast to late and early maturing boys. Dependence upon age and individual differences in actual APHV limits utility of predicted APHV in research with male youth athletes and in talent programmes.

Methodology for the evaluation of vascular surgery manpower in France.

PubMed

Berger, L; Mace, J M; Ricco, J B; Saporta, G

2013-01-01

The French population is growing and ageing. It is expected to increase by 2.7% by 2020, and the number of individuals over 65 years of age is expected to increase by 3.3 million, a 33% increase, between 2005 and 2020. As the number of vascular surgery procedures is closely associated with the age of a population, it is anticipated that there will be a significant increase in the workload of vascular surgeons. A model is presented to predict changes in vascular surgery activity according to population ageing, including other parameters that could affect workload evolution. Three types of arterial procedures were studied: infrarenal abdominal aortic aneurysm (AAA) surgery, peripheral arterial occlusive disease (PAOD) procedures and carotid artery (CEA) procedures. Data were selected and extracted from the national PMSI (Medical Information System Program) database. Data obtained from 2000 were used to predict data based on an ageing population for 2008. From this model, a weighted index was defined for each group by comparing expected and observed workloads. According to the model, over this 8-year period, there was an overall increase in vascular procedures of 52.2%, with an increase of 89% in PAOD procedures. Between 2000 and 2009, the total increase was 58.0%, with 3.9% for AAA procedures, 101.7% for PAOD procedures and 13.2% for CEA procedures. The weighted model based on an ageing population and corrected by a weighted factor predicted this increase. This weighted model is able to predict the workload of vascular surgeons over the coming years. An ageing population and other factors could result in a significant increase in demand for vascular surgical services. Copyright © 2012 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Neonatal intensive care unit: predictive models for length of stay.

PubMed

Bender, G J; Koestler, D; Ombao, H; McCourt, M; Alskinis, B; Rubin, L P; Padbury, J F

2013-02-01

Hospital length of stay (LOS) is important to administrators and families of neonates admitted to the neonatal intensive care unit (NICU). A prediction model for NICU LOS was developed using predictors birth weight, gestational age and two severity of illness tools, the score for neonatal acute physiology, perinatal extension (SNAPPE) and the morbidity assessment index for newborns (MAIN). Consecutive admissions (n=293) to a New England regional level III NICU were retrospectively collected. Multiple predictive models were compared for complexity and goodness-of-fit, coefficient of determination (R (2)) and predictive error. The optimal model was validated prospectively with consecutive admissions (n=615). Observed and expected LOS was compared. The MAIN models had best Akaike's information criterion, highest R (2) (0.786) and lowest predictive error. The best SNAPPE model underestimated LOS, with substantial variability, yet was fairly well calibrated by birthweight category. LOS was longer in the prospective cohort than the retrospective cohort, without differences in birth weight, gestational age, MAIN or SNAPPE. LOS prediction is improved by accounting for severity of illness in the first week of life, beyond factors known at birth. Prospective validation of both MAIN and SNAPPE models is warranted.

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

PubMed

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

2013-08-01

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

The Threshold Bias Model: A Mathematical Model for the Nomothetic Approach of Suicide

PubMed Central

Folly, Walter Sydney Dutra

2011-01-01

Background Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters. Methodology/Principal Findings A simple model (the Threshold Bias Model) was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka. Conclusions/Significance The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health. PMID:21909431

The threshold bias model: a mathematical model for the nomothetic approach of suicide.

PubMed

Folly, Walter Sydney Dutra

2011-01-01

Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters. A simple model (the Threshold Bias Model) was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka. The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health.

Searching for the Kinkeepers: Historian Gender, Age, and Type 2 Diabetes Family History.

PubMed

Giordimaina, Alicia M; Sheldon, Jane P; Kiedrowski, Lesli A; Jayaratne, Toby Epstein

2015-12-01

Kinkeepers facilitate family communication and may be key to family medical history collection and dissemination. Middle-aged women are frequently kinkeepers. Using type 2 diabetes (T2DM) as a model, we explored whether the predicted gender and age effects of kinkeeping can be extended to family medical historians. Through a U.S. telephone survey, nondiabetic Mexican Americans (n = 385), Blacks (n = 387), and Whites (n = 396) reported family histories of T2DM. Negative binomial regressions used age and gender to predict the number of affected relatives reported. Models were examined for the gender gap, parabolic age effect, and gender-by-age interaction predicted by kinkeeping. Results demonstrated support for gender and parabolic age effects but only among Whites. Kinkeeping may have application to the study of White family medical historians, but not Black or Mexican American historians, perhaps because of differences in family structure, salience of T2DM, and/or gender roles. © 2015 Society for Public Health Education.

Resting-State Functional Connectivity Predicts Cognitive Impairment Related to Alzheimer's Disease.

PubMed

Lin, Qi; Rosenberg, Monica D; Yoo, Kwangsun; Hsu, Tiffany W; O'Connell, Thomas P; Chun, Marvin M

2018-01-01

Resting-state functional connectivity (rs-FC) is a promising neuromarker for cognitive decline in aging population, based on its ability to reveal functional differences associated with cognitive impairment across individuals, and because rs-fMRI may be less taxing for participants than task-based fMRI or neuropsychological tests. Here, we employ an approach that uses rs-FC to predict the Alzheimer's Disease Assessment Scale (11 items; ADAS11) scores, which measure overall cognitive functioning, in novel individuals. We applied this technique, connectome-based predictive modeling, to a heterogeneous sample of 59 subjects from the Alzheimer's Disease Neuroimaging Initiative, including normal aging, mild cognitive impairment, and AD subjects. First, we built linear regression models to predict ADAS11 scores from rs-FC measured with Pearson's r correlation. The positive network model tested with leave-one-out cross validation (LOOCV) significantly predicted individual differences in cognitive function from rs-FC. In a second analysis, we considered other functional connectivity features, accordance and discordance, which disentangle the correlation and anticorrelation components of activity timecourses between brain areas. Using partial least square regression and LOOCV, we again built models to successfully predict ADAS11 scores in novel individuals. Our study provides promising evidence that rs-FC can reveal cognitive impairment in an aging population, although more development is needed for clinical application.

Development of a lifetime prediction model for lithium-ion batteries based on extended accelerated aging test data

NASA Astrophysics Data System (ADS)

Ecker, Madeleine; Gerschler, Jochen B.; Vogel, Jan; Käbitz, Stefan; Hust, Friedrich; Dechent, Philipp; Sauer, Dirk Uwe

2012-10-01

Battery lifetime prognosis is a key requirement for successful market introduction of electric and hybrid vehicles. This work aims at the development of a lifetime prediction approach based on an aging model for lithium-ion batteries. A multivariable analysis of a detailed series of accelerated lifetime experiments representing typical operating conditions in hybrid electric vehicle is presented. The impact of temperature and state of charge on impedance rise and capacity loss is quantified. The investigations are based on a high-power NMC/graphite lithium-ion battery with good cycle lifetime. The resulting mathematical functions are physically motivated by the occurring aging effects and are used for the parameterization of a semi-empirical aging model. An impedance-based electric-thermal model is coupled to the aging model to simulate the dynamic interaction between aging of the battery and the thermal as well as electric behavior. Based on these models different drive cycles and management strategies can be analyzed with regard to their impact on lifetime. It is an important tool for vehicle designers and for the implementation of business models. A key contribution of the paper is the parameterization of the aging model by experimental data, while aging simulation in the literature usually lacks a robust empirical foundation.

Bioenergetic and pharmacokinetic model for exposure of common loon (Gavia immer) chicks to methylmercury

USGS Publications Warehouse

Karasov, W.H.; Kenow, K.P.; Meyer, M.W.; Fournier, F.

2007-01-01

A bioenergetics model was used to predict food intake of common loon (Gavia immer) chicks as a function of body mass during development, and a pharmacokinetics model, based on first-order kinetics in a single compartment, was used to predict blood Hg level as a function of food intake rate, food Hg content, body mass, and Hg absorption and elimination. Predictions were tested in captive growing chicks fed trout (Salmo gairdneri) with average MeHg concentrations of 0.02 (control), 0.4, and 1.2 ??g/g wet mass (delivered as CH3HgCl). Predicted food intake matched observed intake through 50 d of age but then exceeded observed intake by an amount that grew progressively larger with age, reaching a significant overestimate of 28% by the end of the trial. Respiration in older, nongrowing birds probably was overestimated by using rates measured in younger, growing birds. Close agreement was found between simulations and measured blood Hg, which varied significantly with dietary Hg and age. Although chicks may hatch with different blood Hg levels, their blood level is determined mainly by dietary Hg level beyond approximately two weeks of age. The model also may be useful for predicting Hg levels in adults and in the eggs that they lay, but its accuracy in both chicks and adults needs to be tested in free-living birds. ?? 2007 SETAC.

An Individual-Tree Growth and Yield Prediction System for Even-Aged Natural Shortleaf Pine Forests

Treesearch

Thomas B. Lynch; Kenneth L. Hitch; Michael M. Huebschmann; Paul A. Murphy

1999-01-01

The development of a system of equations that model the growth and development of even-aged natural shortleaf (Pinus echinata Mill.) pine forests is described. The growth prediction system is a distance-independent individual-tree simulator containing equations that predict basal-area growth, survival, total and merchantable heights, and total and...

Cognitive Components Underpinning the Development of Model-Based Learning

PubMed Central

Potter, Tracey C.S.; Bryce, Nessa V.; Hartley, Catherine A.

2016-01-01

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. PMID:27825732

Cognitive components underpinning the development of model-based learning.

PubMed

Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

2017-06-01

Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prostate cancer prediction using the random forest algorithm that takes into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen.

PubMed

Xiao, Li-Hong; Chen, Pei-Ran; Gou, Zhong-Ping; Li, Yong-Zhong; Li, Mei; Xiang, Liang-Cheng; Feng, Ping

2017-01-01

The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels (P < 0.001), as well as in all transrectal ultrasound characteristics (P < 0.05) except uneven echo (P = 0.609). The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy.

A risk score for the prediction of advanced age-related macular degeneration: Development and validation in 2 prospective cohorts

USDA-ARS?s Scientific Manuscript database

We aimed to develop an eye specific model which used readily available information to predict risk for advanced age-related macular degeneration (AMD). We used the Age-Related Eye Disease Study (AREDS) as our training dataset, which consisted of the 4,507 participants (contributing 1,185 affected v...

Leaf Aging of Amazonian Canopy Trees: Insights to Tropical Ecological Processes and Satellited Detected Canopy Dynamics

NASA Astrophysics Data System (ADS)

Chavana-Bryant, C.; Malhi, Y.; Gerard, F.

2015-12-01

Leaf aging is a fundamental driver of changes in leaf traits, thereby, regulating ecosystem processes and remotely-sensed canopy dynamics. Leaf age is particularly important for carbon-rich tropical evergreen forests, as leaf demography (leaf age distribution) has been proposed as a major driver of seasonal productivity in these forests. We explore leaf reflectance as a tool to monitor leaf age and develop a novel spectra-based (PLSR) model to predict age using data from a phenological study of 1,072 leaves from 12 lowland Amazonian canopy tree species in southern Peru. Our results demonstrate monotonic decreases in LWC and Pmass and increase in LMA with age across species; Nmass and Cmassshowed monotonic but species-specific age responses. Spectrally, we observed large age-related variation across species, with the most age-sensitive spectral domains found to be: green peak (550nm), red edge (680-750 nm), NIR (700-850 nm), and around the main water absorption features (~1450 and ~1940 nm). A spectra-based model was more accurate in predicting leaf age (R2= 0.86; %RMSE= 33) compared to trait-based models using single (R2=0.07 to 0.73; %RMSE=7 to 38) and multiple predictors (step-wise analysis; R2=0.76; %RMSE=28). Spectral and trait-based models established a physiochemical basis for the spectral age model. The relative importance of the traits modifying the leaf spectra of aging leaves was: LWC>LMA>Nmass>Pmass,&Cmass. Vegetation indices (VIs), including NDVI, EVI2, NDWI and PRI were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity, and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

Predicting Longitudinal Change in Language Production and Comprehension in Individuals with Down Syndrome: Hierarchical Linear Modeling.

ERIC Educational Resources Information Center

Chapman, Robin S.; Hesketh, Linda J.; Kistler, Doris J.

2002-01-01

Longitudinal change in syntax comprehension and production skill, measured over six years, was modeled in 31 individuals (ages 5-20) with Down syndrome. The best fitting Hierarchical Linear Modeling model of comprehension uses age and visual and auditory short-term memory as predictors of initial status, and age for growth trajectory. (Contains…

New models for age estimation and assessment of their accuracy using developing mandibular third molar teeth in a Thai population.

PubMed

Duangto, P; Iamaroon, A; Prasitwattanaseree, S; Mahakkanukrauh, P; Janhom, A

2017-03-01

Age estimation using developing third molar teeth is considered an important and accurate technique for both clinical and forensic practices. The aims of this study were to establish population-specific reference data, to develop age prediction models using mandibular third molar development, to test the accuracy of the resulting models, and to find the probability of persons being at the age thresholds of legal relevance in a Thai population. A total of 1867 digital panoramic radiographs of Thai individuals aged between 8 and 23 years was selected to assess dental age. The mandibular third molar development was divided into nine stages. The stages were evaluated and each stage was transformed into a development score. Quadratic regression was employed to develop age prediction models. Our results show that males reached mandibular third molar root formation stages earlier than females. The models revealed a high correlation coefficient for both left and right mandibular third molar teeth in both sexes (R = 0.945 and 0.944 in males, R = 0.922 and 0.923 in females, respectively). Furthermore, the accuracy of the resulting models was tested in randomly selected 374 cases and showed low error values between the predicted dental age and the chronological age for both left and right mandibular third molar teeth in both sexes (-0.13 and -0.17 years in males, 0.01 and 0.03 years in females, respectively). In Thai samples, when the mandibular third molar teeth reached stage H, the probability of the person being over 18 years was 100 % in both sexes.

A study of the 200-metre fast walk test as a possible new assessment tool to predict maximal heart rate and define target heart rate for exercise training of coronary heart disease patients.

PubMed

Casillas, Jean-Marie; Joussain, Charles; Gremeaux, Vincent; Hannequin, Armelle; Rapin, Amandine; Laurent, Yves; Benaïm, Charles

2015-02-01

To develop a new predictive model of maximal heart rate based on two walking tests at different speeds (comfortable and brisk walking) as an alternative to a cardiopulmonary exercise test during cardiac rehabilitation. Evaluation of a clinical assessment tool. A Cardiac Rehabilitation Department in France. A total of 148 patients (133 men), mean age of 59 ±9 years, at the end of an outpatient cardiac rehabilitation programme. Patients successively performed a 6-minute walk test, a 200 m fast-walk test (200mFWT), and a cardiopulmonary exercise test, with measure of heart rate at the end of each test. An all-possible regression procedure was used to determine the best predictive regression models of maximal heart rate. The best model was compared with the Fox equation in term of predictive error of maximal heart rate using the paired t-test. Results of the two walking tests correlated significantly with maximal heart rate determined during the cardiopulmonary exercise test, whereas anthropometric parameters and resting heart rate did not. The simplified predictive model with the most acceptable mean error was: maximal heart rate = 130 - 0.6 × age + 0.3 × HR200mFWT (R(2) = 0.24). This model was superior to the Fox formula (R(2) = 0.138). The relationship between training target heart rate calculated from measured reserve heart rate and that established using this predictive model was statistically significant (r = 0.528, p < 10(-6)). A formula combining heart rate measured during a safe simple fast walk test and age is more efficient than an equation only including age to predict maximal heart rate and training target heart rate. © The Author(s) 2014.

Predictors of transitions from single to multiple job holding: Results of a longitudinal study among employees aged 45-64 in the Netherlands.

PubMed

Bouwhuis, Stef; Geuskens, Goedele A; Boot, Cécile R L; Bongers, Paulien M; van der Beek, Allard J

2017-08-01

To construct prediction models for transitions to combination multiple job holding (MJH) (multiple jobs as an employee) and hybrid MJH (being an employee and self-employed), among employees aged 45-64. A total of 5187 employees in the Netherlands completed online questionnaires annually between 2010 and 2013. We applied logistic regression analyses with a backward elimination strategy to construct prediction models. Transitions to combination MJH and hybrid MJH were best predicted by a combination of factors including: demographics, health and mastery, work characteristics, work history, skills and knowledge, social factors, and financial factors. Not having a permanent contract and a poor household financial situation predicted both transitions. Some predictors only predicted combination MJH, e.g., working part-time, or hybrid MJH, e.g., work-home interference. A wide variety of factors predict combination MJH and/or hybrid MJH. The prediction model approach allowed for the identification of predictors that have not been previously studied. © 2017 Wiley Periodicals, Inc.

Performance Comparison of Systemic Inflammatory Response Syndrome with Logistic Regression Models to Predict Sepsis in Neonates

PubMed Central

Thakur, Jyoti; Pahuja, Sharvan Kumar; Pahuja, Roop

2017-01-01

In 2005, an international pediatric sepsis consensus conference defined systemic inflammatory response syndrome (SIRS) for children <18 years of age, but excluded premature infants. In 2012, Hofer et al. investigated the predictive power of SIRS for term neonates. In this paper, we examined the accuracy of SIRS in predicting sepsis in neonates, irrespective of their gestational age (i.e., pre-term, term, and post-term). We also created two prediction models, named Model A and Model B, using binary logistic regression. Both models performed better than SIRS. We also developed an android application so that physicians can easily use Model A and Model B in real-world scenarios. The sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR) in cases of SIRS were 16.15%, 95.53%, 3.61, and 0.88, respectively, whereas they were 29.17%, 97.82%, 13.36, and 0.72, respectively, in the case of Model A, and 31.25%, 97.30%, 11.56, and 0.71, respectively, in the case of Model B. All models were significant with p < 0.001. PMID:29257099

Exploring tropical forest vegetation dynamics using the FATES model

NASA Astrophysics Data System (ADS)

Koven, C. D.; Fisher, R.; Knox, R. G.; Chambers, J.; Kueppers, L. M.; Christoffersen, B. O.; Davies, S. J.; Dietze, M.; Holm, J.; Massoud, E. C.; Muller-Landau, H. C.; Powell, T.; Serbin, S.; Shuman, J. K.; Walker, A. P.; Wright, S. J.; Xu, C.

2017-12-01

Tropical forest vegetation dynamics represent a critical climate feedback in the Earth system, which is poorly represented in current global modeling approaches. We discuss recent progress on exploring these dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a demographic vegetation model for the CESM and ACME ESMs. We will discuss benchmarks of FATES predictions for forest structure against inventory sites, sensitivity of FATES predictions of size and age structure to model parameter uncertainty, and experiments using the FATES model to explore PFT competitive dynamics and the dynamics of size and age distributions in responses to changing climate and CO2.

Machine Learning Techniques for Prediction of Early Childhood Obesity.

PubMed

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Prediction equations of forced oscillation technique: the insidious role of collinearity.

PubMed

Narchi, Hassib; AlBlooshi, Afaf

2018-03-27

Many studies have reported reference data for forced oscillation technique (FOT) in healthy children. The prediction equation of FOT parameters were derived from a multivariable regression model examining the effect of age, gender, weight and height on each parameter. As many of these variables are likely to be correlated, collinearity might have affected the accuracy of the model, potentially resulting in misleading, erroneous or difficult to interpret conclusions.The aim of this work was: To review all FOT publications in children since 2005 to analyze whether collinearity was considered in the construction of the published prediction equations. Then to compare these prediction equations with our own study. And to analyse, in our study, how collinearity between the explanatory variables might affect the predicted equations if it was not considered in the model. The results showed that none of the ten reviewed studies had stated whether collinearity was checked for. Half of the reports had also included in their equations variables which are physiologically correlated, such as age, weight and height. The predicted resistance varied by up to 28% amongst these studies. And in our study, multicollinearity was identified between the explanatory variables initially considered for the regression model (age, weight and height). Ignoring it would have resulted in inaccuracies in the coefficients of the equation, their signs (positive or negative), their 95% confidence intervals, their significance level and the model goodness of fit. In Conclusion with inaccurately constructed and improperly reported models, understanding the results and reproducing the models for future research might be compromised.

Phenomenological Modeling and Laboratory Simulation of Long-Term Aging of Asphalt Mixtures

NASA Astrophysics Data System (ADS)

Elwardany, Michael Dawoud

The accurate characterization of asphalt mixture properties as a function of pavement service life is becoming more important as more powerful pavement design and performance prediction methods are implemented. Oxidative aging is a major distress mechanism of asphalt pavements. Aging increases the stiffness and brittleness of the material, which leads to a high cracking potential. Thus, an improved understanding of the aging phenomenon and its effect on asphalt binder chemical and rheological properties will allow for the prediction of mixture properties as a function of pavement service life. Many researchers have conducted laboratory binder thin-film aging studies; however, this approach does not allow for studying the physicochemical effects of mineral fillers on age hardening rates in asphalt mixtures. Moreover, aging phenomenon in the field is governed by kinetics of binder oxidation, oxygen diffusion through mastic phase, and oxygen percolation throughout the air voids structure. In this study, laboratory aging trials were conducted on mixtures prepared using component materials of several field projects throughout the USA and Canada. Laboratory aged materials were compared against field cores sampled at different ages. Results suggested that oven aging of loose mixture at 95°C is the most promising laboratory long-term aging method. Additionally, an empirical model was developed in order to account for the effect of mineral fillers on age hardening rates in asphalt mixtures. Kinetics modeling was used to predict field aging levels throughout pavement thickness and to determine the required laboratory aging duration to match field aging. Kinetics model outputs are calibrated using measured data from the field to account for the effects of oxygen diffusion and percolation. Finally, the calibrated model was validated using independent set of field sections. This work is expected to provide basis for improved asphalt mixture and pavement design procedures in order to save taxpayers' money.

Improved age determination of blood and teeth samples using a selected set of DNA methylation markers

PubMed Central

Kamalandua, Aubeline

2015-01-01

Age estimation from DNA methylation markers has seen an exponential growth of interest, not in the least from forensic scientists. The current published assays, however, can still be improved by lowering the number of markers in the assay and by providing more accurate models to predict chronological age. From the published literature we selected 4 age-associated genes (ASPA, PDE4C, ELOVL2, and EDARADD) and determined CpG methylation levels from 206 blood samples of both deceased and living individuals (age range: 0–91 years). This data was subsequently used to compare prediction accuracy with both linear and non-linear regression models. A quadratic regression model in which the methylation levels of ELOVL2 were squared showed the highest accuracy with a Mean Absolute Deviation (MAD) between chronological age and predicted age of 3.75 years and an adjusted R2 of 0.95. No difference in accuracy was observed for samples obtained either from living and deceased individuals or between the 2 genders. In addition, 29 teeth from different individuals (age range: 19–70 years) were analyzed using the same set of markers resulting in a MAD of 4.86 years and an adjusted R2 of 0.74. Cross validation of the results obtained from blood samples demonstrated the robustness and reproducibility of the assay. In conclusion, the set of 4 CpG DNA methylation markers is capable of producing highly accurate age predictions for blood samples from deceased and living individuals PMID:26280308

Effect of age on the performance of bispectral and entropy indices during sevoflurane pediatric anesthesia: a pharmacometric study.

PubMed

Sciusco, Alberto; Standing, Joseph F; Sheng, Yucheng; Raimondo, Pasquale; Cinnella, Gilda; Dambrosio, Michele

2017-04-01

Bispectral index (BIS) and entropy monitors have been proposed for use in children, but research has not supported their validity for infants. However, effective monitoring of young children may be even more important than for adults, to aid appropriate anesthetic dosing and reduce the chance of adverse consequences. This prospective study aimed to investigate the relationships between age and the predictive performance of BIS and entropy monitors in measuring the anesthetic drug effects within a pediatric surgery setting. We concurrently recorded BIS and entropy (SE/RE) in 48 children aged 1 month-12 years, undergoing general anesthesia with sevoflurane and fentanyl. Nonlinear mixed effects modeling was used to characterize the concentration-response relationship independently between the three monitor indicators with sevoflurane. The model's goodness-of-fit was assessed by prediction-corrected visual predictive checks. Model fit with age was evaluated using absolute conditional individual weighted residuals (|CIWRES|). The ability of BIS and entropy monitors to describe the effect of anesthesia was compared with prediction probabilities (P K ) in different age groups. Intraoperative and awakening values were compared in the age groups. The correlation between BIS and entropy was also calculated. |CIWRES| vs age showed an increasing trend in the model's accuracy for all three indicators. P K probabilities were similar for all three indicators within each age group, though lower in infants. The linear correlations between BIS and entropy in different age groups were lower for infants. Infants also tended to have lower values during surgery and at awakening than older children, while toddlers had higher values. Performance of both monitors improves as age increases. Our results suggest a need for the development of new monitor algorithms or calibration to better account for the age-specific EEG dynamics of younger patients. © 2017 John Wiley & Sons Ltd.

Future Time Perspective and Awareness of Age-Related Change: Examining their Role in Predicting Psychological Well-Being

PubMed Central

Brothers, Allyson; Gabrian, Martina; Wahl, Hans-Werner; Diehl, Manfred

2016-01-01

This study examined how two distinct facets of perceived personal lifetime – future time perspective (FTP) and awareness of age-related change (AARC) – are associated with one another, and how they may interact to predict psychological well-being. To better understand associations among subjective perceptions of lifetime, aging and well-being, we tested a series of models to investigate questions of directionality, indirect effects, and conditional processes among FTP, AARC-Gains, AARC-Losses, and psychological well-being. In all models, we tested for differences between middle-aged and older adults, and between adults from the U.S. and Germany. Analyses were conducted within a structural equation modeling framework on a cross-national, 2.5-year longitudinal sample of 537 community-residing adults (age 40–98 years). Awareness of age-related losses (AARC-Losses) at Time 1 predicted FTP at Time 2, but FTP did not predict AARC-Gains or AARC-Losses. Furthermore, future time perspective mediated the association between AARC-Losses and well-being. Moderation analyses revealed a buffering effect of awareness of age-related gains (AARC-Gains) in which perceptions of more age-related gains diminished the negative effect of a limited future time perspective on well-being. Effects were robust across age groups and countries. Taken together, these findings suggest that perceived age-related loss experiences may sensitize individuals to perceive a more limited future lifetime which may then lead to lower psychological well-being. In contrast, perceived age-related gains may function as a resource to preserve psychological well-being, in particular when time is perceived as running out. PMID:27243764

Future time perspective and awareness of age-related change: Examining their role in predicting psychological well-being.

PubMed

Brothers, Allyson; Gabrian, Martina; Wahl, Hans-Werner; Diehl, Manfred

2016-09-01

This study examined how 2 distinct facets of perceived personal lifetime-future time perspective (FTP) and awareness of age-related change (AARC)-are associated with another, and how they may interact to predict psychological well-being. To better understand associations among subjective perceptions of lifetime, aging, and well-being, we tested a series of models to investigate questions of directionality, indirect effects, and conditional processes among FTP, AARC-Gains, AARC-Losses, and psychological well-being. In all models, we tested for differences between middle-aged and older adults, and between adults from the United States and Germany. Analyses were conducted within a structural equation modeling framework on a cross-national, 2.5-year longitudinal sample of 537 community-residing adults (age 40-98 years). Awareness of age-related losses (AARC-Losses) at Time 1 predicted FTP at Time 2, but FTP did not predict AARC-Gains or AARC-Losses. Furthermore, future time perspective mediated the association between AARC-Losses and well-being. Moderation analyses revealed a buffering effect of awareness of age-related gains (AARC-Gains) in which perceptions of more age-related gains diminished the negative effect of a limited future time perspective on well-being. Effects were robust across age groups and countries. Taken together, these findings suggest that perceived age-related loss experiences may sensitize individuals to perceive a more limited future lifetime which may then lead to lower psychological well-being. In contrast, perceived age-related gains may function as a resource to preserve psychological well-being, in particular when time is perceived as running out. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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

Royer, Michael P.; Houser, Kevin W.

An experiment was conducted to examine the effect of tuning optical radiation on brightness perception for younger (18-25 years of age) and older (50 years of age or older) observers. Participants made forced-choice evaluations of the brightness of a full factorial of stimulus pairs selected from two groups of four metameric stimuli. The large-field stimuli were created by systematically varying either the red or the blue primary of an RGB LED mixture. The results indicate that light stimuli of equal illuminance and chromaticity do not appear equally bright to either younger or older subjects. The rank-order of brightness is notmore » predicted by any current model of human vision or theory of brightness perception including Scotopic to Photopic or Cirtopic to Photopic ratio theory, prime color theory, correlated color temperature, V(λ)-based photometry, color quality metrics, linear brightness models, or color appearance models. Age may affect brightness perception when short-wavelength primaries are used, especially those with a peak wavelength shorter than 450 nm. The results suggest further development of metrics to predict brightness perception is warranted, and that including age as a variable in predictive models may be valuable.« less

Field Validation of a Transcriptional Assay for the Prediction of Age of Uncaged Aedes aegypti Mosquitoes in Northern Australia

PubMed Central

Hugo, Leon E.; Cook, Peter E.; Johnson, Petrina H.; Rapley, Luke P.; Kay, Brian H.; Ryan, Peter A.; Ritchie, Scott A.; O'Neill, Scott L.

2010-01-01

Background New strategies to eliminate dengue have been proposed that specifically target older Aedes aegypti mosquitoes, the proportion of the vector population that is potentially capable of transmitting dengue viruses. Evaluation of these strategies will require accurate and high-throughput methods of predicting mosquito age. We previously developed an age prediction assay for individual Ae. aegypti females based on the transcriptional profiles of a selection of age responsive genes. Here we conducted field testing of the method on Ae. aegypti that were entirely uncaged and free to engage in natural behavior. Methodology/Principal Findings We produced “free-range” test specimens by releasing 8007 adult Ae. aegypti inside and around an isolated homestead in north Queensland, Australia, and recapturing females at two day intervals. We applied a TaqMan probe-based assay design that enabled high-throughput quantitative RT-PCR of four transcripts from three age-responsive genes and a reference gene. An age prediction model was calibrated on mosquitoes maintained in small sentinel cages, in which 68.8% of the variance in gene transcription measures was explained by age. The model was then used to predict the ages of the free-range females. The relationship between the predicted and actual ages achieved an R2 value of 0.62 for predictions of females up to 29 days old. Transcriptional profiles and age predictions were not affected by physiological variation associated with the blood feeding/egg development cycle and we show that the age grading method could be applied to differentiate between two populations of mosquitoes having a two-fold difference in mean life expectancy. Conclusions/Significance The transcriptional profiles of age responsive genes facilitated age estimates of near-wild Ae. aegypti females. Our age prediction assay for Ae. aegypti provides a useful tool for the evaluation of mosquito control interventions against dengue where mosquito survivorship or lifespan reduction are crucial to their success. The approximate cost of the method was US$7.50 per mosquito and 60 mosquitoes could be processed in 3 days. The assay is based on conserved genes and modified versions are likely to support similar investigations of several important mosquito and other disease vectors. PMID:20186322

Brain Natriuretic Hormone Predicts Stress Induced Alterations in Diastolic Function

PubMed Central

Choksy, Pratik; Davis, Harry C.; Januzzi, James; Thayer, Julian; Harshfield, Gregory; Robinson, Vincent JB; Kapuku, Gaston K.

2015-01-01

Background Mental stress (MS) reduces diastolic function (DF) and may lead to congestive heart failure with preserved systolic function. Whether brain natriuretic hormone (BNP) mediates the relationship of MS with DF is unknown. Method and Results 160 individuals aged 30 to 50 years underwent 2 hour protocol of 40 minutes rest, videogame stressor and recovery. Hemodynamics, pro-BNP samples and DF indices were obtained throughout the protocol. Separate regression analyses were conducted using rest and stress E/A, E’ and E/E’ as dependent variables. Predictor variables were entered into the stepwise regression models in a hierarchical fashion. At the first level age, sex, race, height, BMI, pro-BNP, and LVM were permitted to enter the models. The second level consisted of SBP, DBP and HR. The final level contained cross-product terms of race by SBP, DBP and HR. E/A ratio was lower during stress compared to rest, and recovery (p<0.01). Resting E/A ratio was predicted by a regression model of age (−.31), pro-BNP (.16), HR (−.40) and DBP (−.23) with an R2 = .33. Stress E/A ratio was predicted by age (−.24), pro-BNP (.08), HR (−.38), and SBP (−.21), total R2 = .22. Resting E’ model consisted of age (−.22), pro-BNP (.26), DBP (−.27) and LVM (−.15) with an R2 = .29. Stress E’ was predicted by age (−.18), pro-BNP (.35) and LVM (−.18) with an R2 = .18. Resting E/E’ was predicted by race (.17, B>W) and DBP (.24) with an R2 = .10. Stress E/E’ consisted of pro-BNP (−.36), height (−.26) and HR (−.21) with R2 = .15. Conclusion pro-BNP predicts both resting and stress DF suggesting that lower BNP during MS may be a maker of diastolic dysfunction in apparently healthy individuals. PMID:24841419

Developing a Comprehensive Model of Risk and Protective Factors That Can Predict Spelling at Age Seven: Findings from a Community Sample of Victorian Children

ERIC Educational Resources Information Center

Serry, Tanya Anne; Castles, Anne; Mensah, Fiona K.; Bavin, Edith L.; Eadie, Patricia; Pezic, Angela; Prior, Margot; Bretherton, Lesley; Reilly, Sheena

2015-01-01

The paper reports on a study designed to develop a risk model that can best predict single-word spelling in seven-year-old children when they were aged 4 and 5. Test measures, personal characteristics and environmental influences were all considered as variables from a community sample of 971 children. Strong concurrent correlations were found…

Validity of VO(2 max) in predicting blood volume: implications for the effect of fitness on aging

NASA Technical Reports Server (NTRS)

Convertino, V. A.; Ludwig, D. A.

2000-01-01

A multiple regression model was constructed to investigate the premise that blood volume (BV) could be predicted using several anthropometric variables, age, and maximal oxygen uptake (VO(2 max)). To test this hypothesis, age, calculated body surface area (height/weight composite), percent body fat (hydrostatic weight), and VO(2 max) were regressed on to BV using data obtained from 66 normal healthy men. Results from the evaluation of the full model indicated that the most parsimonious result was obtained when age and VO(2 max) were regressed on BV expressed per kilogram body weight. The full model accounted for 52% of the total variance in BV per kilogram body weight. Both age and VO(2 max) were related to BV in the positive direction. Percent body fat contributed <1% to the explained variance in BV when expressed in absolute BV (ml) or as BV per kilogram body weight. When the model was cross validated on 41 new subjects and BV per kilogram body weight was reexpressed as raw BV, the results indicated that the statistical model would be stable under cross validation (e.g., predictive applications) with an accuracy of +/- 1,200 ml at 95% confidence. Our results support the hypothesis that BV is an increasing function of aerobic fitness and to a lesser extent the age of the subject. The results may have implication as to a mechanism by which aerobic fitness and activity may be protective against reduced BV associated with aging.

Childhood temperament and family environment as predictors of internalizing and externalizing trajectories from ages 5 to 17.

PubMed

Leve, Leslie D; Kim, Hyoun K; Pears, Katherine C

2005-10-01

Childhood temperament and family environment have been shown to predict internalizing and externalizing behavior; however, less is known about how temperament and family environment interact to predict changes in problem behavior. We conducted latent growth curve modeling on a sample assessed at ages 5, 7, 10, 14, and 17 (N = 337). Externalizing behavior decreased over time for both sexes, and internalizing behavior increased over time for girls only. Two childhood variables (fear/shyness and maternal depression) predicted boys' and girls' age-17 internalizing behavior, harsh discipline uniquely predicted boys' age-17 internalizing behavior, and maternal depression and lower family income uniquely predicted increases in girls' internalizing behavior. For externalizing behavior, an array of temperament, family environment, and Temperament x Family Environment variables predicted age-17 behavior for both sexes. Sex differences were present in the prediction of externalizing slopes, with maternal depression predicting increases in boys' externalizing behavior only when impulsivity was low, and harsh discipline predicting increases in girls' externalizing behavior only when impulsivity was high or when fear/shyness was low.

Epidemiology and Long-term Clinical and Biologic Risk Factors for Pneumonia in Community-Dwelling Older Americans

PubMed Central

Alvarez, Karina; Loehr, Laura; Folsom, Aaron R.; Newman, Anne B.; Weissfeld, Lisa A.; Wunderink, Richard G.; Kritchevsky, Stephen B.; Mukamal, Kenneth J.; London, Stephanie J.; Harris, Tamara B.; Bauer, Doug C.; Angus, Derek C.

2013-01-01

Background: Preventing pneumonia requires better understanding of incidence, mortality, and long-term clinical and biologic risk factors, particularly in younger individuals. Methods: This was a cohort study in three population-based cohorts of community-dwelling individuals. A derivation cohort (n = 16,260) was used to determine incidence and survival and develop a risk prediction model. The prediction model was validated in two cohorts (n = 8,495). The primary outcome was 10-year risk of pneumonia hospitalization. Results: The crude and age-adjusted incidences of pneumonia were 6.71 and 9.43 cases/1,000 person-years (10-year risk was 6.15%). The 30-day and 1-year mortality were 16.5% and 31.5%. Although age was the most important risk factor (range of crude incidence rates, 1.69-39.13 cases/1,000 person-years for each 5-year increment from 45-85 years), 38% of pneumonia cases occurred in adults < 65 years of age. The 30-day and 1-year mortality were 12.5% and 25.7% in those < 65 years of age. Although most comorbidities were associated with higher risk of pneumonia, reduced lung function was the most important risk factor (relative risk = 6.61 for severe reduction based on FEV1 by spirometry). A clinical risk prediction model based on age, smoking, and lung function predicted 10-year risk (area under curve [AUC] = 0.77 and Hosmer-Lemeshow [HL] C statistic = 0.12). Model discrimination and calibration were similar in the internal validation cohort (AUC = 0.77; HL C statistic, 0.65) but lower in the external validation cohort (AUC = 0.62; HL C statistic, 0.45). The model also calibrated well in blacks and younger adults. C-reactive protein and IL-6 were associated with higher pneumonia risk but did not improve model performance. Conclusions: Pneumonia hospitalization is common and associated with high mortality, even in younger healthy adults. Long-term risk of pneumonia can be predicted in community-dwelling adults with a simple clinical risk prediction model. PMID:23744106

Ageing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuits

PubMed Central

Wolpe, Noham; Ingram, James N.; Tsvetanov, Kamen A.; Geerligs, Linda; Kievit, Rogier A.; Henson, Richard N.; Wolpert, Daniel M.; Tyler, Lorraine K.; Brayne, Carol; Bullmore, Edward; Calder, Andrew; Cusack, Rhodri; Dalgleish, Tim; Duncan, John; Matthews, Fiona E.; Marslen-Wilson, William; Shafto, Meredith A.; Campbell, Karen; Cheung, Teresa; Davis, Simon; McCarrey, Anna; Mustafa, Abdur; Price, Darren; Samu, David; Taylor, Jason R.; Treder, Matthias; van Belle, Janna; Williams, Nitin; Bates, Lauren; Emery, Tina; Erzinçlioglu, Sharon; Gadie, Andrew; Gerbase, Sofia; Georgieva, Stanimira; Hanley, Claire; Parkin, Beth; Troy, David; Auer, Tibor; Correia, Marta; Gao, Lu; Green, Emma; Henriques, Rafael; Allen, Jodie; Amery, Gillian; Amunts, Liana; Barcroft, Anne; Castle, Amanda; Dias, Cheryl; Dowrick, Jonathan; Fair, Melissa; Fisher, Hayley; Goulding, Anna; Grewal, Adarsh; Hale, Geoff; Hilton, Andrew; Johnson, Frances; Johnston, Patricia; Kavanagh-Williamson, Thea; Kwasniewska, Magdalena; McMinn, Alison; Norman, Kim; Penrose, Jessica; Roby, Fiona; Rowland, Diane; Sargeant, John; Squire, Maggie; Stevens, Beth; Stoddart, Aldabra; Stone, Cheryl; Thompson, Tracy; Yazlik, Ozlem; Barnes, Dan; Dixon, Marie; Hillman, Jaya; Mitchell, Joanne; Villis, Laura; Rowe, James B.

2016-01-01

The control of voluntary movement changes markedly with age. A critical component of motor control is the integration of sensory information with predictions of the consequences of action, arising from internal models of movement. This leads to sensorimotor attenuation—a reduction in the perceived intensity of sensations from self-generated compared with external actions. Here we show that sensorimotor attenuation occurs in 98% of adults in a population-based cohort (n=325; 18–88 years; the Cambridge Centre for Ageing and Neuroscience). Importantly, attenuation increases with age, in proportion to reduced sensory sensitivity. This effect is associated with differences in the structure and functional connectivity of the pre-supplementary motor area (pre-SMA), assessed with magnetic resonance imaging. The results suggest that ageing alters the balance between the sensorium and predictive models, mediated by the pre-SMA and its connectivity in frontostriatal circuits. This shift may contribute to the motor and cognitive changes observed with age. PMID:27694879

Ageing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuits.

PubMed

Wolpe, Noham; Ingram, James N; Tsvetanov, Kamen A; Geerligs, Linda; Kievit, Rogier A; Henson, Richard N; Wolpert, Daniel M; Rowe, James B

2016-10-03

The control of voluntary movement changes markedly with age. A critical component of motor control is the integration of sensory information with predictions of the consequences of action, arising from internal models of movement. This leads to sensorimotor attenuation-a reduction in the perceived intensity of sensations from self-generated compared with external actions. Here we show that sensorimotor attenuation occurs in 98% of adults in a population-based cohort (n=325; 18-88 years; the Cambridge Centre for Ageing and Neuroscience). Importantly, attenuation increases with age, in proportion to reduced sensory sensitivity. This effect is associated with differences in the structure and functional connectivity of the pre-supplementary motor area (pre-SMA), assessed with magnetic resonance imaging. The results suggest that ageing alters the balance between the sensorium and predictive models, mediated by the pre-SMA and its connectivity in frontostriatal circuits. This shift may contribute to the motor and cognitive changes observed with age.

Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42,103 individuals

PubMed Central

Dunlop, Malcolm G.; Tenesa, Albert; Farrington, Susan M.; Ballereau, Stephane; Brewster, David H.; Pharoah, Paul DP.; Schafmayer, Clemens; Hampe, Jochen; Völzke, Henry; Chang-Claude, Jenny; Hoffmeister, Michael; Brenner, Hermann; von Holst, Susanna; Picelli, Simone; Lindblom, Annika; Jenkins, Mark A.; Hopper, John L.; Casey, Graham; Duggan, David; Newcomb, Polly; Abulí, Anna; Bessa, Xavier; Ruiz-Ponte, Clara; Castellví-Bel, Sergi; Niittymäki, Iina; Tuupanen, Sari; Karhu, Auli; Aaltonen, Lauri; Zanke, Brent W.; Hudson, Thomas J.; Gallinger, Steven; Barclay, Ella; Martin, Lynn; Gorman, Maggie; Carvajal-Carmona, Luis; Walther, Axel; Kerr, David; Lubbe, Steven; Broderick, Peter; Chandler, Ian; Pittman, Alan; Penegar, Steven; Campbell, Harry; Tomlinson, Ian; Houlston, Richard S.

2016-01-01

Objective Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. In a large, multi-population study, we set out to assess the feasibility of CRC risk prediction using common genetic variant data, combined with other risk factors. We built a risk prediction model and applied it to the Scottish population using available data. Design Nine populations of European descent were studied to develop and validate colorectal cancer risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence colorectal cancer risk. Risk models were generated from case-control data incorporating genotypes alone (n=39,266), and in combination with gender, age and family history (n=11,324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4,187 independent samples. 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks. Results Median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10−16), confirmed in external validation sets (Sweden p=1.2×10−6, Finland p=2×10−5). Mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05–1.13). Discriminative performance was poor across the risk spectrum (area under curve (AUC) for genotypes alone - 0.57; AUC for genotype/age/gender/FH - 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk. Conclusion We show that genotype data provides additional information that complements age, gender and FH as risk factors. However, individualized genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential, since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance. PMID:22490517

Does active ageing contribute to life satisfaction for older people? Testing a new model of active ageing.

PubMed

Marsillas, Sara; De Donder, Liesbeth; Kardol, Tinie; van Regenmortel, Sofie; Dury, Sarah; Brosens, Dorien; Smetcoren, An-Sofie; Braña, Teresa; Varela, Jesús

2017-09-01

Several debates have emerged across the literature about the conceptualisation of active ageing. The aim of this study is to develop a model of the construct that is focused on the individual, including different elements of people's lives that have the potential to be modified by intervention programs. Moreover, the paper examines the contributions of active ageing to life satisfaction, as well as the possible predictive role of coping styles on active ageing. For this purpose, a representative sample of 404 Galician (Spain) community-dwelling older adults (aged ≥60 years) were interviewed using a structured survey. The results demonstrate that the proposed model composed of two broad categories is valid. The model comprises status variables (related to physical, psychological, and social health) as well as different types of activities, called processual variables. This model is tested using partial least squares (PLS) regression. The findings show that active ageing is a fourth-order, formative construct. In addition, PLS analyses indicate that active ageing has a moderate and positive path on life satisfaction and that coping styles may predict active ageing. The discussion highlights the potential of active ageing as a relevant concept for people's lives, drawing out policy implications and suggestions for further research.

Accommodation and age-dependent eye model based on in vivo measurements.

PubMed

Zapata-Díaz, Juan F; Radhakrishnan, Hema; Charman, W Neil; López-Gil, Norberto

2018-03-21

To develop a flexible model of the average eye that incorporates changes with age and accommodation in all optical parameters, including entrance pupil diameter, under photopic, natural, environmental conditions. We collated retrospective in vivo measurements of all optical parameters, including entrance pupil diameter. Ray-tracing was used to calculate the wavefront aberrations of the eye model as a function of age, stimulus vergence and pupil diameter. These aberrations were used to calculate objective refraction using paraxial curvature matching. This was also done for several stimulus positions to calculate the accommodation response/stimulus curve. The model predicts a hyperopic change in distance refraction as the eye ages (+0.22D every 10 years) between 20 and 65 years. The slope of the accommodation response/stimulus curve was 0.72 for a 25 years-old subject, with little change between 20 and 45 years. A trend to a more negative value of primary spherical aberration as the eye accommodates is predicted for all ages (20-50 years). When accommodation is relaxed, a slight increase in primary spherical aberration (0.008μm every 10 years) between 20 and 65 years is predicted, for an age-dependent entrance pupil diameter ranging between 3.58mm (20 years) and 3.05mm (65 years). Results match reasonably well with studies performed in real eyes, except that spherical aberration is systematically slightly negative as compared with the practical data. The proposed eye model is able to predict changes in objective refraction and accommodation response. It has the potential to be a useful design and testing tool for devices (e.g. intraocular lenses or contact lenses) designed to correct the eye's optical errors. Copyright © 2018 Spanish General Council of Optometry. Published by Elsevier España, S.L.U. All rights reserved.

Paths to Success in Young Adulthood from Mental Health and Life Transitions in Emerging Adulthood

ERIC Educational Resources Information Center

Howard, Andrea L.; Galambos, Nancy L.; Krahn, Harvey J.

2010-01-01

This study followed a school-based sample (N = 920) to explore how trajectories of depressive symptoms and expressed anger from age 18 to 25, along with important life transitions, predicted life and career satisfaction at age 32. A two-group (women and men) bivariate growth model revealed that higher depressive symptoms at age 18 predicted lower…

Ultra-weak photon emission of hands in aging prediction.

PubMed

Zhao, Xin; van Wijk, Eduard; Yan, Yu; van Wijk, Roeland; Yang, Huanming; Zhang, Yan; Wang, Jian

2016-09-01

Aging has been one of the several topics intensely investigated during recent decades. More scientists have been scrutinizing mechanisms behind the human aging process. Ultra-weak photon emission is known as one type of spontaneous photon emission that can be detected with a highly sensitive single photon counting photomultiplier tube (PMT) from the surface of human bodies. It may reflect the body's oxidative damage. Our aim was to examine whether ultra-weak photon emission from a human hand is able to predict one's chronological age. Sixty subjects were recruited and grouped by age. We examined four areas of each hand: palm side of fingers, palm side of hand, dorsum side of fingers, and dorsum side of hand. Left and right hand were measured synchronously with two independent PMTs. Mean strength and Fano factor values of photon counts were utilized to compare the UPE patterns of males and females of different age groups. Subsequently, we utilized UPE data from the most sensitive PMT to develop an age prediction model. We randomly picked 49 subjects to construct the model, whereas the remaining 11 subjects were utilized for validation. The results demonstrated that the model was a good regression compared to the observed values (Pearson's r=0.6, adjusted R square=0.4, p=9.4E-7, accuracy=49/60). Further analysis revealed that the average difference between the chronological age and predicted age was only 7.6±0.8years. It was concluded that this fast and non-invasive photon technology is sufficiently promising to be developed for the estimation of biological aging. Copyright © 2016 Elsevier B.V. All rights reserved.

Prediction modelling for trauma using comorbidity and 'true' 30-day outcome.

PubMed

Bouamra, Omar; Jacques, Richard; Edwards, Antoinette; Yates, David W; Lawrence, Thomas; Jenks, Tom; Woodford, Maralyn; Lecky, Fiona

2015-12-01

Prediction models for trauma outcome routinely control for age but there is uncertainty about the need to control for comorbidity and whether the two interact. This paper describes recent revisions to the Trauma Audit and Research Network (TARN) risk adjustment model designed to take account of age and comorbidities. In addition linkage between TARN and the Office of National Statistics (ONS) database allows patient's outcome to be accurately identified up to 30 days after injury. Outcome at discharge within 30 days was previously used. Prospectively collected data between 2010 and 2013 from the TARN database were analysed. The data for modelling consisted of 129 786 hospital trauma admissions. Three models were compared using the area under the receiver operating curve (AuROC) for assessing the ability of the models to predict outcome, the Akaike information criteria to measure the quality between models and test for goodness-of-fit and calibration. Model 1 is the current TARN model, Model 2 is Model 1 augmented by a modified Charlson comorbidity index and Model 3 is Model 2 with ONS data on 30 day outcome. The values of the AuROC curve for Model 1 were 0.896 (95% CI 0.893 to 0.899), for Model 2 were 0.904 (0.900 to 0.907) and for Model 3 0.897 (0.896 to 0.902). No significant interaction was found between age and comorbidity in Model 2 or in Model 3. The new model includes comorbidity and this has improved outcome prediction. There was no interaction between age and comorbidity, suggesting that both independently increase vulnerability to mortality after injury. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Outcome Trajectories in Extremely Preterm Infants

PubMed Central

Carlo, Waldemar A.; Tyson, Jon E.; Langer, John C.; Walsh, Michele C.; Parikh, Nehal A.; Das, Abhik; Van Meurs, Krisa P.; Shankaran, Seetha; Stoll, Barbara J.; Higgins, Rosemary D.

2012-01-01

OBJECTIVE: Methods are required to predict prognosis with changes in clinical course. Death or neurodevelopmental impairment in extremely premature neonates can be predicted at birth/admission to the ICU by considering gender, antenatal steroids, multiple birth, birth weight, and gestational age. Predictions may be improved by using additional information available later during the clinical course. Our objective was to develop serial predictions of outcome by using prognostic factors available over the course of NICU hospitalization. METHODS: Data on infants with birth weight ≤1.0 kg admitted to 18 large academic tertiary NICUs during 1998–2005 were used to develop multivariable regression models following stepwise variable selection. Models were developed by using all survivors at specific times during hospitalization (in delivery room [n = 8713], 7-day [n = 6996], 28-day [n = 6241], and 36-week postmenstrual age [n = 5118]) to predict death or death/neurodevelopmental impairment at 18 to 22 months. RESULTS: Prediction of death or neurodevelopmental impairment in extremely premature infants is improved by using information available later during the clinical course. The importance of birth weight declines, whereas the importance of respiratory illness severity increases with advancing postnatal age. The c-statistic in validation models ranged from 0.74 to 0.80 with misclassification rates ranging from 0.28 to 0.30. CONCLUSIONS: Dynamic models of the changing probability of individual outcome can improve outcome predictions in preterm infants. Various current and future scenarios can be modeled by input of different clinical possibilities to develop individual “outcome trajectories” and evaluate impact of possible morbidities on outcome. PMID:22689874

Dental age estimation in Japanese individuals combining permanent teeth and third molars.

PubMed

Ramanan, Namratha; Thevissen, Patrick; Fleuws, Steffen; Willems, G

2012-12-01

The study aim was, firstly, to verify the Willems et al. model on a Japanese reference sample. Secondly to develop a Japanese reference model based on the Willems et al. method and to verify it. Thirdly to analyze the age prediction performance adding tooth development information of third molars to permanent teeth. Retrospectively 1877 panoramic radiographs were selected in the age range between 1 and 23 years (1248 children, 629 sub-adults). Dental development was registered applying Demirjian 's stages of the mandibular left permanent teeth in children and Köhler stages on the third molars. The children's data were, firstly, used to validate the Willems et al. model (developed a Belgian reference sample), secondly, split ino a training and a test sample. On the training sample a Japanese reference model was developed based on the Willems method. The developed model and the Willems et al; model were verified on the test sample. Regression analysis was used to detect the age prediction performance adding third molar scores to permanent tooth scores. The validated Willems et al. model provided a mean absolute error of 0.85 and 0.75 years in females and males, respectively. The mean absolute error in the verified Willems et al. and the developed Japanese reference model was 0.85, 0.77 and 0.79, 0.75 years in females and males, respectively. On average a negligible change in root mean square error values was detected adding third molar scores to permanent teeth scores. The Belgian sample could be used as a reference model to estimate the age of the Japanese individuals. Combining information from the third molars and permanent teeth was not providing clinically significant improvement of age predictions based on permanent teeth information alone.

Profile and determinants of successful aging in the Ibadan Study of Ageing.

PubMed

Gureje, Oye; Oladeji, Bibilola D; Abiona, Taiwo; Chatterji, Somnath

2014-05-01

To determine the profile and determinants of successful aging in a developing country characterized by low life expectancy and where successful agers may represent a unique group. Community-based cohort study. Eight contiguous states in the Yoruba-speaking region of Nigeria. A multistage clustered sampling of households was used to select a representative sample of individuals (N = 2,149) aged 65 and older at baseline. Nine hundred thirty were successfully followed for an average of 64 months between August 2003 and December 2009. Lifestyle and behavioral factors were assessed at baseline. Successful aging, defined using each of three models (absence of chronic health conditions, functional independence, and satisfaction with life), was assessed at follow-up. Between 16% and 75% of respondents could be classified as successful agers using one of the three models while 7.5% could be so classified using a combination of all the models. Correlations between the three models were small, ranging from 0.08 to 0.15. Different features predicted their outcomes, suggesting that they represent relatively independent trajectories of aging. Whichever model was used, more men than women tended to be classified as aging successfully. Men who aged successfully, using a combination of all the three models, were more likely never to have smoked (adjusted odds ratio (aOR) = 4.7, 95% confidence interval (CI) = 1.55-14.46) and to report, at baseline, having contacts with friends (aOR = 4.2, 95% CI = 1.0-18.76) or participating in community activities (aOR = 16.0, 95% CI = 1.23-204.40). In women, there was a nonlinear trend for younger age at baseline to predict this outcome. Modifiable social and lifestyle factors predicted successful aging in this population, suggesting that health promotion targeting behavior change may lead to tangible benefits for health and well-being in old age. © 2014, Copyright the Authors Journal compilation © 2014, The American Geriatrics Society.

Light Water Reactor Sustainability Program: Survey of Models for Concrete Degradation

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

Spencer, Benjamin W.; Huang, Hai

Concrete is widely used in the construction of nuclear facilities because of its structural strength and its ability to shield radiation. The use of concrete in nuclear facilities for containment and shielding of radiation and radioactive materials has made its performance crucial for the safe operation of the facility. As such, when life extension is considered for nuclear power plants, it is critical to have predictive tools to address concerns related to aging processes of concrete structures and the capacity of structures subjected to age-related degradation. The goal of this report is to review and document the main aging mechanismsmore » of concern for concrete structures in nuclear power plants (NPPs) and the models used in simulations of concrete aging and structural response of degraded concrete structures. This is in preparation for future work to develop and apply models for aging processes and response of aged NPP concrete structures in the Grizzly code. To that end, this report also provides recommendations for developing more robust predictive models for aging effects of performance of concrete.« less

Statistical model selection for better prediction and discovering science mechanisms that affect reliability

DOE PAGES

Anderson-Cook, Christine M.; Morzinski, Jerome; Blecker, Kenneth D.

2015-08-19

Understanding the impact of production, environmental exposure and age characteristics on the reliability of a population is frequently based on underlying science and empirical assessment. When there is incomplete science to prescribe which inputs should be included in a model of reliability to predict future trends, statistical model/variable selection techniques can be leveraged on a stockpile or population of units to improve reliability predictions as well as suggest new mechanisms affecting reliability to explore. We describe a five-step process for exploring relationships between available summaries of age, usage and environmental exposure and reliability. The process involves first identifying potential candidatemore » inputs, then second organizing data for the analysis. Third, a variety of models with different combinations of the inputs are estimated, and fourth, flexible metrics are used to compare them. As a result, plots of the predicted relationships are examined to distill leading model contenders into a prioritized list for subject matter experts to understand and compare. The complexity of the model, quality of prediction and cost of future data collection are all factors to be considered by the subject matter experts when selecting a final model.« less

No added value of age at menopause and the lifetime cumulative number of menstrual cycles for cardiovascular risk prediction in postmenopausal women.

PubMed

Atsma, Femke; van der Schouw, Yvonne T; Grobbee, Diederick E; Hoes, Arno W; Bartelink, Marie-Louise E L

2008-11-12

The aim of the present study was to investigate the added value of age at menopause and the lifetime cumulative number of menstrual cycles in cardiovascular risk prediction in postmenopausal women. This study included 971 women. The ankle-arm index was used as a proxy for cardiovascular morbidity and mortality. The ankle-arm index was calculated for each leg by dividing the highest ankle systolic blood pressure by the highest brachial systolic blood pressure. A cut-off value of 0.95 was used to differentiate between low and high risk women. Three cardiovascular risk models were constructed. In the initial model all classical predictors for cardiovascular disease were investigated. This model was then extended by age at menopause or the lifetime cumulative number of menstrual cycles to test their added value for cardiovascular risk prediction. Differences in discriminative power between the models were investigated by comparing the area under the receiver operating characteristic (ROC) curves. The mean age was 66.0 (+/-5.6) years. The 6 independent predictors for cardiovascular disease were age, systolic blood pressure, total to HDL cholesterol ratio, current smoking, glucose level, and body mass index > or =30 kg/m(2). The ROC area was 0.69 (0.64-0.73) and did not change when age at menopause or the lifetime cumulative number of menstrual cycles was added. The findings in this study among postmenopausal women did not support the view that age at menopause or a refined estimation of lifetime endogenous estrogen exposure would improve cardiovascular risk prediction as approximated by the ankle-arm index.

Brain age and other bodily 'ages': implications for neuropsychiatry.

PubMed

Cole, James H; Marioni, Riccardo E; Harris, Sarah E; Deary, Ian J

2018-06-11

As our brains age, we tend to experience cognitive decline and are at greater risk of neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases are also exacerbated during ageing. However, the ageing process does not affect people uniformly; nor, in fact, does the ageing process appear to be uniform even within an individual. Here, we outline recent neuroimaging research into brain ageing and the use of other bodily ageing biomarkers, including telomere length, the epigenetic clock, and grip strength. Some of these techniques, using statistical approaches, have the ability to predict chronological age in healthy people. Moreover, they are now being applied to neurological and psychiatric disease groups to provide insights into how these diseases interact with the ageing process and to deliver individualised predictions about future brain and body health. We discuss the importance of integrating different types of biological measurements, from both the brain and the rest of the body, to build more comprehensive models of the biological ageing process. Finally, we propose seven steps for the field of brain-ageing research to take in coming years. This will help us reach the long-term goal of developing clinically applicable statistical models of biological processes to measure, track and predict brain and body health in ageing and disease.

Predictive models of long-term anatomic outcome in age-related macular degeneration treated with as-needed Ranibizumab.

PubMed

Gonzalez-Buendia, Lucia; Delgado-Tirado, Santiago; Sanabria, M Rosa; Fernandez, Itziar; Coco, Rosa M

2017-08-18

To analyze predictors and develop predictive models of anatomic outcome in neovascular age-related macular degeneration (AMD) treated with as-needed ranibizumab after 4 years of follow-up. A multicenter consecutive case series non-interventional study was performed. Clinical, funduscopic and OCT characteristics of 194 treatment-naïve patients with AMD treated with as-needed ranibizumab for at least 2 years and up to 4 years were analyzed at baseline, 3 months and each year until the end of the follow-up. Baseline demographic and angiographic characteristics were also evaluated. R Statistical Software was used for statistical analysis. Main outcome measure was final anatomic status. Factors associated with less probability of preserved macula were diagnosis in 2009, older age, worse vision, presence of atrophy/fibrosis, pigment epithelium detachment, and geographic atrophy/fibrotic scar/neovascular AMD in the fellow eye. Factors associated with higher probability of GA were presence of atrophy and greater number of injections, whereas male sex, worse vision, lesser change in central macular thickness and presence of fibrosis were associated with less probability of GA as final macular status. Predictive model of preserved macula vs. GA/fibrotic scar showed sensibility of 77.78% and specificity of 69.09%. Predictive model of GA vs. fibrotic scar showed sensibility of 68.89% and specificity of 72.22%. We identified predictors of final macular status, and developed two predictive models. Predictive models that we propose are based on easily harvested variables, and, if validated, could be a useful tool for individual patient management and clinical research studies.

Prediction model with metabolic syndrome to predict recurrent vascular events in patients with clinically manifest vascular diseases.

PubMed

Wassink, Annemarie M; van der Graaf, Yolanda; Janssen, Kristel J; Cook, Nancy R; Visseren, Frank L

2012-12-01

Although the overall average 10-year cardiovascular risk for patients with manifest atherosclerosis is considered to be more than 20%, actual risk for individual patients ranges from much lower to much higher. We investigated whether information on metabolic syndrome (MetS) or its individual components improves cardiovascular risk stratification in these patients. We conducted a prospective cohort study in 3679 patients with clinical manifest atherosclerosis from the Secondary Manifestations of ARTerial disease (SMART) study. Primary outcome was defined as any cardiovascular event (cardiovascular death, ischemic stroke or myocardial infarction). Three pre-specified prediction models were derived, all including information on established MetS components. The association between outcome and predictors was quantified using a Cox proportional hazard analysis. Model performance was assessed using global goodness-of-fit fit (χ(2)), discrimination (C-index) and ability to improve risk stratification. A total of 417 cardiovascular events occurred among 3679 patients with 15,102 person-years of follow-up (median follow-up 3.7 years, range 1.6-6.4 years). Compared to a model with age and gender only, all MetS-based models performed slightly better in terms of global model fit (χ(2)) but not C-index. The Net Reclassification Index associated with the addition of MetS (yes/no), the dichotomous MetS-components or the continuous MetS-components on top of age and gender was 2.1% (p = 0.29), 2.3% (p = 0.31) and 7.5% (p = 0.01), respectively. Prediction models incorporating age, gender and MetS can discriminate between patients with clinical manifest atherosclerosis at the highest vascular risk and those at lower risk. The addition of MetS components to a model with age and gender correctly reclassifies only a small proportion of patients into higher- and lower-risk categories. The clinical utility of a prediction model with MetS is therefore limited.

Action Prediction Allows Hypothesis Testing via Internal Forward Models at 6 Months of Age

PubMed Central

Gredebäck, Gustaf; Lindskog, Marcus; Juvrud, Joshua C.; Green, Dorota; Marciszko, Carin

2018-01-01

We propose that action prediction provides a cornerstone in a learning process known as internal forward models. According to this suggestion infants’ predictions (looking to the mouth of someone moving a spoon upward) will moments later be validated or proven false (spoon was in fact directed toward a bowl), information that is directly perceived as the distance between the predicted and actual goal. Using an individual difference approach we demonstrate that action prediction correlates with the tendency to react with surprise when social interactions are not acted out as expected (action evaluation). This association is demonstrated across tasks and in a large sample (n = 118) at 6 months of age. These results provide the first indication that infants might rely on internal forward models to structure their social world. Additional analysis, consistent with prior work and assumptions from embodied cognition, demonstrates that the latency of infants’ action predictions correlate with the infant’s own manual proficiency. PMID:29593600

Predicting Gang Fight Participation in a General Youth Sample via the HEW Youth Development Model's Community Program Impact Scales, Age, and Sex.

ERIC Educational Resources Information Center

Truckenmiller, James L.

The accurate prediction of violence has been in the spotlight of critical concern in recent years. To investigate the relative predictive power of peer pressure, youth perceived negative labeling, youth perceived access to educational and occupational roles, social alienation, self-esteem, sex, and age with regard to gang fight participation…

Thermo-mechanical simulations of early-age concrete cracking with durability predictions

NASA Astrophysics Data System (ADS)

Havlásek, Petr; Šmilauer, Vít; Hájková, Karolina; Baquerizo, Luis

2017-09-01

Concrete performance is strongly affected by mix design, thermal boundary conditions, its evolving mechanical properties, and internal/external restraints with consequences to possible cracking with impaired durability. Thermo-mechanical simulations are able to capture those relevant phenomena and boundary conditions for predicting temperature, strains, stresses or cracking in reinforced concrete structures. In this paper, we propose a weakly coupled thermo-mechanical model for early age concrete with an affinity-based hydration model for thermal part, taking into account concrete mix design, cement type and thermal boundary conditions. The mechanical part uses B3/B4 model for concrete creep and shrinkage with isotropic damage model for cracking, able to predict a crack width. All models have been implemented in an open-source OOFEM software package. Validations of thermo-mechanical simulations will be presented on several massive concrete structures, showing excellent temperature predictions. Likewise, strain validation demonstrates good predictions on a restrained reinforced concrete wall and concrete beam. Durability predictions stem from induction time of reinforcement corrosion, caused by carbonation and/or chloride ingress influenced by crack width. Reinforcement corrosion in concrete struts of a bridge will serve for validation.

A Predictive Model of Weight Loss After Roux-en-Y Gastric Bypass up to 5 Years After Surgery: a Useful Tool to Select and Manage Candidates to Bariatric Surgery.

PubMed

Seyssel, Kevin; Suter, Michel; Pattou, François; Caiazzo, Robert; Verkindt, Helene; Raverdy, Violeta; Jolivet, Mathieu; Disse, Emmanuel; Robert, Maud; Giusti, Vittorio

2018-06-19

Different factors, such as age, gender, preoperative weight but also the patient's motivation, are known to impact outcomes after Roux-en-Y gastric bypass (RYGBP). Weight loss prediction is helpful to define realistic expectations and maintain motivation during follow-up, but also to select good candidates for surgery and limit failures. Therefore, developing a realistic predictive tool appears interesting. A Swiss cohort (n = 444), who underwent RYGBP, was used, with multiple linear regression models, to predict weight loss up to 60 months after surgery considering age, height, gender and weight at baseline. We then applied our model on two French cohorts and compared predicted weight to the one finally reached. Accuracy of our model was controlled using root mean square error (RMSE). Mean weight loss was 43.6 ± 13.0 and 40.8 ± 15.4 kg at 12 and 60 months respectively. The model was reliable to predict weight loss (0.37 < R 2  < 0.48) and RMSE between 5.0 and 12.2 kg. High preoperative weight and young age were positively correlated to weight loss, as well as male gender. Correlations between predicted weight and real weight were highly significant in both validation cohorts (R ≥ 0.7 and P < 0.01) and RMSE increased throughout follow-up between 6.2 and 15.4 kg. Our statistical model to predict weight loss outcomes after RYGBP seems accurate. It could be a valuable tool to define realistic weight loss expectations and to improve patient selection and outcomes during follow-up. Further research is needed to demonstrate the interest of this model in improving patients' motivation and results and limit the failures.

VAT=TAAT-SAAT: innovative anthropometric model to predict visceral adipose tissue without resort to CT-Scan or DXA.

PubMed

Samouda, Hanen; Dutour, Anne; Chaumoitre, Kathia; Panuel, Michel; Dutour, Olivier; Dadoun, Frédéric

2013-01-01

To investigate whether a combination of a selected but limited number of anthropometric measurements predicts visceral adipose tissue (VAT) better than other anthropometric measurements, without resort to medical imaging. Abdominal anthropometric measurements are total abdominal adipose tissue indicators and global measures of VAT and SAAT (subcutaneous abdominal adipose tissue). Therefore, subtracting the anthropometric measurement the more correlated possible with SAAT while being the least correlated possible with VAT, from the most correlated abdominal anthropometric measurement with VAT while being highly correlated with TAAT, may better predict VAT. BMI participants' range was from 16.3 to 52.9 kg m(-2) . Anthropometric and abdominal adipose tissues data by computed tomography (CT-Scan) were available in 253 patients (18-78 years) (CHU Nord, Marseille) and used to develop the anthropometric VAT prediction models. Subtraction of proximal thigh circumference from waist circumference, adjusted to age and/or BMI, predicts better VAT (Women: VAT = 2.15 × Waist C - 3.63 × Proximal Thigh C + 1.46 × Age + 6.22 × BMI - 92.713; R(2) = 0.836. Men: VAT = 6 × Waist C - 4.41 × proximal thigh C + 1.19 × Age - 213.65; R(2) = 0.803) than the best single anthropometric measurement or the association of two anthropometric measurements highly correlated with VAT. Both multivariate models showed no collinearity problem. Selected models demonstrate high sensitivity (97.7% in women, 100% in men). Similar predictive abilities were observed in the validation sample (Women: R(2) = 76%; Men: R(2) = 70%). Bland and Altman method showed no systematic estimation error of VAT. Validated in a large range of age and BMI, our results suggest the usefulness of the anthropometric selected models to predict VAT in Europides (South of France). Copyright © 2013 The Obesity Society.

VAT=TAAT-SAAT: Innovative Anthropometric Model to Predict Visceral Adipose Tissue Without Resort to CT-Scan or DXA

PubMed Central

Samouda, Hanen; Dutour, Anne; Chaumoitre, Kathia; Panuel, Michel; Dutour, Olivier; Dadoun, Frédéric

2013-01-01

Objective To investigate whether a combination of a selected but limited number of anthropometric measurements predicts visceral adipose tissue (VAT) better than other anthropometric measurements, without resort to medical imaging. Hypothesis Abdominal anthropometric measurements are total abdominal adipose tissue indicators and global measures of VAT and SAAT (subcutaneous abdominal adipose tissue). Therefore, subtracting the anthropometric measurement the more correlated possible with SAAT while being the least correlated possible with VAT, from the most correlated abdominal anthropometric measurement with VAT while being highly correlated with TAAT, may better predict VAT. Design and Methods BMI participants' range was from 16.3 to 52.9 kg m−2. Anthropometric and abdominal adipose tissues data by computed tomography (CT-Scan) were available in 253 patients (18-78 years) (CHU Nord, Marseille) and used to develop the anthropometric VAT prediction models. Results Subtraction of proximal thigh circumference from waist circumference, adjusted to age and/or BMI, predicts better VAT (Women: VAT = 2.15 × Waist C − 3.63 × Proximal Thigh C + 1.46 × Age + 6.22 × BMI − 92.713; R2 = 0.836. Men: VAT = 6 × Waist C − 4.41 × proximal thigh C + 1.19 × Age − 213.65; R2 = 0.803) than the best single anthropometric measurement or the association of two anthropometric measurements highly correlated with VAT. Both multivariate models showed no collinearity problem. Selected models demonstrate high sensitivity (97.7% in women, 100% in men). Similar predictive abilities were observed in the validation sample (Women: R2 = 76%; Men: R2 = 70%). Bland and Altman method showed no systematic estimation error of VAT. Conclusion Validated in a large range of age and BMI, our results suggest the usefulness of the anthropometric selected models to predict VAT in Europides (South of France). PMID:23404678

Evaluation of methodology for detecting/predicting migration of forest species

Treesearch

Dale S. Solomon; William B. Leak

1996-01-01

Available methods for analyzing migration of forest species are evaluated, including simulation models, remeasured plots, resurveys, pollen/vegetation analysis, and age/distance trends. Simulation models have provided some of the most drastic estimates of species changes due to predicted changes in global climate. However, these models require additional testing...

The interplay of maternal sensitivity and toddler engagement of mother in predicting self-regulation.

PubMed

Ispa, Jean M; Su-Russell, Chang; Palermo, Francisco; Carlo, Gustavo

2017-03-01

Using data from the Early Head Start Research and Evaluation Project, a cross-lag mediation model was tested to examine longitudinal relations among low-income mothers' sensitivity; toddlers' engagement of their mothers; and toddler's self-regulation at ages 1, 2, and 3 years (N = 2,958). Age 1 maternal sensitivity predicted self-regulation at ages 2 and 3 years, and age 2 engagement of mother mediated the relation between age 1 maternal sensitivity and age 3 self-regulation. Lagged relations from toddler self-regulation at ages 1 and 2 years to later maternal sensitivity were not significant, suggesting stronger influence from mother to toddler than vice versa. Model fit was similar regardless of child gender and depth of family poverty. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Childhood social withdrawal, interpersonal impairment, and young adult depression: a mediational model.

PubMed

Katz, Shaina J; Conway, Christopher C; Hammen, Constance L; Brennan, Patricia A; Najman, Jake M

2011-11-01

Building on interpersonal theories of depression, the current study sought to explore whether early childhood social withdrawal serves as a risk factor for depressive symptoms and diagnoses in young adulthood. The researchers hypothesized that social impairment at age 15 would mediate the association between social withdrawal at age 5 and depression by age 20. This mediational model was tested in a community sample of 702 Australian youth followed from mother's pregnancy to youth age 20. Structural equation modeling analyses found support for a model in which childhood social withdrawal predicted adolescent social impairment, which, in turn, predicted depression in young adulthood. Additionally, gender was found to moderate the relationship between adolescent social impairment and depression in early adulthood, with females exhibiting a stronger association between social functioning and depression at the symptom and diagnostic level. This study illuminates one potential pathway from early developing social difficulties to later depressive symptoms and disorders.

Childhood Social Withdrawal, Interpersonal Impairment, and Young Adult Depression: A Mediational Model

PubMed Central

Katz, Shaina J.; Conway, Christopher C.; Hammen, Constance L.; Brennan, Patricia A.; Najman, Jake M.

2012-01-01

Building on interpersonal theories of depression, the current study sought to explore whether early childhood social withdrawal serves as a risk factor for depressive symptoms and diagnoses in young adulthood. The researchers hypothesized that social impairment at age 15 would mediate the association between social withdrawal at age 5 and depression by age 20. This mediational model was tested in a community sample of 702 Australian youth followed from mother’s pregnancy to youth age 20. Structural equation modeling analyses found support for a model in which childhood social withdrawal predicted adolescent social impairment, which, in turn, predicted depression in young adulthood. Additionally, gender was found to moderate the relationship between adolescent social impairment and depression in early adulthood, with females exhibiting a stronger association between social functioning and depression at the symptom and diagnostic level. This study illuminates one potential pathway from early developing social difficulties to later depressive symptoms and disorders. PMID:21744059

Modified Maturity Offset Prediction Equations: Validation in Independent Longitudinal Samples of Boys and Girls.

PubMed

Kozieł, Sławomir M; Malina, Robert M

2018-01-01

Predicted maturity offset and age at peak height velocity are increasingly used with youth athletes, although validation studies of the equations indicated major limitations. The equations have since been modified and simplified. The objective of this study was to validate the new maturity offset prediction equations in independent longitudinal samples of boys and girls. Two new equations for boys with chronological age and sitting height and chronological age and stature as predictors, and one equation for girls with chronological age and stature as predictors were evaluated in serial data from the Wrocław Growth Study, 193 boys (aged 8-18 years) and 198 girls (aged 8-16 years). Observed age at peak height velocity for each youth was estimated with the Preece-Baines Model 1. The original prediction equations were included for comparison. Predicted age at peak height velocity was the difference between chronological age at prediction and maturity offset. Predicted ages at peak height velocity with the new equations approximated observed ages at peak height velocity in average maturing boys near the time of peak height velocity; a corresponding window for average maturing girls was not apparent. Compared with observed age at peak height velocity, predicted ages at peak height velocity with the new and original equations were consistently later in early maturing youth and earlier in late maturing youth of both sexes. Predicted ages at peak height velocity with the new equations had reduced variation compared with the original equations and especially observed ages at peak height velocity. Intra-individual variation in predicted ages at peak height velocity with all equations was considerable. The new equations are useful for average maturing boys close to the time of peak height velocity; there does not appear to be a clear window for average maturing girls. The new and original equations have major limitations with early and late maturing boys and girls.

Histologic and biochemical alterations predict pulmonary mechanical dysfunction in aging mice with chronic lung inflammation

PubMed Central

Laskin, Debra L.; Gow, Andrew J.

2017-01-01

Both aging and chronic inflammation produce complex structural and biochemical alterations to the lung known to impact work of breathing. Mice deficient in surfactant protein D (Sftpd) develop progressive age-related lung pathology characterized by tissue destruction/remodeling, accumulation of foamy macrophages and alteration in surfactant composition. This study proposes to relate changes in tissue structure seen in normal aging and in chronic inflammation to altered lung mechanics using a computational model. Alterations in lung function in aging and Sftpd -/- mice have been inferred from fitting simple mechanical models to respiratory impedance data (Zrs), however interpretation has been confounded by the simultaneous presence of multiple coexisting pathophysiologic processes. In contrast to the inverse modeling approach, this study uses simulation from experimental measurements to recapitulate how aging and inflammation alter Zrs. Histologic and mechanical measurements were made in C57BL6/J mice and congenic Sftpd-/- mice at 8, 27 and 80 weeks of age (n = 8/group). An anatomic computational model based on published airway morphometry was developed and Zrs was simulated between 0.5 and 20 Hz. End expiratory pressure dependent changes in airway caliber and recruitment were estimated from mechanical measurements. Tissue elements were simulated using the constant phase model of viscoelasticity. Baseline elastance distribution was estimated in 8-week-old wild type mice, and stochastically varied for each condition based on experimentally measured alteration in elastic fiber composition, alveolar geometry and surfactant composition. Weighing reduction in model error against increasing model complexity allowed for identification of essential features underlying mechanical pathology and their contribution to Zrs. Using a maximum likelihood approach, alteration in lung recruitment and diminished elastic fiber density were shown predictive of mechanical alteration at airway opening, to a greater extent than overt acinar wall destruction. Model-predicted deficits in PEEP-dependent lung recruitment correlate with altered lung lining fluid composition independent of age or genotype. PMID:28837561

Histologic and biochemical alterations predict pulmonary mechanical dysfunction in aging mice with chronic lung inflammation.

PubMed

Massa, Christopher B; Groves, Angela M; Jaggernauth, Smita U; Laskin, Debra L; Gow, Andrew J

2017-08-01

Both aging and chronic inflammation produce complex structural and biochemical alterations to the lung known to impact work of breathing. Mice deficient in surfactant protein D (Sftpd) develop progressive age-related lung pathology characterized by tissue destruction/remodeling, accumulation of foamy macrophages and alteration in surfactant composition. This study proposes to relate changes in tissue structure seen in normal aging and in chronic inflammation to altered lung mechanics using a computational model. Alterations in lung function in aging and Sftpd -/- mice have been inferred from fitting simple mechanical models to respiratory impedance data (Zrs), however interpretation has been confounded by the simultaneous presence of multiple coexisting pathophysiologic processes. In contrast to the inverse modeling approach, this study uses simulation from experimental measurements to recapitulate how aging and inflammation alter Zrs. Histologic and mechanical measurements were made in C57BL6/J mice and congenic Sftpd-/- mice at 8, 27 and 80 weeks of age (n = 8/group). An anatomic computational model based on published airway morphometry was developed and Zrs was simulated between 0.5 and 20 Hz. End expiratory pressure dependent changes in airway caliber and recruitment were estimated from mechanical measurements. Tissue elements were simulated using the constant phase model of viscoelasticity. Baseline elastance distribution was estimated in 8-week-old wild type mice, and stochastically varied for each condition based on experimentally measured alteration in elastic fiber composition, alveolar geometry and surfactant composition. Weighing reduction in model error against increasing model complexity allowed for identification of essential features underlying mechanical pathology and their contribution to Zrs. Using a maximum likelihood approach, alteration in lung recruitment and diminished elastic fiber density were shown predictive of mechanical alteration at airway opening, to a greater extent than overt acinar wall destruction. Model-predicted deficits in PEEP-dependent lung recruitment correlate with altered lung lining fluid composition independent of age or genotype.

Prediction of adolescent and adult adiposity outcomes from early life anthropometrics.

PubMed

Graversen, Lise; Sørensen, Thorkild I A; Gerds, Thomas A; Petersen, Liselotte; Sovio, Ulla; Kaakinen, Marika; Sandbaek, Annelli; Laitinen, Jaana; Taanila, Anja; Pouta, Anneli; Järvelin, Marjo-Riitta; Obel, Carsten

2015-01-01

Maternal body mass index (BMI), birth weight, and preschool BMI may help identify children at high risk of overweight as they are (1) similarly linked to adolescent overweight at different stages of the obesity epidemic, (2) linked to adult obesity and metabolic alterations, and (3) easily obtainable in health examinations in young children. The aim was to develop early childhood prediction models of adolescent overweight, adult overweight, and adult obesity. Prediction models at various ages in the Northern Finland Birth Cohort born in 1966 (NFBC1966) were developed. Internal validation was tested using a bootstrap design, and external validation was tested for the model predicting adolescent overweight using the Northern Finland Birth Cohort born in 1986 (NFBC1986). A prediction model developed in the NFBC1966 to predict adolescent overweight, applied to the NFBC1986, and aimed at labelling 10% as "at risk" on the basis of anthropometric information collected until 5 years of age showed that half of those at risk in fact did become overweight. This group constituted one-third of all who became overweight. Our prediction model identified a subgroup of children at very high risk of becoming overweight, which may be valuable in public health settings dealing with obesity prevention. © 2014 The Obesity Society.

Psychosocial work environment factors and weight change: a prospective study among Danish health care workers.

PubMed

Gram Quist, Helle; Christensen, Ulla; Christensen, Karl Bang; Aust, Birgit; Borg, Vilhelm; Bjorner, Jakob B

2013-01-17

Lifestyle variables may serve as important intermediate factors between psychosocial work environment and health outcomes. Previous studies, focussing on work stress models have shown mixed and weak results in relation to weight change. This study aims to investigate psychosocial factors outside the classical work stress models as potential predictors of change in body mass index (BMI) in a population of health care workers. A cohort study, with three years follow-up, was conducted among Danish health care workers (3982 women and 152 men). Logistic regression analyses examined change in BMI (more than +/- 2 kg/m(2)) as predicted by baseline psychosocial work factors (work pace, workload, quality of leadership, influence at work, meaning of work, predictability, commitment, role clarity, and role conflicts) and five covariates (age, cohabitation, physical work demands, type of work position and seniority). Among women, high role conflicts predicted weight gain, while high role clarity predicted both weight gain and weight loss. Living alone also predicted weight gain among women, while older age decreased the odds of weight gain. High leadership quality predicted weight loss among men. Associations were generally weak, with the exception of quality of leadership, age, and cohabitation. This study of a single occupational group suggested a few new risk factors for weight change outside the traditional work stress models.

Outcomes of Occupational Self-Efficacy in Older Workers

PubMed Central

Paggi, Michelle E.; Jopp, Daniela S.

2016-01-01

Because of the increasing number of older workers, it is important to develop models of work-related constructs for this population. The present article developed a model surrounding occupational self-efficacy, testing its relation to other factors (e.g., intrinsic job motivation), predictors (e.g., self-perceptions of aging), and outcomes (e.g., job satisfaction). Employed adults of ages 50 and older (n= 313) were recruited via organizations and social media sites. Study participants (M= 59.7, SD= 6.1, range = 50–78) volunteered to fill out an Internet survey. Occupational self-efficacy predicted job satisfaction, and intrinsic job motivation fully mediated this relationship. More negative self-perceptions of aging predicted poorer occupational self-efficacy. Occupational self-efficacy also predicted life satisfaction. Expected retirement age and job performance were unrelated to occupational self-efficacy. These findings may inform workplace interventions that seek to maintain or increase older worker job and life satisfaction. PMID:26394821

A multivariate model for predicting segmental body composition.

PubMed

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

2013-12-01

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

Development of Predictive Models of Injury for the Lower Extremity, Lumbar, and Thoracic Spine after Discharge from Physical Rehabilitation

DTIC Science & Technology

2016-10-01

prediction models will vary by age and sex . Hypothesis 3: A multi-factorial prediction model that accurately predicts risk of new and recurring injuries...members for injury risk after they have been cleared to return to duty from an injury is of great importance. The purpose of this project is to determine ...It turns out that many patients are not formally discharged from rehabilitation. Many of them “ self -discharge” and just stop coming back, either

Accelerated optical polymer aging studies for LED luminaire applications

NASA Astrophysics Data System (ADS)

Estupiñán, Edgar; Wendling, Peter; Kostrun, Marijan; Garner, Richard

2013-09-01

There is a need in the lighting industry to design and implement accelerated aging methods that accurately simulate the aging process of LED luminaire components. In response to this need, we have built a flexible and reliable system to study the aging characteristics of optical polymer materials, and we have employed it to study a commercially available LED luminaire diffuser made of PMMA. The experimental system consists of a "Blue LED Emitter" and a working surface. Both the temperatures of the samples and the optical powers of the LEDs are appropriately characterized in the system. Several accelerated aging experiments are carried out at different temperatures and optical powers over a 90 hour period and the measured transmission values are used as inputs to a degradation model derived using plausibility arguments. This model seems capable of predicting the behavior of the material as a function of time, temperature and optical power. The model satisfactorily predicts the measured transmission values of diffusers aged in luminaires at two different times and thus can be used to make application recommendations for this material. Specifically, at 35000 hours (the manufacturer's stated life of the luminaire) and at the typical operational temperature of the diffuser, the model predicts a transmission loss of only a few percent over the original transmission of the material at 450 nm, which renders this material suitable for this application.

Predicting life satisfaction of the Angolan elderly: a structural model.

PubMed

Gutiérrez, M; Tomás, J M; Galiana, L; Sancho, P; Cebrià, M A

2013-01-01

Satisfaction with life is of particular interest in the study of old age well-being because it has arisen as an important component of old age. A considerable amount of research has been done to explain life satisfaction in the elderly, and there is growing empirical evidence on best predictors of life satisfaction. This research evaluates the predictive power of some aging process variables, on Angolan elderly people's life satisfaction, while including perceived health into the model. Data for this research come from a cross-sectional survey of elderly people living in the capital of Angola, Luanda. A total of 1003 Angolan elderly were surveyed on socio-demographic information, perceived health, active engagement, generativity, and life satisfaction. A Multiple Indicators Multiple Causes model was built to test variables' predictive power on life satisfaction. The estimated theoretical model fitted the data well. The main predictors were those related to active engagement with others. Perceived health also had a significant and positive effect on life satisfaction. Several processes together may predict life satisfaction in the elderly population of Angola, and the variance accounted for it is large enough to be considered relevant. The key factor associated to life satisfaction seems to be active engagement with others.

Using a GIS model to assess terrestrial salamander response to alternative forest management plans

Treesearch

Eric J. Gustafson; Nathan L. Murphy; Thomas R. Crow

2001-01-01

A GIS model predicting the spatial distribution of terrestrial salamander abundance based on topography and forest age was developed using parameters derived from the literature. The model was tested by sampling salamander abundance across the full range of site conditions used in the model. A regression of the predictions of our GIS model against these sample data...

The transcriptional landscape of age in human peripheral blood

PubMed Central

Peters, Marjolein J.; Joehanes, Roby; Pilling, Luke C.; Schurmann, Claudia; Conneely, Karen N.; Powell, Joseph; Reinmaa, Eva; Sutphin, George L.; Zhernakova, Alexandra; Schramm, Katharina; Wilson, Yana A.; Kobes, Sayuko; Tukiainen, Taru; Nalls, Michael A.; Hernandez, Dena G.; Cookson, Mark R.; Gibbs, Raphael J.; Hardy, John; Ramasamy, Adaikalavan; Zonderman, Alan B.; Dillman, Allissa; Traynor, Bryan; Smith, Colin; Longo, Dan L.; Trabzuni, Daniah; Troncoso, Juan; van der Brug, Marcel; Weale, Michael E.; O'Brien, Richard; Johnson, Robert; Walker, Robert; Zielke, Ronald H.; Arepalli, Sampath; Ryten, Mina; Singleton, Andrew B.; Ramos, Yolande F.; Göring, Harald H. H.; Fornage, Myriam; Liu, Yongmei; Gharib, Sina A.; Stranger, Barbara E.; De Jager, Philip L.; Aviv, Abraham; Levy, Daniel; Murabito, Joanne M.; Munson, Peter J.; Huan, Tianxiao; Hofman, Albert; Uitterlinden, André G.; Rivadeneira, Fernando; van Rooij, Jeroen; Stolk, Lisette; Broer, Linda; Verbiest, Michael M. P. J.; Jhamai, Mila; Arp, Pascal; Metspalu, Andres; Tserel, Liina; Milani, Lili; Samani, Nilesh J.; Peterson, Pärt; Kasela, Silva; Codd, Veryan; Peters, Annette; Ward-Caviness, Cavin K.; Herder, Christian; Waldenberger, Melanie; Roden, Michael; Singmann, Paula; Zeilinger, Sonja; Illig, Thomas; Homuth, Georg; Grabe, Hans-Jörgen; Völzke, Henry; Steil, Leif; Kocher, Thomas; Murray, Anna; Melzer, David; Yaghootkar, Hanieh; Bandinelli, Stefania; Moses, Eric K.; Kent, Jack W.; Curran, Joanne E.; Johnson, Matthew P.; Williams-Blangero, Sarah; Westra, Harm-Jan; McRae, Allan F.; Smith, Jennifer A.; Kardia, Sharon L. R.; Hovatta, Iiris; Perola, Markus; Ripatti, Samuli; Salomaa, Veikko; Henders, Anjali K.; Martin, Nicholas G.; Smith, Alicia K.; Mehta, Divya; Binder, Elisabeth B.; Nylocks, K Maria; Kennedy, Elizabeth M.; Klengel, Torsten; Ding, Jingzhong; Suchy-Dicey, Astrid M.; Enquobahrie, Daniel A.; Brody, Jennifer; Rotter, Jerome I.; Chen, Yii-Der I.; Houwing-Duistermaat, Jeanine; Kloppenburg, Margreet; Slagboom, P. Eline; Helmer, Quinta; den Hollander, Wouter; Bean, Shannon; Raj, Towfique; Bakhshi, Noman; Wang, Qiao Ping; Oyston, Lisa J.; Psaty, Bruce M.; Tracy, Russell P.; Montgomery, Grant W.; Turner, Stephen T.; Blangero, John; Meulenbelt, Ingrid; Ressler, Kerry J.; Yang, Jian; Franke, Lude; Kettunen, Johannes; Visscher, Peter M.; Neely, G. Gregory; Korstanje, Ron; Hanson, Robert L.; Prokisch, Holger; Ferrucci, Luigi; Esko, Tonu; Teumer, Alexander; van Meurs, Joyce B. J.; Johnson, Andrew D.

2015-01-01

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts. PMID:26490707

Sexting as the mirror on the wall: Body-esteem attribution, media models, and objectified-body consciousness.

PubMed

Bianchi, Dora; Morelli, Mara; Baiocco, Roberto; Chirumbolo, Antonio

2017-12-01

Sexting motivations during adolescence are related to developmental dimensions-such as sexual identity and body-image development-or harmful intentions-such as aggression among peers and partners. Sociocultural and media models can affect explorations of sexuality and redefinitions of body image, which in turn are related to sexting behaviors and motivations. In this study, we investigated the roles of body-esteem attribution, the internalization of media models, and body objectification as predictors of three sexting motivations: sexual purposes, body-image reinforcement, and instrumental/aggravated reasons. The participants were 190 Italian adolescents aged from 13 to 20 years old (M age  = 17.4, SD age  = 1.8; 44.7% females). Sexual purposes were predicted by body-esteem attribution and body objectification; body-image reinforcement was predicted by the internalization of media models, and instrumental/aggravated reasons were not predicted by any variable. Thus, only sexual purposes and body-image reinforcement appeared to be affected by body-image concerns due to media models. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Neural network models - a novel tool for predicting the efficacy of growth hormone (GH) therapy in children with short stature.

PubMed

Smyczynska, Joanna; Hilczer, Maciej; Smyczynska, Urszula; Stawerska, Renata; Tadeusiewicz, Ryszard; Lewinski, Andrzej

2015-01-01

The leading method for prediction of growth hormone (GH) therapy effectiveness are multiple linear regression (MLR) models. Best of our knowledge, we are the first to apply artificial neural networks (ANN) to solve this problem. For ANN there is no necessity to assume the functions linking independent and dependent variables. The aim of study is to compare ANN and MLR models of GH therapy effectiveness. Analysis comprised the data of 245 GH-deficient children (170 boys) treated with GH up to final height (FH). Independent variables included: patients' height, pre-treatment height velocity, chronological age, bone age, gender, pubertal status, parental heights, GH peak in 2 stimulation tests, IGF-I concentration. The output variable was FH. For testing dataset, MLR model predicted FH SDS with average error (RMSE) 0.64 SD, explaining 34.3% of its variability; ANN model derived on the same pre-processed data predicted FH SDS with RMSE 0.60 SD, explaining 42.0% of its variability; ANN model derived on raw data predicted FH with RMSE 3.9 cm (0.63 SD), explaining 78.7% of its variability. ANN seem to be valuable tool in prediction of GH treatment effectiveness, especially since they can be applied to raw clinical data.

Predictive factors of alcohol and tobacco use in adolescents.

PubMed

Alvarez-Aguirre, Alicia; Alonso-Castillo, María Magdalena; Zanetti, Ana Carolina Guidorizzi

2014-01-01

to analyze the effect of self-esteem, assertiveness, self-efficacy and resiliency on alcohol and tobacco consumption in adolescents. a descriptive and correlational study was undertaken with 575 adolescents in 2010. The Self-Esteem Scale, the Situational Confidence Scale, the Assertiveness Questionnaire and the Resiliency Scale were used. the adjustment of the logistic regression model, considering age, sex, self-esteem, assertiveness, self-efficacy and resiliency, demonstrates significance in the consumption of alcohol and tobacco. Age, resiliency and assertiveness predict alcohol consumption in the lifetime and assertiveness predicts alcohol consumption in the last year. Similarly, age and sex predict tobacco consumption in the lifetime and age in the last year. this study can offer important information to plan nursing interventions involving adolescent alcohol and tobacco users.

Longitudinal Associations Between Parental Monitoring Discrepancy and Delinquency: An Application of the Latent Congruency Model.

PubMed

Ksinan, Albert J; Vazsonyi, Alexander T

2016-12-01

Studies have shown that discrepancies (relative concordance or discordance) between parent and adolescent ratings are predictive of problem behaviors; monitoring, in particular, has been consistently linked to them. The current study tested whether discrepancies in perceptions of maternal monitoring, rated by mothers and youth at age 12, foretold delinquency (rule breaking) at age 15, and whether parental closeness and conflict predicted higher discrepancies, and indirectly, higher delinquency. The final study sample used the NICHD longitudinal dataset with N = 966 youth (50.1 % female) and their mothers (80.1 % European American, 12.9 % African American, 7 % other ethnicity). The analytic approach consisted of an extension and application of the Latent Congruency Model (LCM) to estimate monitoring discrepancies as well as age 15 delinquency scores. Findings showed that age 12 monitoring discrepancy was predictive of age 15 delinquency for both boys and girls based on youth reports, but not for maternal reports. Age 11 closeness predicted age 12 monitoring discrepancy, which served as a mediator for its effect on age 15 adolescent-reported delinquency. Thus, based on the rigorous LCM analytic approach which seeks to minimize the effects by competing explanations and to maximize precision in providing robust estimates, rates of perceived discordance in parenting behaviors during early adolescence matter in understanding variability in adolescent delinquency during middle adolescence.

A model to predict progression in brain-injured patients.

PubMed

Tommasino, N; Forteza, D; Godino, M; Mizraji, R; Alvarez, I

2014-11-01

The study of brain death (BD) epidemiology and the acute brain injury (ABI) progression profile is important to improve public health programs, organ procurement strategies, and intensive care unit (ICU) protocols. The purpose of this study was to analyze the ABI progression profile among patients admitted to ICUs with a Glasgow Coma Score (GCS) ≤8, as well as establishing a prediction model of probability of death and BD. This was a retrospective analysis of prospective data that included all brain-injured patients with GCS ≤8 admitted to a total of four public and private ICUs in Uruguay (N = 1447). The independent predictor factors of death and BD were studied using logistic regression analysis. A hierarchical model consisting of 2 nested logit regression models was then created. With these models, the probabilities of death, BD, and death by cardiorespiratory arrest were analyzed. In the first regression, we observed that as the GCS decreased and age increased, the probability of death rose. Each additional year of age increased the probability of death by 0.014. In the second model, however, BD risk decreased with each year of age. The presence of swelling, mass effect, and/or space-occupying lesion increased BD risk for the same given GCS. In the presence of injuries compatible with intracranial hypertension, age behaved as a protective factor that reduced the probability of BD. Based on the analysis of the local epidemiology, a model to predict the probability of death and BD can be developed. The organ potential donation of a country, region, or hospital can be predicted on the basis of this model, customizing it to each specific situation.

The evolution of life-history variation in fishes, with particular reference to flatfishes

NASA Astrophysics Data System (ADS)

Roff, Derek A.

This paper explores four aspects of the evolution of life-history variation in fish, with particular reference to the flatfishes: 1. genetic variation and evolutionary response; 2. the size and age at first reproduction; 3. adult lifespan and variation in recruitment; 4. the relationship between reproductive effort and age. Evolutionary response may be limited by previous evolutionary pathways (phylogenetic variation) or by lack of genetic variation due to selection for a single trait. Estimates of heritability suggest, as predicted, that selection is stronger on life-history traits than morphological traits; but there is still adequate genetic variation to permit fairly rapid evolutionary changes. Several approaches to the analysis of the optimal age and size at first reproduction are discussed in the light of a general life-history model based on the assumption that natural selection maximizes r or R 0. It is concluded that one of the most important areas of future research is the relationship between reproduction and mortality. Murphy's hypothesis that the reproductive lifespan should increase with variation in spawning success is shown to be incorrect for fish, at least at the level of interspecific comparison. The model of Charlesworth & León predicting the sufficient condition for reproductive effort to increase with age is tested: in 28 of 31 cases the model predicts an increase of reproductive effort with age. These results suggest that, in general, reproductive effort should increase with age in fish. This prediction is confirmed in the 15 species for which adequate data exist.

Retrospective lifetime dietary patterns predict cognitive performance in community-dwelling older Australians.

PubMed

Hosking, Diane E; Nettelbeck, Ted; Wilson, Carlene; Danthiir, Vanessa

2014-07-28

Dietary intake is a modifiable exposure that may have an impact on cognitive outcomes in older age. The long-term aetiology of cognitive decline and dementia, however, suggests that the relevance of dietary intake extends across the lifetime. In the present study, we tested whether retrospective dietary patterns from the life periods of childhood, early adulthood, adulthood and middle age predicted cognitive performance in a cognitively healthy sample of 352 older Australian adults >65 years. Participants completed the Lifetime Diet Questionnaire and a battery of cognitive tests designed to comprehensively assess multiple cognitive domains. In separate regression models, lifetime dietary patterns were the predictors of cognitive factor scores representing ten constructs derived by confirmatory factor analysis of the cognitive test battery. All regression models were progressively adjusted for the potential confounders of current diet, age, sex, years of education, English as native language, smoking history, income level, apoE ɛ4 status, physical activity, other past dietary patterns and health-related variables. In the adjusted models, lifetime dietary patterns predicted cognitive performance in this sample of older adults. In models additionally adjusted for intake from the other life periods and mechanistic health-related variables, dietary patterns from the childhood period alone reached significance. Higher consumption of the 'coffee and high-sugar, high-fat extras' pattern predicted poorer performance on simple/choice reaction time, working memory, retrieval fluency, short-term memory and reasoning. The 'vegetable and non-processed' pattern negatively predicted simple/choice reaction time, and the 'traditional Australian' pattern positively predicted perceptual speed and retrieval fluency. Identifying early-life dietary antecedents of older-age cognitive performance contributes to formulating strategies for delaying or preventing cognitive decline.

The mortality risk score and the ADG score: two points-based scoring systems for the Johns Hopkins aggregated diagnosis groups to predict mortality in a general adult population cohort in Ontario, Canada.

PubMed

Austin, Peter C; Walraven, Carl van

2011-10-01

Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults. To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults. The ADG Score derives weights for the individual ADG diagnosis groups. : Retrospective cohort constructed using population-based administrative data. All 10,498,413 residents of Ontario, Canada, between the age of 20 and 100 years who were alive on their birthday in 2007, participated in this study. Participants were randomly divided into derivation and validation samples. : Death within 1 year. In the derivation cohort, the MRS ranged from -21 to 139 (median value 29, IQR 17 to 44). In the validation group, a logistic regression model with the MRS as the sole predictor significantly predicted the risk of 1-year mortality with a c-statistic of 0.917. A regression model with age, sex, and the ADG Score has similar performance. Both methods accurately predicted the risk of 1-year mortality across the 20 vigintiles of risk. The MRS combined values for a person's age, sex, and the John Hopkins ADGs to accurately predict 1-year mortality in adults. The ADG Score is a weighted score representing the presence or absence of the 32 ADG diagnosis groups. These scores will facilitate health services researchers conducting risk adjustment using administrative health care databases.

Using Latent Class Analysis to Model Temperament Types.

PubMed

Loken, Eric

2004-10-01

Mixture models are appropriate for data that arise from a set of qualitatively different subpopulations. In this study, latent class analysis was applied to observational data from a laboratory assessment of infant temperament at four months of age. The EM algorithm was used to fit the models, and the Bayesian method of posterior predictive checks was used for model selection. Results show at least three types of infant temperament, with patterns consistent with those identified by previous researchers who classified the infants using a theoretically based system. Multiple imputation of group memberships is proposed as an alternative to assigning subjects to the latent class with maximum posterior probability in order to reflect variance due to uncertainty in the parameter estimation. Latent class membership at four months of age predicted longitudinal outcomes at four years of age. The example illustrates issues relevant to all mixture models, including estimation, multi-modality, model selection, and comparisons based on the latent group indicators.

Isolation and identification of age-related DNA methylation markers for forensic age-prediction.

PubMed

Yi, Shao Hua; Xu, Long Chang; Mei, Kun; Yang, Rong Zhi; Huang, Dai Xin

2014-07-01

Age-prediction is an important part of forensic science. There is no available method of individual age-prediction for general forensic biological samples at crime scenes. Accumulating evidence indicates that aging resembles a developmentally regulated process tightly controlled by specific age-associated methylation exists in human genome. This study isolated and identified eight gene fragments in which the degree of cytosine methylation is significantly correlated with age in blood of 40 donors. Furthermore, we validated two CpG sites of each gene fragment and replicated our results in a general population sample of 40 males and 25 females with a wide age-range (11-72 years). The methylation of these fragments is linear with age over a range of six decades (Fragment P1 (r=-0.64), P2 (r=-0.58), P3 (r=-0.79), R1 (r=0.82), R2 (r=0.63), R3 (r=0.59), R4 (r=0.63) and R5 (r=0.62)). Using average methylation of two CpG sites from each fragment, we built a regression model that explained 95% of the variance in age and is able to predict the age of an individual with great accuracy (R(2)=0.918). The predicted values are highly correlated with the observed age in the sample (r=0.91). This study implicates that DNA methylation will be an available biological marker of age-prediction. Furthermore, measurement of relevant sites in the genome could be a tool in routine forensic screening to predict age of biological samples. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Estimation of Gestational Age, Using Neonatal Anthropometry: A Cross-sectional Study in India

PubMed Central

Thawani, Rajat; Faridi, M.M.A.; Arora, Shilpa Khanna; Kumar, Rajeev

2013-01-01

Prematurity is a significant contributor to neonatal mortality in India. Conventionally, assessment of gestational age of newborns is based on New Ballard Technique, for which a paediatric specialist is needed. Anthropometry of the newborn, especially birthweight, has been used in the past to predict the gestational age of the neonate in peripheral health facilities where a trained paediatrician is often not available. We aimed to determine if neonatal anthropometric parameters, viz. birthweight, crown heel-length, head-circumference, mid-upper arm-circumference, lower segment-length, foot-length, umbilical nipple distance, calf-circumference, intermammary distance, and hand-length, can reliably predict the gestational age. The study also aimed to derive an equation for the same. We also assessed if these neonatal anthropometric parameters had a better prediction of gestational age when used in combination compared to individual parameters. We evaluated 1,000 newborns in a cross-sectional study conducted in Guru Teg Bahadur Hospital in Delhi. Detailed anthropometric estimation of the neonates was done within 48 hours after birth, using standard techniques. Gestational age was estimated using New Ballard Scoring. Out of 1,250 consecutive neonates, 1,000 were included in the study. Of them, 800 randomly-selected newborns were used in devising the model, and the remaining 200 newborns were used in validating the final model. Quadratic regression analysis using stepwise selection was used in building the predictive model. Birthweight (R=0.72), head-circumference (R=0.60), and mid-upper arm-circumference (R=0.67) were found highly correlated with gestation. The final equation to assess gestational age was as follows: Gestational age (weeks)=5.437×W–0.781×W2+2.815×HC–0.041×HC2+0.285×MUAC–22.745 where W=Weight, HC=Head-circumference and MUAC=Mid-upper arm-circumference; Adjusted R=0.76. On validation, the predictability of this equation is 46% (±1 week), 75.5% (+2 weeks), and 91.5% (+3 weeks). This mathematical model may be used in identifying preterm neonates. PMID:24592594

Predicting Time to Hospital Discharge for Extremely Preterm Infants

PubMed Central

Hintz, Susan R.; Bann, Carla M.; Ambalavanan, Namasivayam; Cotten, C. Michael; Das, Abhik; Higgins, Rosemary D.

2010-01-01

As extremely preterm infant mortality rates have decreased, concerns regarding resource utilization have intensified. Accurate models to predict time to hospital discharge could aid in resource planning, family counseling, and perhaps stimulate quality improvement initiatives. Objectives For infants <27 weeks estimated gestational age (EGA), to develop, validate and compare several models to predict time to hospital discharge based on time-dependent covariates, and based on the presence of 5 key risk factors as predictors. Patients and Methods This was a retrospective analysis of infants <27 weeks EGA, born 7/2002-12/2005 and surviving to discharge from a NICHD Neonatal Research Network site. Time to discharge was modeled as continuous (postmenstrual age at discharge, PMAD), and categorical variables (“Early” and “Late” discharge). Three linear and logistic regression models with time-dependent covariate inclusion were developed (perinatal factors only, perinatal+early neonatal factors, perinatal+early+later factors). Models for Early and Late discharge using the cumulative presence of 5 key risk factors as predictors were also evaluated. Predictive capabilities were compared using coefficient of determination (R2) for linear models, and AUC of ROC curve for logistic models. Results Data from 2254 infants were included. Prediction of PMAD was poor, with only 38% of variation explained by linear models. However, models incorporating later clinical characteristics were more accurate in predicting “Early” or “Late” discharge (full models: AUC 0.76-0.83 vs. perinatal factor models: AUC 0.56-0.69). In simplified key risk factors models, predicted probabilities for Early and Late discharge compared favorably with observed rates. Furthermore, the AUC (0.75-0.77) were similar to those of models including the full factor set. Conclusions Prediction of Early or Late discharge is poor if only perinatal factors are considered, but improves substantially with knowledge of later-occurring morbidities. Prediction using a few key risk factors is comparable to full models, and may offer a clinically applicable strategy. PMID:20008430

Predicting the Timing of Maturational Spurts in Skeletal Age

PubMed Central

Nahhas, Ramzi W.; Sherwood, Richard J.; Chumlea, Wm. Cameron; Towne, Bradford; Duren, Dana L.

2014-01-01

Measures of maturity provide windows into the timing and tempo of childhood growth and maturation. Delayed maturation in a single child, or systemically in a population, can result from either genetic or environmental factors. In terms of the skeleton, delayed maturation may result in short stature or indicate another underlying issue. Thus, prediction of the timing of a maturational spurt is often desirable in order to determine the likelihood that a child will catch up to their chronological age peers. Serial data from the Fels Longitudinal Study were used to predict future skeletal age conditional on current skeletal age and to predict the timing of maturational spurts. For children who were delayed relative to their chronological age peers, the like-lihood of catch-up maturation increased through the average age of onset of puberty and decreased prior to the average age of peak height velocity. For boys, the probability of an imminent maturational spurt was higher for those who were less mature. For girls aged 11 to 13 years, however, this probability was higher for those who were more mature, potentially indicating the presence of a skeletal maturation plateau between multiple spurts. The prediction model, available on the web, is most relevant to children of European ancestry living in the Midwestern US. Our model may also provide insight into the tempo of maturation for children in other populations, but must be applied with caution if those populations are known to have high burdens of environmental stressors not typical of the Midwestern US. Am J Phys Anthropol 150:68–75, 2013. PMID:23283666

Predictive models to determine imagery strategies employed by children to judge hand laterality.

PubMed

Spruijt, Steffie; Jongsma, Marijtje L A; van der Kamp, John; Steenbergen, Bert

2015-01-01

A commonly used paradigm to study motor imagery is the hand laterality judgment task. The present study aimed to determine which strategies young children employ to successfully perform this task. Children of 5 to 8 years old (N = 92) judged laterality of back and palm view hand pictures in different rotation angles. Response accuracy and response duration were registered. Response durations of the trials with a correct judgment were fitted to a-priori defined predictive sinusoid models, representing different strategies to successfully perform the hand laterality judgment task. The first model predicted systematic changes in response duration as a function of rotation angle of the displayed hand. The second model predicted that response durations are affected by biomechanical constraints of hand rotation. If observed data could be best described by the first model, this would argue for a mental imagery strategy that does not involve motor processes to solve the task. The second model reflects a motor imagery strategy to solve the task. In line with previous research, we showed an age-related increase in response accuracy and decrease in response duration in children. Observed data for both back and palm view showed that motor imagery strategies were used to perform hand laterality judgments, but that not all the children use these strategies (appropriately) at all times. A direct comparison of response duration patterns across age sheds new light on age-related differences in the strategies employed to solve the task. Importantly, the employment of the motor imagery strategy for successful task performance did not change with age.

Accuracy of travel time distribution (TTD) models as affected by TTD complexity, observation errors, and model and tracer selection

USGS Publications Warehouse

Green, Christopher T.; Zhang, Yong; Jurgens, Bryant C.; Starn, J. Jeffrey; Landon, Matthew K.

2014-01-01

Analytical models of the travel time distribution (TTD) from a source area to a sample location are often used to estimate groundwater ages and solute concentration trends. The accuracies of these models are not well known for geologically complex aquifers. In this study, synthetic datasets were used to quantify the accuracy of four analytical TTD models as affected by TTD complexity, observation errors, model selection, and tracer selection. Synthetic TTDs and tracer data were generated from existing numerical models with complex hydrofacies distributions for one public-supply well and 14 monitoring wells in the Central Valley, California. Analytical TTD models were calibrated to synthetic tracer data, and prediction errors were determined for estimates of TTDs and conservative tracer (NO3−) concentrations. Analytical models included a new, scale-dependent dispersivity model (SDM) for two-dimensional transport from the watertable to a well, and three other established analytical models. The relative influence of the error sources (TTD complexity, observation error, model selection, and tracer selection) depended on the type of prediction. Geological complexity gave rise to complex TTDs in monitoring wells that strongly affected errors of the estimated TTDs. However, prediction errors for NO3− and median age depended more on tracer concentration errors. The SDM tended to give the most accurate estimates of the vertical velocity and other predictions, although TTD model selection had minor effects overall. Adding tracers improved predictions if the new tracers had different input histories. Studies using TTD models should focus on the factors that most strongly affect the desired predictions.

EVALUATION OF THE EFFICACY OF EXTRAPOLATION POPULATION MODELING TO PREDICT THE DYNAMICS OF AMERICAMYSIS BAHIA POPULATIONS IN THE LABORATORY

EPA Science Inventory

An age-classified projection matrix model has been developed to extrapolate the chronic (28-35d) demographic responses of Americamysis bahia (formerly Mysidopsis bahia) to population-level response. This study was conducted to evaluate the efficacy of this model for predicting t...

Pregnant Low-Income Teenagers: A Social Structural Model of the Determinants of Abortion-Seeking Behavior.

ERIC Educational Resources Information Center

Dworkin, Rosalind J.; Poindexter, Alfred N.

1980-01-01

Reviews literature on teenage abortion seekers. Suggests and tests a theoretical model designed to predict whether a low-income pregnant teenager will decide to abort or to deliver her baby. Concludes that age and socioemotional variables are the strongest predictive elements in the model. (GC)

Individualized predictions of time to menopause using multiple measurements of antimüllerian hormone.

PubMed

Gohari, Mahmood Reza; Ramezani Tehrani, Fahime; Chenouri, Shojaeddin; Solaymani-Dodaran, Masoud; Azizi, Fereidoun

2016-08-01

The ability of antimüllerian hormone (AMH) to predict age at menopause has been reported in several studies, and a decrease in AMH level has been found to increase the probability of menopause. The rate of decline varies among women, and there is also a variability of decline between women's cycles. As a result, individualized evaluation is required to accurately predict time of menopause. To this end, we have used the AMH trajectories of individual women to predict each one's age at menopause. From a cohort study, 266 women (ages 20-50 y) who had regular and predictable menstrual cycles at the initiation of the study were randomly selected from among 1,265 women for multiple AMH measurements. Participants were visited at approximately 3-year intervals and followed for an average of 6.5 years. Individual likelihood of menopause was predicted by fitting the shared random-effects joint model to the baseline covariates and the specific AMH trajectory of each woman. In total, 23.7% of the women reached menopause during the follow-up period. The estimated mean (SD) AMH concentration at the time of menopause was 0.05 ng/mL (0.06 ng/mL), compared with 1.36 ng/mL (1.85 ng/mL) for those with a regular menstrual cycle at their last assessment. The decline rate in the AMH level varied among age groups, and age was a significant prognostic factor for AMH level (P < 0.001). Adjusting for age and body mass index, each woman had her own specific AMH trajectory. Lower AMH and older age had significant effects on the onset of menopause. Individualized prediction of time to menopause was obtained from the fitted model. Longitudinal measurements of AMH will enable physicians to individualize the prediction of menopause, thereby facilitating counseling on the timing of childbearing or medical management of health issues associated with menopause.

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

Development of Interpretable Predictive Models for BPH and Prostate Cancer.

PubMed

Bermejo, Pablo; Vivo, Alicia; Tárraga, Pedro J; Rodríguez-Montes, J A

2015-01-01

Traditional methods for deciding whether to recommend a patient for a prostate biopsy are based on cut-off levels of stand-alone markers such as prostate-specific antigen (PSA) or any of its derivatives. However, in the last decade we have seen the increasing use of predictive models that combine, in a non-linear manner, several predictives that are better able to predict prostate cancer (PC), but these fail to help the clinician to distinguish between PC and benign prostate hyperplasia (BPH) patients. We construct two new models that are capable of predicting both PC and BPH. An observational study was performed on 150 patients with PSA ≥3 ng/mL and age >50 years. We built a decision tree and a logistic regression model, validated with the leave-one-out methodology, in order to predict PC or BPH, or reject both. Statistical dependence with PC and BPH was found for prostate volume (P-value < 0.001), PSA (P-value < 0.001), international prostate symptom score (IPSS; P-value < 0.001), digital rectal examination (DRE; P-value < 0.001), age (P-value < 0.002), antecedents (P-value < 0.006), and meat consumption (P-value < 0.08). The two predictive models that were constructed selected a subset of these, namely, volume, PSA, DRE, and IPSS, obtaining an area under the ROC curve (AUC) between 72% and 80% for both PC and BPH prediction. PSA and volume together help to build predictive models that accurately distinguish among PC, BPH, and patients without any of these pathologies. Our decision tree and logistic regression models outperform the AUC obtained in the compared studies. Using these models as decision support, the number of unnecessary biopsies might be significantly reduced.

The Reciprocal Influence of Callous-Unemotional Traits, Oppositional Defiant Disorder and Parenting Practices in Preschoolers.

PubMed

Brown, Caitlin A; Granero, Roser; Ezpeleta, Lourdes

2017-04-01

The present study investigates reciprocal associations between positive parenting, parental monitoring, CU traits, and ODD in children assessed at age 3 and again at age 6. Data were collected from a sample of preschoolers (N = 419; 51.58 % female) through diagnostic interviews and questionnaires answered by parents and teachers. Structural equation modeling revealed a bidirectional relationship between poor monitoring and ODD, with poor monitoring at age 3 predicting ODD at age 6 (β = 0.11, p < 0.05), and ODD at age 3 predicting poor monitoring at age 6 (β = 0.10, p < 0.05). While poor monitoring at age 3 predicted CU traits at age 6 (β = 0.11, p < 0.05), CU traits at age 3 predicted positive parenting (β = 0.09, p < 0.05) and ODD (β = 0.13, p < 0.05) at age 6. Results have important implications for early targeted parenting interventions for CU traits and ODD.

An Individual-Tree Growth and Yield Prediction System for Uneven-Aged Shortleaf Pine Stands

Treesearch

Michael M. Huebschmann; Lawrence R. Gering; Thomas B. Lynch; Onesphore Bitoki; Paul A. Murphy

2000-01-01

A system of equations modeling the growth and development of uneven-aged shortleaf pine (Pinus echinata Mill.) stands is described. The prediction system consists of two main components: (1) a distance-independent, individual-tree simulator containing equations that forecast ingrowth, basal-area growth, probability of survival, total and...

The Dangers of Estimating V˙O2max Using Linear, Nonexercise Prediction Models.

PubMed

Nevill, Alan M; Cooke, Carlton B

2017-05-01

This study aimed to compare the accuracy and goodness of fit of two competing models (linear vs allometric) when estimating V˙O2max (mL·kg·min) using nonexercise prediction models. The two competing models were fitted to the V˙O2max (mL·kg·min) data taken from two previously published studies. Study 1 (the Allied Dunbar National Fitness Survey) recruited 1732 randomly selected healthy participants, 16 yr and older, from 30 English parliamentary constituencies. Estimates of V˙O2max were obtained using a progressive incremental test on a motorized treadmill. In study 2, maximal oxygen uptake was measured directly during a fatigue limited treadmill test in older men (n = 152) and women (n = 146) 55 to 86 yr old. In both studies, the quality of fit associated with estimating V˙O2max (mL·kg·min) was superior using allometric rather than linear (additive) models based on all criteria (R, maximum log-likelihood, and Akaike information criteria). Results suggest that linear models will systematically overestimate V˙O2max for participants in their 20s and underestimate V˙O2max for participants in their 60s and older. The residuals saved from the linear models were neither normally distributed nor independent of the predicted values nor age. This will probably explain the absence of a key quadratic age term in the linear models, crucially identified using allometric models. Not only does the curvilinear age decline within an exponential function follow a more realistic age decline (the right-hand side of a bell-shaped curve), but the allometric models identified either a stature-to-body mass ratio (study 1) or a fat-free mass-to-body mass ratio (study 2), both associated with leanness when estimating V˙O2max. Adopting allometric models will provide more accurate predictions of V˙O2max (mL·kg·min) using plausible, biologically sound, and interpretable models.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects.

PubMed

Ziegler, G; Ridgway, G R; Dahnke, R; Gaser, C

2014-08-15

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18-94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects

PubMed Central

Ziegler, G.; Ridgway, G.R.; Dahnke, R.; Gaser, C.

2014-01-01

Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18–94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. PMID:24742919

Planning for subacute care: predicting demand using acute activity data.

PubMed

Green, Janette P; McNamee, Jennifer P; Kobel, Conrad; Seraji, Md Habibur R; Lawrence, Suanne J

2016-01-01

Objective The aim of the present study was to develop a robust model that uses the concept of 'rehabilitation-sensitive' Diagnosis Related Groups (DRGs) in predicting demand for rehabilitation and geriatric evaluation and management (GEM) care following acute in-patient episodes provided in Australian hospitals. Methods The model was developed using statistical analyses of national datasets, informed by a panel of expert clinicians and jurisdictional advice. Logistic regression analysis was undertaken using acute in-patient data, published national hospital statistics and data from the Australasian Rehabilitation Outcomes Centre. Results The predictive model comprises tables of probabilities that patients will require rehabilitation or GEM care after an acute episode, with columns defined by age group and rows defined by grouped Australian Refined (AR)-DRGs. Conclusions The existing concept of rehabilitation-sensitive DRGs was revised and extended. When applied to national data, the model provided a conservative estimate of 83% of the activity actually provided. An example demonstrates the application of the model for service planning. What is known about the topic? Health service planning is core business for jurisdictions and local areas. With populations ageing and an acknowledgement of the underservicing of subacute care, it is timely to find improved methods of estimating demand for this type of care. Traditionally, age-sex standardised utilisation rates for individual DRGs have been applied to Australian Bureau of Statistics (ABS) population projections to predict the future need for subacute services. Improved predictions became possible when some AR-DRGs were designated 'rehabilitation-sensitive'. This improved methodology has been used in several Australian jurisdictions. What does this paper add? This paper presents a new tool, or model, to predict demand for rehabilitation and GEM services based on in-patient acute activity. In this model, the methodology based on rehabilitation-sensitive AR-DRGs has been extended by updating them to AR-DRG Version 7.0, quantifying the level of 'sensitivity' and incorporating the patient's age to improve the prediction of demand for subacute services. What are the implications for practitioners? The predictive model takes the form of tables of probabilities that patients will require rehabilitation or GEM care after an acute episode and can be applied to acute in-patient administrative datasets in any Australian jurisdiction or local area. The use of patient-level characteristics will enable service planners to improve their forecasting of demand for these services. Clinicians and jurisdictional representatives consulted during the project regarded the model favourably and believed that it was an improvement on currently available methods.

Using a generalized linear mixed model approach to explore the role of age, motor proficiency, and cognitive styles in children's reach estimation accuracy.

PubMed

Caçola, Priscila M; Pant, Mohan D

2014-10-01

The purpose was to use a multi-level statistical technique to analyze how children's age, motor proficiency, and cognitive styles interact to affect accuracy on reach estimation tasks via Motor Imagery and Visual Imagery. Results from the Generalized Linear Mixed Model analysis (GLMM) indicated that only the 7-year-old age group had significant random intercepts for both tasks. Motor proficiency predicted accuracy in reach tasks, and cognitive styles (object scale) predicted accuracy in the motor imagery task. GLMM analysis is suitable to explore age and other parameters of development. In this case, it allowed an assessment of motor proficiency interacting with age to shape how children represent, plan, and act on the environment.

Extrapolation of enalapril efficacy from adults to children using pharmacokinetic/pharmacodynamic modelling.

PubMed

Kechagia, Irene-Ariadne; Kalantzi, Lida; Dokoumetzidis, Aristides

2015-11-01

To extrapolate enalapril efficacy to children 0-6 years old, a pharmacokinetic/pharmacodynamic (PKPD) model was built using literature data, with blood pressure as the PD endpoint. A PK model of enalapril was developed from literature paediatric data up to 16 years old. A PD model of enalapril was fitted to adult literature response vs time data with various doses. The final PKPD model was validated with literature paediatric efficacy observations (diastolic blood pressure (DBP) drop after 2 weeks of treatment) in children of age 6 years and higher. The model was used to predict enalapril efficacy for ages 0-6 years. A two-compartment PK model was chosen with weight, reflecting indirectly age as a covariate on clearance and central volume. An indirect link PD model was chosen to describe drug effect. External validation of the model's capability to predict efficacy in children was successful. Enalapril efficacy was extrapolated to ages 1-11 months and 1-6 years finding the mean DBP drop 11.2 and 11.79 mmHg, respectively. Mathematical modelling was used to extrapolate enalapril efficacy to young children to support a paediatric investigation plan targeting a paediatric-use marketing authorization application. © 2015 Royal Pharmaceutical Society.

Pneumococcal vaccine targeting strategy for older adults: customized risk profiling.

PubMed

Balicer, Ran D; Cohen, Chandra J; Leibowitz, Morton; Feldman, Becca S; Brufman, Ilan; Roberts, Craig; Hoshen, Moshe

2014-02-12

Current pneumococcal vaccine campaigns take a broad, primarily age-based approach to immunization targeting, overlooking many clinical and administrative considerations necessary in disease prevention and resource planning for specific patient populations. We aim to demonstrate the utility of a population-specific predictive model for hospital-treated pneumonia to direct effective vaccine targeting. Data was extracted for 1,053,435 members of an Israeli HMO, age 50 and older, during the study period 2008-2010. We developed and validated a logistic regression model to predict hospital-treated pneumonia using training and test samples, including a set of standard and population-specific risk factors. The model's predictive value was tested for prospectively identifying cases of pneumonia and invasive pneumococcal disease (IPD), and was compared to the existing international paradigm for patient immunization targeting. In a multivariate regression, age, co-morbidity burden and previous pneumonia events were most strongly positively associated with hospital-treated pneumonia. The model predicting hospital-treated pneumonia yielded a c-statistic of 0.80. Utilizing the predictive model, the top 17% highest-risk within the study validation population were targeted to detect 54% of those members who were subsequently treated for hospitalized pneumonia in the follow up period. The high-risk population identified through this model included 46% of the follow-up year's IPD cases, and 27% of community-treated pneumonia cases. These outcomes were compared with international guidelines for risk for pneumococcal diseases that accurately identified only 35% of hospitalized pneumonia, 41% of IPD cases and 21% of community-treated pneumonia. We demonstrate that a customized model for vaccine targeting performs better than international guidelines, and therefore, risk modeling may allow for more precise vaccine targeting and resource allocation than current national and international guidelines. Health care managers and policy-makers may consider the strategic potential of utilizing clinical and administrative databases for creating population-specific risk prediction models to inform vaccination campaigns. Copyright © 2013 Elsevier Ltd. All rights reserved.

Interaction between age and race alters predicted survival in colorectal cancer.

PubMed

Phatak, Uma R; Kao, Lillian S; Millas, Stefanos G; Wiatrek, Rebecca L; Ko, Tien C; Wray, Curtis J

2013-10-01

Racial disparities in colorectal cancer persist. Late stage at presentation and lack of stage-specific treatment may be contributing factors. We sought to evaluate the magnitude of disparity remaining after accounting for gender, stage, and treatment using predicted survival models. We used institutional tumor registries from a public health system (two hospitals) and a not-for-profit health system (nine hospitals) from 1995 to 2011. Demographics, stage at diagnosis, treatment, and survival were recorded. Hazard ratios (HRs) and predicted HRs were determined by Cox regression and postestimation analyses. There were 6,990 patients: 55.7 % white, 23.6 % African American, 15.1 % Hispanic, and 5.6 % Asian/other. Predictors of survival were surgery (HR 0.57, 95 % confidence interval [CI] 0.46-0.70), chemotherapy (HR 0.7, 95 % CI 0.62-0.79), female gender (HR 0.87, 95 % CI 0.83-0.90), age (HR 1.04, 95 % CI 1.03-1.05), and African American race (HR 3.6, 95 % CI 1.5-8.4). Balancing for stage, gender, and treatment reduced the predicted HRs for African Americans by 28 % and Hispanics by 17 %. In this model, African American and Hispanics still had the worst predicted HRs at younger ages, but whites had the worst predicted HR after age 75. Gender, stage, and treatment partially accounted for worsened survival in African Americans and Hispanics at all ages. At younger ages, race-related disparities remained which may reflect tumor biology or other unknown factors. Once gender, stage, and treatment are balanced at older ages, the increased mortality observed in whites may be due to factors such as comorbidities. Further system- and patient-level study is needed to investigate reasons for colorectal cancer survival disparities.

The evolution of human phenotypic plasticity: age and nutritional status at maturity.

PubMed

Gage, Timothy B

2003-08-01

Several evolutionary optimal models of human plasticity in age and nutritional status at reproductive maturation are proposed and their dynamics examined. These models differ from previously published models because fertility is not assumed to be a function of body size or nutritional status. Further, the models are based on explicitly human demographic patterns, that is, model human life-tables, model human fertility tables, and, a nutrient flow-based model of maternal nutritional status. Infant survival (instead of fertility as in previous models) is assumed to be a function of maternal nutritional status. Two basic models are examined. In the first the cost of reproduction is assumed to be a constant proportion of total nutrient flow. In the second the cost of reproduction is constant for each birth. The constant proportion model predicts a negative slope of age and nutritional status at maturation. The constant cost per birth model predicts a positive slope of age and nutritional status at maturation. Either model can account for the secular decline in menarche observed over the last several centuries in Europe. A search of the growth literature failed to find definitive empirical documentation of human phenotypic plasticity in age and nutritional status at maturation. Most research strategies confound genetics with phenotypic plasticity. The one study that reports secular trends suggests a marginally insignificant, but positive slope. This view tends to support the constant cost per birth model.

Predictive aging results for cable materials in nuclear power plants

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

Gillen, K.T.; Clough, R.L.

1990-11-01

In this report, we provide a detailed discussion of methodology of predicting cable degradation versus dose rate, temperature, and exposure time and its application to data obtained on a number of additional nuclear power plant cable insulation (a hypalon, a silicon rubber and two ethylenetetrafluoroethylenes) and jacket (a hypalon) materials. We then show that the predicted, low-dose-rate results for our materials are in excellent agreement with long-term (7 to 9 years), low dose-rate results recently obtained for the same material types actually aged under nuclear power plant conditions. Based on a combination of the modelling and long-term results, we findmore » indications of reasonably similar degradation responses among several different commercial formulations for each of the following generic'' materials: hypalon, ethylenetetrafluoroethylene, silicone rubber and PVC. If such generic'' behavior can be further substantiated through modelling and long-term results on additional formulations, predictions of cable life for other commercial materials of the same generic types would be greatly facilitated. Finally, to aid utilities in their cable life extension decisions, we utilize our modelling results to generate lifetime prediction curves for the materials modelled to data. These curves plot expected material lifetime versus dose rate and temperature down to the levels of interest to nuclear power plant aging. 18 refs., 30 figs., 3 tabs.« less

Re-evaluating neonatal-age models for ungulates: Does model choice affect survival estimates?

USGS Publications Warehouse

Grovenburg, Troy W.; Monteith, Kevin L.; Jacques, Christopher N.; Klaver, Robert W.; DePerno, Christopher S.; Brinkman, Todd J.; Monteith, Kyle B.; Gilbert, Sophie L.; Smith, Joshua B.; Bleich, Vernon C.; Swanson, Christopher C.; Jenks, Jonathan A.

2014-01-01

New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001–2009, we captured and radiocollared 174 newborn (≤24-hrs old) ungulates: 76 white-tailed deer (Odocoileus virginianus) in Minnesota and South Dakota, 61 mule deer (O. hemionus) in California, and 37 pronghorn (Antilocapra americana) in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age) in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days) for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days) for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i.e., weekly versus daily) for estimating survival.

A new lifetime estimation model for a quicker LED reliability prediction

NASA Astrophysics Data System (ADS)

Hamon, B. H.; Mendizabal, L.; Feuillet, G.; Gasse, A.; Bataillou, B.

2014-09-01

LED reliability and lifetime prediction is a key point for Solid State Lighting adoption. For this purpose, one hundred and fifty LEDs have been aged for a reliability analysis. LEDs have been grouped following nine current-temperature stress conditions. Stress driving current was fixed between 350mA and 1A and ambient temperature between 85C and 120°C. Using integrating sphere and I(V) measurements, a cross study of the evolution of electrical and optical characteristics has been done. Results show two main failure mechanisms regarding lumen maintenance. The first one is the typically observed lumen depreciation and the second one is a much more quicker depreciation related to an increase of the leakage and non radiative currents. Models of the typical lumen depreciation and leakage resistance depreciation have been made using electrical and optical measurements during the aging tests. The combination of those models allows a new method toward a quicker LED lifetime prediction. These two models have been used for lifetime predictions for LEDs.

Predictive model of third molar eruption after second molar extraction.

PubMed

De-la-Rosa-Gay, Cristina; Valmaseda-Castellón, Eduard; Gay-Escoda, Cosme

2010-03-01

Extraction of second permanent molars is an option for providing space in orthodontic treatment. Although many articles have described its impact on the outcome, there are few data on the prognosis of the eruption of the adjacent third molars. The aims of this investigation were to provide predictive models of eruption of third molars after second permanent molar extraction and to validate them. A total of 48 patients (ages, 11-23 years) who had 128 second permanent molars (54 maxillary, 74 mandibular) extracted during orthodontic treatment were followed until eruption of the third molars was complete. A lineal regression model predicted the final angle of the third molars with the permanent first molar by using the variables of initial angle, jaw, and the developmental stage of the third molar. A logistic regression model predicted the probability of correct eruption by using the variables of initial angle, jaw, sex, age, and the developmental stage of the third molar. 2010 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

The combined effect of age and basal follicle-stimulating hormone on the cost of a live birth at assisted reproductive technology.

PubMed

Henne, Melinda B; Stegmann, Barbara J; Neithardt, Adrienne B; Catherino, William H; Armstrong, Alicia Y; Kao, Tzu-Cheg; Segars, James H

2008-01-01

To predict the cost of a delivery following assisted reproductive technologies (ART). Cost analysis based on retrospective chart analysis. University-based ART program. Women aged >or=26 and

A prediction model for periodontal disease: modelling and validation from a National Survey of 4061 Taiwanese adults.

PubMed

Lai, Hongmin; Su, Chiu-Wen; Yen, Amy Ming-Fang; Chiu, Sherry Yueh-Hsia; Fann, Jean Ching-Yuan; Wu, Wendy Yi-Ying; Chuang, Shu-Lin; Liu, Hsing-Chih; Chen, Hsiu-Hsi; Chen, Li-Sheng

2015-05-01

The aim of this study was to predict periodontal disease (PD) with demographical features, oral health behaviour, and clinical correlates based on a national survey of periodontal disease in Taiwan. A total of 4061 subjects who were enrolled in a cross-sectional nationwide survey on periodontal conditions of residents aged 18 years or older in Taiwan between 2007 and 2008 were included. The community periodontal index (CPI) was used to measure the periodontal status at the subject and sextant levels. Information on demographical features and other relevant predictive factors for PD was collected using a questionnaire. In our study population, 56.2% of subjects had CPI grades ≥3. Periodontitis, as defined by CPI ≥3, was best predicted by a model including age, gender, education, brushing frequency, mobile teeth, gingival bleeding, smoking, and BMI. The area under the curve (AUC) for the final prediction model was 0.712 (0.690-0.734). The AUC was 0.702 (0.665-0.740) according to cross-validation. A prediction model for PD using information obtained from questionnaires was developed. The feasibility of its application to risk stratification of PD should be considered with regard to community-based screening for asymptomatic PD. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A Conceptual Model for the Development of Externalizing Behavior Problems Among Kindergarten Children of Alcoholic Families: Role of Parenting and Children's Self-Regulation

PubMed Central

Eiden, Rina D.; Edwards, Ellen P.; Leonard, Kenneth E.

2009-01-01

The purpose of this study was to test a conceptual model predicting children's externalizing behavior problems in kindergarten in a sample of children with alcoholic (n = 130) and nonalcoholic (n = 97) parents. The model examined the role of parents' alcohol diagnoses, depression, and antisocial behavior at 12–18 months of child age in predicting parental warmth/sensitivity at 2 years of child age. Parental warmth/sensitivity at 2 years was hypothesized to predict children's self-regulation at 3 years (effortful control and internalization of rules), which in turn was expected to predict externalizing behavior problems in kindergarten. Structural equation modeling was largely supportive of this conceptual model. Fathers' alcohol diagnosis at 12–18 months was associated with lower maternal and paternal warmth/sensitivity at 2 years. Lower maternal warmth/sensitivity was longitudinally predictive of lower child self-regulation at 3 years, which in turn was longitudinally predictive of higher externalizing behavior problems in kindergarten, after controlling for prior behavior problems. There was a direct association between parents' depression and children's externalizing behavior problems. Results indicate that one pathway to higher externalizing behavior problems among children of alcoholics may be via parenting and self-regulation in the toddler to preschool years. PMID:17723044

Classification Models to Predict Survival of Kidney Transplant Recipients Using Two Intelligent Techniques of Data Mining and Logistic Regression.

PubMed

Nematollahi, M; Akbari, R; Nikeghbalian, S; Salehnasab, C

2017-01-01

Kidney transplantation is the treatment of choice for patients with end-stage renal disease (ESRD). Prediction of the transplant survival is of paramount importance. The objective of this study was to develop a model for predicting survival in kidney transplant recipients. In a cross-sectional study, 717 patients with ESRD admitted to Nemazee Hospital during 2008-2012 for renal transplantation were studied and the transplant survival was predicted for 5 years. The multilayer perceptron of artificial neural networks (MLP-ANN), logistic regression (LR), Support Vector Machine (SVM), and evaluation tools were used to verify the determinant models of the predictions and determine the independent predictors. The accuracy, area under curve (AUC), sensitivity, and specificity of SVM, MLP-ANN, and LR models were 90.4%, 86.5%, 98.2%, and 49.6%; 85.9%, 76.9%, 97.3%, and 26.1%; and 84.7%, 77.4%, 97.5%, and 17.4%, respectively. Meanwhile, the independent predictors were discharge time creatinine level, recipient age, donor age, donor blood group, cause of ESRD, recipient hypertension after transplantation, and duration of dialysis before transplantation. SVM and MLP-ANN models could efficiently be used for determining survival prediction in kidney transplant recipients.

Predictive factors of alcohol and tobacco use in adolescents

PubMed Central

Alvarez-Aguirre, Alicia; Alonso-Castillo, María Magdalena; Zanetti, Ana Carolina Guidorizzi

2014-01-01

OBJECTIVES: to analyze the effect of self-esteem, assertiveness, self-efficacy and resiliency on alcohol and tobacco consumption in adolescents. METHOD: a descriptive and correlational study was undertaken with 575 adolescents in 2010. The Self-Esteem Scale, the Situational Confidence Scale, the Assertiveness Questionnaire and the Resiliency Scale were used. RESULTS: the adjustment of the logistic regression model, considering age, sex, self-esteem, assertiveness, self-efficacy and resiliency, demonstrates significance in the consumption of alcohol and tobacco. Age, resiliency and assertiveness predict alcohol consumption in the lifetime and assertiveness predicts alcohol consumption in the last year. Similarly, age and sex predict tobacco consumption in the lifetime and age in the last year. CONCLUSION: this study can offer important information to plan nursing interventions involving adolescent alcohol and tobacco users. PMID:25591103

Antenatal blood pressure for prediction of pre-eclampsia, preterm birth, and small for gestational age babies: development and validation in two general population cohorts

PubMed Central

Silverwood, Richard J; de Stavola, Bianca L; Inskip, Hazel; Cooper, Cyrus; Godfrey, Keith M; Crozier, Sarah; Fraser, Abigail; Nelson, Scott M; Lawlor, Debbie A; Tilling, Kate

2015-01-01

Study question Can routine antenatal blood pressure measurements between 20 and 36 weeks’ gestation contribute to the prediction of pre-eclampsia and its associated adverse outcomes? Methods This study used repeated antenatal measurements of blood pressure from 12 996 women in the Avon Longitudinal Study of Parents and Children (ALSPAC) to develop prediction models and validated these in 3005 women from the Southampton Women’s Survey (SWS). A model based on maternal early pregnancy characteristics only (BMI, height, age, parity, smoking, existing and previous gestational hypertension and diabetes, and ethnicity) plus initial mean arterial pressure was compared with a model additionally including current mean arterial pressure, a model including the deviation of current mean arterial pressure from a stratified normogram, and a model including both at different gestational ages from 20-36 weeks. Study answer and limitations The addition of blood pressure measurements from 28 weeks onwards improved prediction models compared with use of early pregnancy risk factors alone, but they contributed little to the prediction of preterm birth or small for gestational age. Though multiple imputation of missing data was used to increase the sample size and minimise selection bias, the validation sample might have been slightly underpowered as the number of cases of pre-eclampsia was just below the recommended 100. Several risk factors were self reported, potentially introducing measurement error, but this reflects how information would be obtained in clinical practice. What this study adds The addition of routinely collected blood pressure measurements from 28 weeks onwards improves predictive models for pre-eclampsia based on blood pressure in early pregnancy and other characteristics, facilitating a reduction in scheduled antenatal care. Funding, competing interests, data sharing UK Wellcome Trust, US National Institutes of Health, and UK Medical Research Council. Other funding sources for authors are detailed in the full online paper. With the exceptions of CM-W, HMI, and KMG there were no competing interests. PMID:26578347

Single non-invasive model to diagnose non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH).

PubMed

Otgonsuren, Munkhzul; Estep, Michael J; Hossain, Nayeem; Younossi, Elena; Frost, Spencer; Henry, Linda; Hunt, Sharon; Fang, Yun; Goodman, Zachary; Younossi, Zobair M

2014-12-01

Non-alcoholic steatohepatitis (NASH) is the progressive form of non-alcoholic fatty liver disease (NAFLD). A liver biopsy is considered the "gold standard" for diagnosing/staging NASH. Identification of NAFLD/NASH using non-invasive tools is important for intervention. The study aims were to: develop/validate the predictive performance of a non-invasive model (index of NASH [ION]); assess the performance of a recognized non-invasive model (fatty liver index [FLI]) compared with ION for NAFLD diagnosis; determine which non-invasive model (FLI, ION, or NAFLD fibrosis score [NFS]) performed best in predicting age-adjusted mortality. From the National Health and Nutrition Examination Survey III database, anthropometric, clinical, ultrasound, laboratory, and mortality data were obtained (n = 4458; n = 861 [19.3%] NAFLD by ultrasound) and used to develop the ION model, and then to compare the ION and FLI models for NAFLD diagnosis. For validation and diagnosis of NASH, liver biopsy data were used (n = 152). Age-adjusted Cox proportional hazard modeling estimated the association among the three non-invasive tests (FLI, ION, and NFS) and mortality. FLI's threshold score > 60 and ION's threshold score > 22 had similar specificity (FLI = 80% vs ION = 82%) for NAFLD diagnosis; FLI < 30 (80% sensitivity) and ION < 11 (81% sensitivity) excluded NAFLD. An ION score > 50 predicted histological NASH (92% specificity); the FLI model did not predict NASH or mortality. The ION model was best in predicting cardiovascular/diabetes-related mortality; NFS predicted overall or diabetes-related mortality. The ION model was superior in predicting NASH and mortality compared with the FLI model. Studies are needed to validate ION. © 2014 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

Investigating the impact of age-depended hair colour darkening during childhood on DNA-based hair colour prediction with the HIrisPlex system.

PubMed

Kukla-Bartoszek, Magdalena; Pośpiech, Ewelina; Spólnicka, Magdalena; Karłowska-Pik, Joanna; Strapagiel, Dominik; Żądzińska, Elżbieta; Rosset, Iwona; Sobalska-Kwapis, Marta; Słomka, Marcin; Walsh, Susan; Kayser, Manfred; Sitek, Aneta; Branicki, Wojciech

2018-06-06

Predictive DNA analysis of externally visible characteristics exerts an increasing influence on contemporary forensic and anthropological investigations, with pigmentation traits currently being the most advanced for predictive modelling. Since pigmentation prediction error in some cases may be due to the result of age-related hair colour darkening, and sex influence in eye colour, this study aims to investigate these less explored phenomena on a group of juvenile individuals. Pigmentation phenotypes of children between the age of 6-13 years old were evaluated, in addition to data about their hair colour during early childhood from a select number of these individuals. The HIrisPlex models for DNA-based eye and hair colour prediction were used with input from SNP genotyping using massive parallel sequencing. Analysis of the total group of 476 children showed high accuracy in blue (AUC = 0.89) and brown (AUC = 0.91) eye colour prediction, while hair colour was predicted with AUC = 0.64 for blond, AUC = 0.64 for brown and AUC = 0.97 for red. 70.8% (n = 143) of the total number of children phenotypically blond for hair colour during early childhood progressed to brown during advanced childhood. In 70.6% (n = 101) of those cases, an incorrect blond hair prediction was made during the time of analysis. A noticeable decline in AUC values for blond (from 0.76 to 0.65) and brown (from 0.72 to 0.64) were observed when comparing hair colour prediction outcomes for the phenotypes recorded for the two different time points (at the age of 2-3 and 6-13). The number of incorrect blond hair colour predictions was significantly higher in children with brown hair at age 6-13 who were blond at early childhood (n = 47, 32.9%), relative to children who had brown hair at both time points (n = 6, 9.4%). However, in 28.0% (n = 40) of children who did experience hair colour darkening, HIrisPlex provided the correct prediction for the darkened hair colour phenotype, despite them being blond in early childhood. Our study implies that HIrisPlex can correctly predict adult hair colour in some individuals who experience age-related hair colour darkening during adolescence. However, in most instances prediction seems to default to the pre-adolescent hair colour for individuals with this phenomenon. In the future, the full adolescent age range in which hair colour darkening can occur should be considered in the study samples used for training hair colour prediction models to obtain a more complete picture of the phenomenon and its impact on DNA-based hair colour prediction in adults. Copyright © 2018 Elsevier B.V. All rights reserved.

Mortality and One-Year Functional Outcome in Elderly and Very Old Patients with Severe Traumatic Brain Injuries: Observed and Predicted

PubMed Central

Røe, Cecilie; Skandsen, Toril; Manskow, Unn; Ader, Tiina; Anke, Audny

2015-01-01

The aim of the present study was to evaluate mortality and functional outcome in old and very old patients with severe traumatic brain injury (TBI) and compare to the predicted outcome according to the internet based CRASH (Corticosteroid Randomization After Significant Head injury) model based prediction, from the Medical Research Council (MRC). Methods. Prospective, national multicenter study including patients with severe TBI ≥65 years. Predicted mortality and outcome were calculated based on clinical information (CRASH basic) (age, GCS score, and pupil reactivity to light), as well as with additional CT findings (CRASH CT). Observed 14-day mortality and favorable/unfavorable outcome according to the Glasgow Outcome Scale at one year was compared to the predicted outcome according to the CRASH models. Results. 97 patients, mean age 75 (SD 7) years, 64% men, were included. Two patients were lost to follow-up; 48 died within 14 days. The predicted versus the observed odds ratio (OR) for mortality was 2.65. Unfavorable outcome (GOSE < 5) was observed at one year follow-up in 72% of patients. The CRASH models predicted unfavorable outcome in all patients. Conclusion. The CRASH model overestimated mortality and unfavorable outcome in old and very old Norwegian patients with severe TBI. PMID:26688614

A survival model for individual shortleaf pine trees in even-aged natural stands

Treesearch

Thomas B. Lynch; Michael M. Huebschmann; Paul A. Murphy

2000-01-01

A model was developed that predicts the probability of survival for individual shortleaf pine (Pinus echinata Mill.) trees growing in even-aged natural stands. Data for model development were obtained from the first two measurements of permanently established plots located in naturally occurring shortleaf pine forests on the Ouachita and...

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.

Predicting the risk for colorectal cancer with personal characteristics and fecal immunochemical test.

PubMed

Li, Wen; Zhao, Li-Zhong; Ma, Dong-Wang; Wang, De-Zheng; Shi, Lei; Wang, Hong-Lei; Dong, Mo; Zhang, Shu-Yi; Cao, Lei; Zhang, Wei-Hua; Zhang, Xi-Peng; Zhang, Qing-Huai; Yu, Lin; Qin, Hai; Wang, Xi-Mo; Chen, Sam Li-Sheng

2018-05-01

We aimed to predict colorectal cancer (CRC) based on the demographic features and clinical correlates of personal symptoms and signs from Tianjin community-based CRC screening data.A total of 891,199 residents who were aged 60 to 74 and were screened in 2012 were enrolled. The Lasso logistic regression model was used to identify the predictors for CRC. Predictive validity was assessed by the receiver operating characteristic (ROC) curve. Bootstrapping method was also performed to validate this prediction model.CRC was best predicted by a model that included age, sex, education level, occupations, diarrhea, constipation, colon mucosa and bleeding, gallbladder disease, a stressful life event, family history of CRC, and a positive fecal immunochemical test (FIT). The area under curve (AUC) for the questionnaire with a FIT was 84% (95% CI: 82%-86%), followed by 76% (95% CI: 74%-79%) for a FIT alone, and 73% (95% CI: 71%-76%) for the questionnaire alone. With 500 bootstrap replications, the estimated optimism (<0.005) shows good discrimination in validation of prediction model.A risk prediction model for CRC based on a series of symptoms and signs related to enteric diseases in combination with a FIT was developed from first round of screening. The results of the current study are useful for increasing the awareness of high-risk subjects and for individual-risk-guided invitations or strategies to achieve mass screening for CRC.

Using Transport Diagnostics to Understand Chemistry Climate Model Ozone Simulations

NASA Technical Reports Server (NTRS)

Strahan, S. E.; Douglass, A. R.; Stolarski, R. S.; Akiyoshi, H.; Bekki, S.; Braesicke, P.; Butchart, N.; Chipperfield, M. P.; Cugnet, D.; Dhomse, S.;

BioAge: Toward A Multi-Determined, Mechanistic Account of Cognitive Aging

PubMed Central

DeCarlo, Correne A.; Tuokko, Holly A.; Williams, Dorothy; Dixon, Roger A.; MacDonald, Stuart W.S.

2014-01-01

The search for reliable early indicators of age-related cognitive decline represents a critical avenue for progress in aging research. Chronological age is a commonly used developmental index; however, it offers little insight into the mechanisms underlying cognitive decline. In contrast, biological age (BioAge), reflecting the vitality of essential biological systems, represents a promising operationalization of developmental time. Current BioAge models have successfully predicted age-related cognitive deficits. Research on aging-related cognitive function indicates that the interaction of multiple risk and protective factors across the human lifespan confers individual risk for late-life cognitive decline, implicating a multi-causal explanation. In this review, we explore current BioAge models, describe three broad yet pathologically relevant biological processes linked to cognitive decline, and propose a novel operationalization of BioAge accounting for both moderating and causal mechanisms of cognitive decline and dementia. We argue that a multivariate and mechanistic BioAge approach will lead to a greater understanding of disease pathology as well as more accurate prediction and early identification of late-life cognitive decline. PMID:25278166

BioAge: toward a multi-determined, mechanistic account of cognitive aging.

PubMed

DeCarlo, Correne A; Tuokko, Holly A; Williams, Dorothy; Dixon, Roger A; MacDonald, Stuart W S

2014-11-01

The search for reliable early indicators of age-related cognitive decline represents a critical avenue for progress in aging research. Chronological age is a commonly used developmental index; however, it offers little insight into the mechanisms underlying cognitive decline. In contrast, biological age (BioAge), reflecting the vitality of essential biological systems, represents a promising operationalization of developmental time. Current BioAge models have successfully predicted age-related cognitive deficits. Research on aging-related cognitive function indicates that the interaction of multiple risk and protective factors across the human lifespan confers individual risk for late-life cognitive decline, implicating a multi-causal explanation. In this review, we explore current BioAge models, describe three broad yet pathologically relevant biological processes linked to cognitive decline, and propose a novel operationalization of BioAge accounting for both moderating and causal mechanisms of cognitive decline and dementia. We argue that a multivariate and mechanistic BioAge approach will lead to a greater understanding of disease pathology as well as more accurate prediction and early identification of late-life cognitive decline. Copyright © 2014 Elsevier B.V. All rights reserved.

Reported maternal styles and substance use: a cross-sectional study among educated Albanian young adults.

PubMed

Kalyva, Efrosini; Melonashi, Erika

2014-05-01

The study explored a predictive model of substance use including perceived maternal parenting style, age and gender. Participants were 347 Albanian young adults (144 males and 203 females) aged 18 to 28 years. They completed the Parental Authority Questionnaire and the Adolescent Alcohol and Drug Involvement Scale. Gender, perceived authoritative maternal style, and age predicted a proportion of substance use involvement. Gender and perceived authoritative maternal style also predicted the proportion of young people at risk for substance use or abuse. Implications of the findings and limitations of the study are discussed.

Predicting clinical symptoms of attention deficit hyperactivity disorder based on temporal patterns between and within intrinsic connectivity networks.

PubMed

Wang, Xun-Heng; Jiao, Yun; Li, Lihua

2017-10-24

Attention deficit hyperactivity disorder (ADHD) is a common brain disorder with high prevalence in school-age children. Previously developed machine learning-based methods have discriminated patients with ADHD from normal controls by providing label information of the disease for individuals. Inattention and impulsivity are the two most significant clinical symptoms of ADHD. However, predicting clinical symptoms (i.e., inattention and impulsivity) is a challenging task based on neuroimaging data. The goal of this study is twofold: to build predictive models for clinical symptoms of ADHD based on resting-state fMRI and to mine brain networks for predictive patterns of inattention and impulsivity. To achieve this goal, a cohort of 74 boys with ADHD and a cohort of 69 age-matched normal controls were recruited from the ADHD-200 Consortium. Both structural and resting-state fMRI images were obtained for each participant. Temporal patterns between and within intrinsic connectivity networks (ICNs) were applied as raw features in the predictive models. Specifically, sample entropy was taken asan intra-ICN feature, and phase synchronization (PS) was used asan inter-ICN feature. The predictive models were based on the least absolute shrinkage and selectionator operator (LASSO) algorithm. The performance of the predictive model for inattention is r=0.79 (p<10 -8 ), and the performance of the predictive model for impulsivity is r=0.48 (p<10 -8 ). The ICN-related predictive patterns may provide valuable information for investigating the brain network mechanisms of ADHD. In summary, the predictive models for clinical symptoms could be beneficial for personalizing ADHD medications. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Predictive Factors of Regular Physical Activity among Middle-Aged Women in the West of Iran, Hamadan: Application of PRECEDE Model.

PubMed

Emdadi, Shohreh; Hazavehie, Seyed Mohammad Mehdi; Soltanian, Alireza; Bashirian, Saeed; Heidari Moghadam, Rashid

2015-01-01

Regular physical activity is important for midlife women. Models and theories help better understanding this behavior among middle-aged women and better planning for change behavior in target group. This study aimed to investigate predictive factors of regular physical activity among middle-aged women based on PRECEDE model as a theoretical framework. This descriptive-analytical study was performed on 866 middle-aged women of Hamadan City western Iran, recruited with a proportional stratified sampling method in 2015. The participants completed a self-administered questionnaire including questions on demographic characteristics and PRECEDE model constructs and IPAQ questionnaire. Data were then analyzed by SPSS-16 and AMOS-16 using the Pearson correlation test and the pathway analysis method. Overall, 57% of middle-aged women were inactive (light level) or not sufficiently active. With SEM (Structural Equation Modeling) analysis, knowledge b=0.84, P<0.001, attitude b=0.799, P<0.001, self-efficacy b=0.633, P<0.001 as predisposing factor and social support as reinforcing factor b=0.2, P<0.001 were the most important predictors for physical activity among middle-aged women in Hamadan. The framework of the PRECEDE model is useful in understanding regular physical activity among middle-aged women. Furthermore, results showed the importance of predisposing and reinforcing factors when planning educational interventions.

Peer Rejection, Aggressive or Withdrawn Behavior, and Psychological Maladjustment from Ages 5 to 12: An Examination of Four Predictive Models

ERIC Educational Resources Information Center

Ladd, Gary W.

2006-01-01

Findings yielded a comprehensive portrait of the predictive relations among children's aggressive or withdrawn behaviors, peer rejection, and psychological maladjustment across the 5-12 age period. Examination of peer rejection in different variable contexts and across repeated intervals throughout childhood revealed differences in the timing,…

Evaluating the All-Ages Lead Model Using SiteSpecific Data ...

EPA Pesticide Factsheets

Lead (Pb) exposure continues to be a problem in the United States. Even after years of progress in reducing environmental levels, CDC estimates at least 500,000 U.S. children ages 1-5 years have blood Pb levels (BLL) above the CDC reference level of 5 µg/dL. Childhood Pb exposure is associated with neurological consequences and public health professionals continue to work to reduce Pb exposures. To better understand the relationship between exposure and BLL, the USEPA has developed a beta version of the All-Ages Lead Model (AALM). Compared to the Integrated Exposure Uptake Biokinetics (IEUBK) Model for lead in children, the AALM provides greater flexibility to describe Pb exposures (acute or chronic, constant or intermittent) for any age. At this time, the AALM has the capability to predict exposure in each of the following media: dust/soil, water, air, food, and other. As part of an interagency test group, we evaluated the ability of the AALM beta v4.2 (Leggett version) to predict BLLs for children that were exposed to Pb in their environment near the John T. Lewis and Bros Lead Smelter Superfund site. The model predicted that fourteen children met our inclusion criteria that spent less than 20 hours a week away from the home (e.g., no daycare or school) and had paired BLL and environmental sampling data (i.e., Pb in soil, window sill dust, front door dust, floor dust, and drinking water). The model can predict average BLLs, but it remains difficult to predic

Predictors of responses to stress among families coping with poverty-related stress.

PubMed

Santiago, Catherine DeCarlo; Etter, Erica Moran; Wadsworth, Martha E; Raviv, Tali

2012-05-01

This study tested how poverty-related stress (PRS), psychological distress, and responses to stress predicted future effortful coping and involuntary stress responses one year later. In addition, we explored age, sex, ethnicity, and parental influences on responses to stress over time. Hierarchical linear modeling analyses conducted with 98 low-income families (300 family members: 136 adults, 82 school-aged children, 82 adolescents) revealed that primary control coping, secondary control coping, disengagement, involuntary engagement, and involuntary disengagement each significantly predicted future use of that response. Primary and secondary control coping also predicted less maladaptive future responses to stress, while involuntary responses to stress undermined the development of adaptive responding. Age, sex, and interactions among PRS and prior coping were also found to predict certain responses to stress. In addition, child subgroup analyses demonstrate the importance of parental modeling of coping and involuntary stress responses, and warmth/nurturance and monitoring practices. Results are discussed with regard to the implications for preventive interventions with families in poverty.

Robust human body model injury prediction in simulated side impact crashes.

PubMed

Golman, Adam J; Danelson, Kerry A; Stitzel, Joel D

2016-01-01

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.

Predictors of outcome after elective endovascular abdominal aortic aneurysm repair and external validation of a risk prediction model.

PubMed

Wisniowski, Brendan; Barnes, Mary; Jenkins, Jason; Boyne, Nicholas; Kruger, Allan; Walker, Philip J

2011-09-01

Endovascular abdominal aortic aneurysm (AAA) repair (EVAR) has been associated with lower operative mortality and morbidity than open surgery but comparable long-term mortality and higher delayed complication and reintervention rates. Attention has therefore been directed to identifying preoperative and operative variables that influence outcomes after EVAR. Risk-prediction models, such as the EVAR Risk Assessment (ERA) model, have also been developed to help surgeons plan EVAR procedures. The aims of this study were (1) to describe outcomes of elective EVAR at the Royal Brisbane and Women's Hospital (RBWH), (2) to identify preoperative and operative variables predictive of outcomes after EVAR, and (3) to externally validate the ERA model. All elective EVAR procedures at the RBWH before July 1, 2009, were reviewed. Descriptive analyses were performed to determine the outcomes. Univariate and multivariate analyses were performed to identify preoperative and operative variables predictive of outcomes after EVAR. Binomial logistic regression analyses were used to externally validate the ERA model. Before July 1, 2009, 197 patients (172 men), who were a mean age of 72.8 years, underwent elective EVAR at the RBWH. Operative mortality was 1.0%. Survival was 81.1% at 3 years and 63.2% at 5 years. Multivariate analysis showed predictors of survival were age (P = .0126), American Society of Anesthesiologists (ASA) score (P = .0180), and chronic obstructive pulmonary disease (P = .0348) at 3 years and age (P = .0103), ASA score (P = .0006), renal failure (P = .0048), and serum creatinine (P = .0022) at 5 years. Aortic branch vessel score was predictive of initial (30-day) type II endoleak (P = .0015). AAA tortuosity was predictive of midterm type I endoleak (P = .0251). Female sex was associated with lower rates of initial clinical success (P = .0406). The ERA model fitted RBWH data well for early death (C statistic = .906), 3-year survival (C statistic = .735), 5-year survival (C statistic = .800), and initial type I endoleak (C statistic = .850). The outcomes of elective EVAR at the RBWH are broadly consistent with those of a nationwide Australian audit and recent randomized trials. Age and ASA score are independent predictors of midterm survival after elective EVAR. The ERA model predicts mortality-related outcomes and initial type I endoleak well for RBWH elective EVAR patients. Copyright © 2011 Society for Vascular Surgery. All rights reserved.

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis.

PubMed

Lee, Byeong-Ju; Kim, Hye-Youn; Lim, Sa Rang; Huang, Linfang; Choi, Hyung-Kyoon

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values.

Discrimination and prediction of cultivation age and parts of Panax ginseng by Fourier-transform infrared spectroscopy combined with multivariate statistical analysis

PubMed Central

Lim, Sa Rang; Huang, Linfang

2017-01-01

Panax ginseng C.A. Meyer is a herb used for medicinal purposes, and its discrimination according to cultivation age has been an important and practical issue. This study employed Fourier-transform infrared (FT-IR) spectroscopy with multivariate statistical analysis to obtain a prediction model for discriminating cultivation ages (5 and 6 years) and three different parts (rhizome, tap root, and lateral root) of P. ginseng. The optimal partial-least-squares regression (PLSR) models for discriminating ginseng samples were determined by selecting normalization methods, number of partial-least-squares (PLS) components, and variable influence on projection (VIP) cutoff values. The best prediction model for discriminating 5- and 6-year-old ginseng was developed using tap root, vector normalization applied after the second differentiation, one PLS component, and a VIP cutoff of 1.0 (based on the lowest root-mean-square error of prediction value). In addition, for discriminating among the three parts of P. ginseng, optimized PLSR models were established using data sets obtained from vector normalization, two PLS components, and VIP cutoff values of 1.5 (for 5-year-old ginseng) and 1.3 (for 6-year-old ginseng). To our knowledge, this is the first study to provide a novel strategy for rapidly discriminating the cultivation ages and parts of P. ginseng using FT-IR by selected normalization methods, number of PLS components, and VIP cutoff values. PMID:29049369

Application of statistical shape analysis for the estimation of bone and forensic age using the shapes of the 2nd, 3rd, and 4th cervical vertebrae in a young Japanese population.

PubMed

Rhee, Chang-Hoon; Shin, Sang Min; Choi, Yong-Seok; Yamaguchi, Tetsutaro; Maki, Koutaro; Kim, Yong-Il; Kim, Seong-Sik; Park, Soo-Byung; Son, Woo-Sung

2015-12-01

From computed tomographic images, the dentocentral synchondrosis can be identified in the second cervical vertebra. This can demarcate the border between the odontoid process and the body of the 2nd cervical vertebra and serve as a good model for the prediction of bone and forensic age. Nevertheless, until now, there has been no application of the 2nd cervical vertebra based on the dentocentral synchondrosis. In this study, statistical shape analysis was used to build bone and forensic age estimation regression models. Following the principles of statistical shape analysis and principal components analysis, we used cone-beam computed tomography (CBCT) to evaluate a Japanese population (35 males and 45 females, from 5 to 19 years old). The narrowest prediction intervals among the multivariate regression models were 19.63 for bone age and 2.99 for forensic age. There was no significant difference between form space and shape space in the bone and forensic age estimation models. However, for gender comparison, the bone and forensic age estimation models for males had the higher explanatory power. This study derived an improved objective and quantitative method for bone and forensic age estimation based on only the 2nd, 3rd and 4th cervical vertebral shapes. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Age structure is critical to the population dynamics and survival of honeybee colonies.

PubMed

Betti, M I; Wahl, L M; Zamir, M

2016-11-01

Age structure is an important feature of the division of labour within honeybee colonies, but its effects on colony dynamics have rarely been explored. We present a model of a honeybee colony that incorporates this key feature, and use this model to explore the effects of both winter and disease on the fate of the colony. The model offers a novel explanation for the frequently observed phenomenon of 'spring dwindle', which emerges as a natural consequence of the age-structured dynamics. Furthermore, the results indicate that a model taking age structure into account markedly affects the predicted timing and severity of disease within a bee colony. The timing of the onset of disease with respect to the changing seasons may also have a substantial impact on the fate of a honeybee colony. Finally, simulations predict that an infection may persist in a honeybee colony over several years, with effects that compound over time. Thus, the ultimate collapse of the colony may be the result of events several years past.

Predictive model for serious bacterial infections among infants younger than 3 months of age.

PubMed

Bachur, R G; Harper, M B

2001-08-01

To develop a data-derived model for predicting serious bacterial infection (SBI) among febrile infants <3 months old. All infants /=38.0 degrees C seen in an urban emergency department (ED) were retrospectively identified. SBI was defined as a positive culture of urine, blood, or cerebrospinal fluid. Tree-structured analysis via recursive partitioning was used to develop the model. SBI or No-SBI was the dichotomous outcome variable, and age, temperature, urinalysis (UA), white blood cell (WBC) count, absolute neutrophil count, and cerebrospinal fluid WBC were entered as potential predictors. The model was tested by V-fold cross-validation. Of 5279 febrile infants studied, SBI was diagnosed in 373 patients (7%): 316 urinary tract infections (UTIs), 17 meningitis, and 59 bacteremia (8 with meningitis, 11 with UTIs). The model sequentially used 4 clinical parameters to define high-risk patients: positive UA, WBC count >/=20 000/mm(3) or /=39.6 degrees C, and age <13 days. The sensitivity of the model for SBI is 82% (95% confidence interval [CI]: 78%-86%) and the negative predictive value is 98.3% (95% CI: 97.8%-98.7%). The negative predictive value for bacteremia or meningitis is 99.6% (95% CI: 99.4%-99.8%). The relative risk between high- and low-risk groups is 12.1 (95% CI: 9.3-15.6). Sixty-six SBI patients (18%) were misclassified into the lower risk group: 51 UTIs, 14 with bacteremia, and 1 with meningitis. Decision-tree analysis using common clinical variables can reasonably predict febrile infants at high-risk for SBI. Sequential use of UA, WBC count, temperature, and age can identify infants who are at high risk of SBI with a relative risk of 12.1 compared with lower-risk infants.

Comparing self-reported health status and diagnosis-based risk adjustment to predict 1- and 2 to 5-year mortality.

PubMed

Pietz, Kenneth; Petersen, Laura A

2007-04-01

To compare the ability of two diagnosis-based risk adjustment systems and health self-report to predict short- and long-term mortality. Data were obtained from the Department of Veterans Affairs (VA) administrative databases. The study population was 78,164 VA beneficiaries at eight medical centers during fiscal year (FY) 1998, 35,337 of whom completed an 36-Item Short Form Health Survey for veterans (SF-36V) survey. We tested the ability of Diagnostic Cost Groups (DCGs), Adjusted Clinical Groups (ACGs), SF-36V Physical Component score (PCS) and Mental Component Score (MCS), and eight SF-36V scales to predict 1- and 2-5 year all-cause mortality. The additional predictive value of adding PCS and MCS to ACGs and DCGs was also evaluated. Logistic regression models were compared using Akaike's information criterion, the c-statistic, and the Hosmer-Lemeshow test. The c-statistics for the eight scales combined with age and gender were 0.766 for 1-year mortality and 0.771 for 2-5-year mortality. For DCGs with age and gender the c-statistics for 1- and 2-5-year mortality were 0.778 and 0.771, respectively. Adding PCS and MCS to the DCG model increased the c-statistics to 0.798 for 1-year and 0.784 for 2-5-year mortality. The DCG model showed slightly better performance than the eight-scale model in predicting 1-year mortality, but the two models showed similar performance for 2-5-year mortality. Health self-report may add health risk information in addition to age, gender, and diagnosis for predicting longer-term mortality.

Comparing Self-Reported Health Status and Diagnosis-Based Risk Adjustment to Predict 1- and 2 to 5-Year Mortality

PubMed Central

Pietz, Kenneth; Petersen, Laura A

2007-01-01

Objectives To compare the ability of two diagnosis-based risk adjustment systems and health self-report to predict short- and long-term mortality. Data Sources/Study Setting Data were obtained from the Department of Veterans Affairs (VA) administrative databases. The study population was 78,164 VA beneficiaries at eight medical centers during fiscal year (FY) 1998, 35,337 of whom completed an 36-Item Short Form Health Survey for veterans (SF-36V) survey. Study Design We tested the ability of Diagnostic Cost Groups (DCGs), Adjusted Clinical Groups (ACGs), SF-36V Physical Component score (PCS) and Mental Component Score (MCS), and eight SF-36V scales to predict 1- and 2–5 year all-cause mortality. The additional predictive value of adding PCS and MCS to ACGs and DCGs was also evaluated. Logistic regression models were compared using Akaike's information criterion, the c-statistic, and the Hosmer–Lemeshow test. Principal Findings The c-statistics for the eight scales combined with age and gender were 0.766 for 1-year mortality and 0.771 for 2–5-year mortality. For DCGs with age and gender the c-statistics for 1- and 2–5-year mortality were 0.778 and 0.771, respectively. Adding PCS and MCS to the DCG model increased the c-statistics to 0.798 for 1-year and 0.784 for 2–5-year mortality. Conclusions The DCG model showed slightly better performance than the eight-scale model in predicting 1-year mortality, but the two models showed similar performance for 2–5-year mortality. Health self-report may add health risk information in addition to age, gender, and diagnosis for predicting longer-term mortality. PMID:17362210

Mouth opening in patients irradiated for head and neck cancer: a prospective repeated measures study.

PubMed

Kamstra, J I; Dijkstra, P U; van Leeuwen, M; Roodenburg, J L N; Langendijk, J A

2015-05-01

Aims of this prospective cohort study were (1) to analyze the course of mouth opening up to 48months post-radiotherapy (RT), (2) to assess risk factors predicting decrease in mouth opening, and (3) to develop a multivariable prediction model for change in mouth opening in a large sample of patients irradiated for head and neck cancer. Mouth opening was measured prior to RT (baseline) and at 6, 12, 18, 24, 36, and 48months post-RT. The primary outcome variable was mouth opening. Potential risk factors were entered into a linear mixed model analysis (manual backward-stepwise elimination) to create a multivariable prediction model. The interaction terms between time and risk factors that were significantly related to mouth opening were explored. The study population consisted of 641 patients: 70.4% male, mean age at baseline 62.3years (sd 12.5). Primary tumors were predominantly located in the oro- and nasopharynx (25.3%) and oral cavity (20.6%). Mean mouth opening at baseline was 38.7mm (sd 10.8). Six months post-RT, mean mouth opening was smallest, 36.7mm (sd 10.0). In the linear mixed model analysis, mouth opening was statistically predicted by the location of the tumor, natural logarithm of time post-RT in months (Ln (months)), gender, baseline mouth opening, and baseline age. All main effects interacted with Ln (months). The mean mouth opening decreased slightly over time. Mouth opening was predicted by tumor location, time, gender, baseline mouth opening, and age. The model can be used to predict mouth opening. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assessment of Poisson, probit and linear models for genetic analysis of presence and number of black spots in Corriedale sheep.

PubMed

Peñagaricano, F; Urioste, J I; Naya, H; de los Campos, G; Gianola, D

2011-04-01

Black skin spots are associated with pigmented fibres in wool, an important quality fault. Our objective was to assess alternative models for genetic analysis of presence (BINBS) and number (NUMBS) of black spots in Corriedale sheep. During 2002-08, 5624 records from 2839 animals in two flocks, aged 1 through 6 years, were taken at shearing. Four models were considered: linear and probit for BINBS and linear and Poisson for NUMBS. All models included flock-year and age as fixed effects and animal and permanent environmental as random effects. Models were fitted to the whole data set and were also compared based on their predictive ability in cross-validation. Estimates of heritability ranged from 0.154 to 0.230 for BINBS and 0.269 to 0.474 for NUMBS. For BINBS, the probit model fitted slightly better to the data than the linear model. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For NUMBS, the Poisson model, with a residual term to account for overdispersion, performed better than the linear model in goodness of fit and predictive ability. Predictions of random effects from the Poisson model were more strongly correlated with those from BINBS models than those from the linear model. Overall, the use of probit or linear models for BINBS and of a Poisson model with a residual for NUMBS seems a reasonable choice for genetic selection purposes in Corriedale sheep. © 2010 Blackwell Verlag GmbH.

Increased brain-predicted aging in treated HIV disease

PubMed Central

Underwood, Jonathan; Caan, Matthan W.A.; De Francesco, Davide; van Zoest, Rosan A.; Leech, Robert; Wit, Ferdinand W.N.M.; Portegies, Peter; Geurtsen, Gert J.; Schmand, Ben A.; Schim van der Loeff, Maarten F.; Franceschi, Claudio; Sabin, Caroline A.; Majoie, Charles B.L.M.; Winston, Alan; Reiss, Peter; Sharp, David J.

2017-01-01

Objective: To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. Methods: A large sample of virologically suppressed HIV-positive adults (n = 162, age 45–82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18–90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age − chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. Results: HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (−0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Conclusion: Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. PMID:28258081

Increased brain-predicted aging in treated HIV disease.

PubMed

Cole, James H; Underwood, Jonathan; Caan, Matthan W A; De Francesco, Davide; van Zoest, Rosan A; Leech, Robert; Wit, Ferdinand W N M; Portegies, Peter; Geurtsen, Gert J; Schmand, Ben A; Schim van der Loeff, Maarten F; Franceschi, Claudio; Sabin, Caroline A; Majoie, Charles B L M; Winston, Alan; Reiss, Peter; Sharp, David J

2017-04-04

To establish whether HIV disease is associated with abnormal levels of age-related brain atrophy, by estimating apparent brain age using neuroimaging and exploring whether these estimates related to HIV status, age, cognitive performance, and HIV-related clinical parameters. A large sample of virologically suppressed HIV-positive adults (n = 162, age 45-82 years) and highly comparable HIV-negative controls (n = 105) were recruited as part of the Comorbidity in Relation to AIDS (COBRA) collaboration. Using T1-weighted MRI scans, a machine-learning model of healthy brain aging was defined in an independent cohort (n = 2,001, aged 18-90 years). Neuroimaging data from HIV-positive and HIV-negative individuals were then used to estimate brain-predicted age; then brain-predicted age difference (brain-PAD = brain-predicted brain age - chronological age) scores were calculated. Neuropsychological and clinical assessments were also carried out. HIV-positive individuals had greater brain-PAD score (mean ± SD 2.15 ± 7.79 years) compared to HIV-negative individuals (-0.87 ± 8.40 years; b = 3.48, p < 0.01). Increased brain-PAD score was associated with decreased performance in multiple cognitive domains (information processing speed, executive function, memory) and general cognitive performance across all participants. Brain-PAD score was not associated with age, duration of HIV infection, or other HIV-related measures. Increased apparent brain aging, predicted using neuroimaging, was observed in HIV-positive adults, despite effective viral suppression. Furthermore, the magnitude of increased apparent brain aging related to cognitive deficits. However, predicted brain age difference did not correlate with chronological age or duration of HIV infection, suggesting that HIV disease may accentuate rather than accelerate brain aging. Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

Model for predicting peak expiratory flow rate of Nigerian workers in a cement factory in Itori, Ogun State, Nigeria.

PubMed

Ismaila, Salami Olasunkanmi; Akanbi, Olusegun Gabriel; Olaoniye, Wasiu

2015-01-01

The main aim of the study was to propose a model for predicting the peak expiratory flow rate (PEFR) of Nigerian workers in a cement factory. Sixty randomly selected non-smoker and healthy workers (30 in production sections, 30 in the administrative section of the factory) participated in the study. Their physical characteristics and PEFR were measured. Multiple correlations using SPSS version 16.0 were performed on the data. The values of PEFR, using the obtained model, were compared with the measured values using a two-tailed t test. There were positive correlations among age, height and PEFR. A prediction equation for PEFR based on age, height, weight and years of exposure (experience) was obtained with R² = .843 (p < 0.001). The developed model will be useful for the management in determining PEFR of workers in the cement industry for possible medical attention.

A prediction model for cognitive performance in health ageing using diffusion tensor imaging with graph theory.

PubMed

Yun, Ruijuan; Lin, Chung-Chih; Wu, Shuicai; Huang, Chu-Chung; Lin, Ching-Po; Chao, Yi-Ping

2013-01-01

In this study, we employed diffusion tensor imaging (DTI) to construct brain structural network and then derive the connection matrices from 96 healthy elderly subjects. The correlation analysis between these topological properties of network based on graph theory and the Cognitive Abilities Screening Instrument (CASI) index were processed to extract the significant network characteristics. These characteristics were then integrated to estimate the models by various machine-learning algorithms to predict user's cognitive performance. From the results, linear regression model and Gaussian processes model showed presented better abilities with lower mean absolute errors of 5.8120 and 6.25 to predict the cognitive performance respectively. Moreover, these extracted topological properties of brain structural network derived from DTI also could be regarded as the bio-signatures for further evaluation of brain degeneration in healthy aged and early diagnosis of mild cognitive impairment (MCI).

Can we predict age at natural menopause using ovarian reserve tests or mother's age at menopause? A systematic literature review.

PubMed

Depmann, Martine; Broer, Simone L; van der Schouw, Yvonne T; Tehrani, Fahimeh R; Eijkemans, Marinus J; Mol, Ben W; Broekmans, Frank J

2016-02-01

This review aimed to appraise data on prediction of age at natural menopause (ANM) based on antimüllerian hormone (AMH), antral follicle count (AFC), and mother's ANM to evaluate clinical usefulness and to identify directions for further research. We conducted three systematic reviews of the literature to identify studies of menopause prediction based on AMH, AFC, or mother's ANM, corrected for baseline age. Six studies selected in the search for AMH all consistently demonstrated AMH as being capable of predicting ANM (hazard ratio, 5.6-9.2). The sole study reporting on mother's ANM indicated that AMH was capable of predicting ANM (hazard ratio, 9.1-9.3). Two studies provided analyses of AFC and yielded conflicting results, making this marker less strong. AMH is currently the most promising marker for ANM prediction. The predictive capacity of mother's ANM demonstrated in a single study makes this marker a promising contributor to AMH for menopause prediction. Models, however, do not predict the extremes of menopause age very well and have wide prediction interval. These markers clearly need improvement before they can be used for individual prediction of menopause in the clinical setting. Moreover, potential limitations for such use include variations in AMH assays used and a lack of correction for factors or diseases affecting AMH levels or ANM. Future studies should include women of a broad age range (irrespective of cycle regularity) and should base predictions on repeated AMH measurements. Furthermore, currently unknown candidate predictors need to be identified.

A Model-based Prognostics Methodology for Electrolytic Capacitors Based on Electrical Overstress Accelerated Aging

NASA Technical Reports Server (NTRS)

Celaya, Jose; Kulkarni, Chetan; Biswas, Gautam; Saha, Sankalita; Goebel, Kai

2011-01-01

A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.

Towards A Model-Based Prognostics Methodology for Electrolytic Capacitors: A Case Study Based on Electrical Overstress Accelerated Aging

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Kulkarni, Chetan S.; Biswas, Gautam; Goebel, Kai

2012-01-01

A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.

Variation in probability of first reproduction of Weddell seals.

PubMed

Hadley, Gillian L; Rotella, Jay J; Garrott, Robert A; Nichols, James D

2006-09-01

1. For many species, when to begin reproduction is an important life-history decision that varies by individual and can have substantial implications for lifetime reproductive success and fitness. 2. We estimated age-specific probabilities of first-time breeding and modelled variation in these rates to determine age at first reproduction and understand why it varies in a population of Weddell seals in Erebus Bay, Antarctica. We used multistate mark-recapture modelling methods and encounter histories of 4965 known-age female seals to test predictions about age-related variation in probability of first reproduction and the effects of annual variation, cohort and population density. 3. Mean age at first reproduction in this southerly located study population (7.62 years of age, SD=1.71) was greater than age at first reproduction for a Weddell seal population at a more northerly and typical latitude for breeding Weddell seals (mean=4-5 years of age). This difference suggests that age at first reproduction may be influenced by whether a population inhabits the core or periphery of its range. 4. Age at first reproduction varied from 4 to 14 years, but there was no age by which all seals recruited to the breeding population, suggesting that individual heterogeneity exists among females in this population. 5. In the best model, the probability of breeding for the first time varied by age and year, and the amount of annual variation varied with age (average variance ratio for age-specific rates=4.3%). 6. Our results affirmed the predictions of life-history theory that age at first reproduction in long-lived mammals will be sensitive to environmental variation. In terms of life-history evolution, this variability suggests that Weddell seals display flexibility in age at first reproduction in order to maximize reproductive output under varying environmental conditions. Future analyses will attempt to test predictions regarding relationships between environmental covariates and annual variation in age at first reproduction and evaluate the relationship between age at first reproduction and lifetime reproductive success.

Artificial neural networks: Predicting head CT findings in elderly patients presenting with minor head injury after a fall.

PubMed

Dusenberry, Michael W; Brown, Charles K; Brewer, Kori L

2017-02-01

To construct an artificial neural network (ANN) model that can predict the presence of acute CT findings with both high sensitivity and high specificity when applied to the population of patients≥age 65years who have incurred minor head injury after a fall. An ANN was created in the Python programming language using a population of 514 patients ≥ age 65 years presenting to the ED with minor head injury after a fall. The patient dataset was divided into three parts: 60% for "training", 20% for "cross validation", and 20% for "testing". Sensitivity, specificity, positive and negative predictive values, and accuracy were determined by comparing the model's predictions to the actual correct answers for each patient. On the "cross validation" data, the model attained a sensitivity ("recall") of 100.00%, specificity of 78.95%, PPV ("precision") of 78.95%, NPV of 100.00%, and accuracy of 88.24% in detecting the presence of positive head CTs. On the "test" data, the model attained a sensitivity of 97.78%, specificity of 89.47%, PPV of 88.00%, NPV of 98.08%, and accuracy of 93.14% in detecting the presence of positive head CTs. ANNs show great potential for predicting CT findings in the population of patients ≥ 65 years of age presenting with minor head injury after a fall. As a good first step, the ANN showed comparable sensitivity, predictive values, and accuracy, with a much higher specificity than the existing decision rules in clinical usage for predicting head CTs with acute intracranial findings. Copyright © 2016 Elsevier Inc. All rights reserved.

Modeling the Etiology of Adolescent Substance Use: A Test of the Social Development Model

PubMed Central

Catalano, Richard F.; Kosterman, Rick; Hawkins, J. David; Newcomb, Michael D.; Abbott, Robert D.

2007-01-01

The social development model is a general theory of human behavior that seeks to explain antisocial behaviors through specification of predictive developmental relationships. It incorporates the effects of empirical predictors (“risk factors” and “protective factors”) for antisocial behavior and attempts to synthesize the most strongly supported propositions of control theory, social learning theory, and differential association theory. This article examines the power of social development model constructs measured at ages 9 to 10 and 13 to 14 to predict drug use at ages 17 to 18. The sample of 590 is from the longitudinal panel of the Seattle Social Development Project, which in 1985 sampled fifth grade students from high crime neighborhoods in Seattle, Washington. Structural equation modeling techniques were used to examine the fit of the model to the data. Although all but one path coefficient were significant and in the expected direction, the model did not fit the data as well as expected (CFI=.87). We next specified second-order factors for each path to capture the substantial common variance in the constructs' opportunities, involvement, and rewards. This model fit the data well (CFI=.90). We conclude that the social development model provides an acceptable fit to predict drug use at ages 17 to 18. Implications for the temporal nature of key constructs and for prevention are discussed. PMID:17848978

Systematic review of prediction models for delirium in the older adult inpatient.

PubMed

Lindroth, Heidi; Bratzke, Lisa; Purvis, Suzanne; Brown, Roger; Coburn, Mark; Mrkobrada, Marko; Chan, Matthew T V; Davis, Daniel H J; Pandharipande, Pratik; Carlsson, Cynthia M; Sanders, Robert D

2018-04-28

To identify existing prognostic delirium prediction models and evaluate their validity and statistical methodology in the older adult (≥60 years) acute hospital population. Systematic review. PubMed, CINAHL, PsychINFO, SocINFO, Cochrane, Web of Science and Embase were searched from 1 January 1990 to 31 December 2016. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses and CHARMS Statement guided protocol development. age >60 years, inpatient, developed/validated a prognostic delirium prediction model. alcohol-related delirium, sample size ≤50. The primary performance measures were calibration and discrimination statistics. Two authors independently conducted search and extracted data. The synthesis of data was done by the first author. Disagreement was resolved by the mentoring author. The initial search resulted in 7,502 studies. Following full-text review of 192 studies, 33 were excluded based on age criteria (<60 years) and 27 met the defined criteria. Twenty-three delirium prediction models were identified, 14 were externally validated and 3 were internally validated. The following populations were represented: 11 medical, 3 medical/surgical and 13 surgical. The assessment of delirium was often non-systematic, resulting in varied incidence. Fourteen models were externally validated with an area under the receiver operating curve range from 0.52 to 0.94. Limitations in design, data collection methods and model metric reporting statistics were identified. Delirium prediction models for older adults show variable and typically inadequate predictive capabilities. Our review highlights the need for development of robust models to predict delirium in older inpatients. We provide recommendations for the development of such models. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Modeling and measuring extravascular hemoglobin: aging contusions

NASA Astrophysics Data System (ADS)

Lines, Collin; Kim, Oleg; Duffy, Susan; Alber, Mark; Crawford, Gregory P.

2011-07-01

Medical expertise is frequently elicited to aid in determining the age and the cause of the trauma or injury. Child protection and law enforcement frequently rely on the physical assessment of the trauma which involves delineating intentional from unintentional types of trauma. Recent studies have shown that current methods to assess the age of traumatic injuries are highly inaccurate and do not give reasonable predictions. Hemoglobin is one of the strongest chromophores in human tissues. Transport of hemoglobin and its breakdown products in tissue determines the spectrophotometric characteristics of the skin and its variations in time. Therefore, measurements of diffuse reflective spectra of the skin allow noninvasive screening. This paper reviews potential transmission and diffusive reflection spectroscopy based techniques and predictive and quantitative modeling methods assisting in efficient retrieval of the age of extravascular contusions. This paper then presents a novel Monte Carlo technique for 3D photon tracking and blood transport model. In future studies, clinically obtained spectra will be used to validate the model as well as fine-tune coefficients for absorption. It is the goal of this study to develop a model that would allow a non-invasive, accurate determination of the age of a bruise.

Development and Validation of an Older Occupant Finite Element Model of a Mid-Sized Male for Investigation of Age-related Injury Risk.

PubMed

Schoell, Samantha L; Weaver, Ashley A; Urban, Jillian E; Jones, Derek A; Stitzel, Joel D; Hwang, Eunjoo; Reed, Matthew P; Rupp, Jonathan D; Hu, Jingwen

2015-11-01

The aging population is a growing concern as the increased fragility and frailty of the elderly results in an elevated incidence of injury as well as an increased risk of mortality and morbidity. To assess elderly injury risk, age-specific computational models can be developed to directly calculate biomechanical metrics for injury. The first objective was to develop an older occupant Global Human Body Models Consortium (GHBMC) average male model (M50) representative of a 65 year old (YO) and to perform regional validation tests to investigate predicted fractures and injury severity with age. Development of the GHBMC M50 65 YO model involved implementing geometric, cortical thickness, and material property changes with age. Regional validation tests included a chest impact, a lateral impact, a shoulder impact, a thoracoabdominal impact, an abdominal bar impact, a pelvic impact, and a lateral sled test. The second objective was to investigate age-related injury risks by performing a frontal US NCAP simulation test with the GHBMC M50 65 YO and the GHBMC M50 v4.2 models. Simulation results were compared to the GHBMC M50 v4.2 to evaluate the effect of age on occupant response and risk for head injury, neck injury, thoracic injury, and lower extremity injury. Overall, the GHBMC M50 65 YO model predicted higher probabilities of AIS 3+ injury for the head and thorax.

A survival model for individual shortleaf pine trees in even-aged natural stands

Treesearch

Thomas B. Lynch; Michael M. Huebschmann; Paul A. Murphy

2000-01-01

A model was developed that predicts the probability of survival for individual shortleaf pine (Pinus echinata Mill.) trees growing in even-aged natural stands. Data for model development were obtained from the first two measurements of permanently established plots located in naturally occurring shortleaf pine forests on the Ouachita and Ozark...

Personality Traits Predict the Developmental Course of Externalizing: A Four-wave Longitudinal Study Spanning Age 17 to Age 29

PubMed Central

Walton, Kate E.; Krueger, Robert F.; Elkins, Irene; D’Accordo, Cassandra; McGue, Matt; Iacono, William G.

2016-01-01

Objective The objective of the present study was to determine whether and how personality predicts the developmental course of externalizing problems, including antisocial behavior and substance dependence. Method In a large population-based longitudinal study (N=1252), the 11 personality traits assessed by the Multidimensional Personality Questionnaire were measured at age 17, and DSM diagnoses of adult antisocial behavior, alcohol dependence, and drug dependence were obtained at ages 17, 20, 24, and 29. We fit a quadratic multiple indicator latent growth model where the three diagnoses loaded onto an externalizing factor. Results This model fit the data well, and externalizing increased until it started to decline at age 24. High aggression and low control were the most significant predictors of the development of externalizing, with aggression playing a significant role in the development of externalizing across the 12-year time span, and control predicting the development from age 17 to 24. Conclusions The findings highlight the importance of considering the developmental course of externalizing in the context of personality and suggest that the specific personality traits of aggression and control might be targeted in externalizing prevention and intervention programs. PMID:26808279

Personality Traits Predict the Developmental Course of Externalizing: A Four-Wave Longitudinal Study Spanning Age 17 to Age 29.

PubMed

Walton, Kate E; Krueger, Robert F; Elkins, Irene; D'Accordo, Cassandra; McGue, Matt; Iacono, William G

2017-06-01

The objective of the present study was to determine whether and how personality predicts the developmental course of externalizing problems, including antisocial behavior and substance dependence. In a large, population-based longitudinal study (N = 1,252), the 11 personality traits assessed by the Multidimensional Personality Questionnaire were measured at age 17, and DSM diagnoses of adult antisocial behavior, alcohol dependence, and drug dependence were obtained at ages 17, 20, 24, and 29. We fit a quadratic multiple indicator latent growth model where the three diagnoses loaded onto an externalizing factor. This model fit the data well, and externalizing increased until it started to decline at age 24. High aggression and low control were the most significant predictors of the development of externalizing, with aggression playing a significant role in the development of externalizing across the 12-year time span, and control predicting the development from age 17 to 24. The findings highlight the importance of considering the developmental course of externalizing in the context of personality and suggest that the specific personality traits of aggression and control might be targeted in externalizing prevention and intervention programs. © 2016 Wiley Periodicals, Inc.

Novel prediction model of renal function after nephrectomy from automated renal volumetry with preoperative multidetector computed tomography (MDCT).

PubMed

Isotani, Shuji; Shimoyama, Hirofumi; Yokota, Isao; Noma, Yasuhiro; Kitamura, Kousuke; China, Toshiyuki; Saito, Keisuke; Hisasue, Shin-ichi; Ide, Hisamitsu; Muto, Satoru; Yamaguchi, Raizo; Ukimura, Osamu; Gill, Inderbir S; Horie, Shigeo

2015-10-01

The predictive model of postoperative renal function may impact on planning nephrectomy. To develop the novel predictive model using combination of clinical indices with computer volumetry to measure the preserved renal cortex volume (RCV) using multidetector computed tomography (MDCT), and to prospectively validate performance of the model. Total 60 patients undergoing radical nephrectomy from 2011 to 2013 participated, including a development cohort of 39 patients and an external validation cohort of 21 patients. RCV was calculated by voxel count using software (Vincent, FUJIFILM). Renal function before and after radical nephrectomy was assessed via the estimated glomerular filtration rate (eGFR). Factors affecting postoperative eGFR were examined by regression analysis to develop the novel model for predicting postoperative eGFR with a backward elimination method. The predictive model was externally validated and the performance of the model was compared with that of the previously reported models. The postoperative eGFR value was associated with age, preoperative eGFR, preserved renal parenchymal volume (RPV), preserved RCV, % of RPV alteration, and % of RCV alteration (p < 0.01). The significant correlated variables for %eGFR alteration were %RCV preservation (r = 0.58, p < 0.01) and %RPV preservation (r = 0.54, p < 0.01). We developed our regression model as follows: postoperative eGFR = 57.87 - 0.55(age) - 15.01(body surface area) + 0.30(preoperative eGFR) + 52.92(%RCV preservation). Strong correlation was seen between postoperative eGFR and the calculated estimation model (r = 0.83; p < 0.001). The external validation cohort (n = 21) showed our model outperformed previously reported models. Combining MDCT renal volumetry and clinical indices might yield an important tool for predicting postoperative renal function.

Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

PubMed Central

2013-01-01

Background The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population. Methods We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732–0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0. Conclusion ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population. PMID:23902963

The use of regression tree analysis for predicting the functional outcome following traumatic spinal cord injury.

PubMed

Facchinello, Yann; Beauséjour, Marie; Richard-Denis, Andreane; Thompson, Cynthia; Mac-Thiong, Jean-Marc

2017-10-25

Predicting the long-term functional outcome following traumatic spinal cord injury is needed to adapt medical strategies and to plan an optimized rehabilitation. This study investigates the use of regression tree for the development of predictive models based on acute clinical and demographic predictors. This prospective study was performed on 172 patients hospitalized following traumatic spinal cord injury. Functional outcome was quantified using the Spinal Cord Independence Measure collected within the first-year post injury. Age, delay prior to surgery and Injury Severity Score were considered as continuous predictors while energy of injury, trauma mechanisms, neurological level of injury, injury severity, occurrence of early spasticity, urinary tract infection, pressure ulcer and pneumonia were coded as categorical inputs. A simplified model was built using only injury severity, neurological level, energy and age as predictor and was compared to a more complex model considering all 11 predictors mentioned above The models built using 4 and 11 predictors were found to explain 51.4% and 62.3% of the variance of the Spinal Cord Independence Measure total score after validation, respectively. The severity of the neurological deficit at admission was found to be the most important predictor. Other important predictors were the Injury Severity Score, age, neurological level and delay prior to surgery. Regression trees offer promising performances for predicting the functional outcome after a traumatic spinal cord injury. It could help to determine the number and type of predictors leading to a prediction model of the functional outcome that can be used clinically in the future.

Multivariate Cholesky models of human female fertility patterns in the NLSY.

PubMed

Rodgers, Joseph Lee; Bard, David E; Miller, Warren B

2007-03-01

Substantial evidence now exists that variables measuring or correlated with human fertility outcomes have a heritable component. In this study, we define a series of age-sequenced fertility variables, and fit multivariate models to account for underlying shared genetic and environmental sources of variance. We make predictions based on a theory developed by Udry [(1996) Biosocial models of low-fertility societies. In: Casterline, JB, Lee RD, Foote KA (eds) Fertility in the United States: new patterns, new theories. The Population Council, New York] suggesting that biological/genetic motivations can be more easily realized and measured in settings in which fertility choices are available. Udry's theory, along with principles from molecular genetics and certain tenets of life history theory, allow us to make specific predictions about biometrical patterns across age. Consistent with predictions, our results suggest that there are different sources of genetic influence on fertility variance at early compared to later ages, but that there is only one source of shared environmental influence that occurs at early ages. These patterns are suggestive of the types of gene-gene and gene-environment interactions for which we must account to better understand individual differences in fertility outcomes.

Development of a child head analytical dynamic model considering cranial nonuniform thickness and curvature - Applying to children aged 0-1 years old.

PubMed

Li, Zhigang; Ji, Cheng; Wang, Lishu

2018-07-01

Although analytical models have been used to quickly predict head response under impact condition, the existing models generally took the head as regular shell with uniform thickness which cannot account for the actual head geometry with varied cranial thickness and curvature at different locations. The objective of this study is to develop and validate an analytical model incorporating actual cranial thickness and curvature for child aged 0-1YO and investigate their effects on child head dynamic responses at different head locations. To develop the new analytical model, the child head was simplified into an irregular fluid-filled shell with non-uniform thickness and the cranial thickness and curvature at different locations were automatically obtained from CT scans using a procedure developed in this study. The implicit equation of maximum impact force was derived as a function of elastic modulus, thickness and radius of curvature of cranium. The proposed analytical model are compared with cadaver test data of children aged 0-1 years old and it is shown to be accurate in predicting head injury metrics. According to this model, obvious difference in injury metrics were observed among subjects with the same age, but different cranial thickness and curvature; and the injury metrics at forehead location are significant higher than those at other locations due to large thickness it owns. The proposed model shows good biofidelity and can be used in quickly predicting the dynamics response at any location of head for child younger than 1 YO. Copyright © 2018 Elsevier B.V. All rights reserved.

A Matrix Transition Model for an Uneven-Aged, Oak-Hickory Forest in the Missouri Ozark Highlands

Treesearch

James R. Lootens; David R. Larsen; Edward F. Loewenstein

1999-01-01

We present a matrix growth model for an uneven-aged, oak-hickory forest in the Ozark Highlands of Missouri. The model was developed to predict ingrowth, growth of surviving trees, and mortality by diameter class for a five-year period. Tree removal from management activities is accounted for in the model. We evaluated a progression of models from a static, fixed-...

A matrix transition model for an uneven-aged, oak-hickory forest in the Missouri ozark highlands

Treesearch

James R. Lootens; David R. Larsen; Edward F. Loewenstein

1999-01-01

We presented a matrix growth model for an uneven-aged, oak-hickory forest in the Ozark Highlands of Missouri. The model was developed to predict ingrowth, growth of surviving trees, and mortality by diameter class for a five-year period. Tree removal from management activities is accounted for in the model. We evaluated a progression of models from a static, fixed...

Integration of Harvest and Time-to-Event Data Used to Estimate Demographic Parameters for White-tailed Deer

NASA Astrophysics Data System (ADS)

Norton, Andrew S.

An integral component of managing game species is an understanding of population dynamics and relative abundance. Harvest data are frequently used to estimate abundance of white-tailed deer. Unless harvest age-structure is representative of the population age-structure and harvest vulnerability remains constant from year to year, these data alone are of limited value. Additional model structure and auxiliary information has accommodated this shortcoming. Specifically, integrated age-at-harvest (AAH) state-space population models can formally combine multiple sources of data, and regularization via hierarchical model structure can increase flexibility of model parameters. I collected known fates data, which I evaluated and used to inform trends in survival parameters for an integrated AAH model. I used temperature and snow depth covariates to predict survival outside of the hunting season, and opening weekend temperature and percent of corn harvest covariates to predict hunting season survival. When auxiliary empirical data were unavailable for the AAH model, moderately informative priors provided sufficient information for convergence and parameter estimates. The AAH model was most sensitive to errors in initial abundance, but this error was calibrated after 3 years. Among vital rates, the AAH model was most sensitive to reporting rates (percentage of mortality during the hunting season related to harvest). The AAH model, using only harvest data, was able to track changing abundance trends due to changes in survival rates even when prior models did not inform these changes (i.e. prior models were constant when truth varied). I also compared AAH model results with estimates from the Wisconsin Department of Natural Resources (WIDNR). Trends in abundance estimates from both models were similar, although AAH model predictions were systematically higher than WIDNR estimates in the East study area. When I incorporated auxiliary information (i.e. integrated AAH model) about survival outside the hunting season from known fates data, predicted trends appeared more closely related to what was expected. Disagreements between the AAH model and WIDNR estimates in the East were likely related to biased predictions for reporting and survival rates from the AAH model.

Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

PubMed Central

Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

2013-01-01

Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eos

A New Scoring System to Predict the Risk for High-risk Adenoma and Comparison of Existing Risk Calculators.

PubMed

Murchie, Brent; Tandon, Kanwarpreet; Hakim, Seifeldin; Shah, Kinchit; O'Rourke, Colin; Castro, Fernando J

2017-04-01

Colorectal cancer (CRC) screening guidelines likely over-generalizes CRC risk, 35% of Americans are not up to date with screening, and there is growing incidence of CRC in younger patients. We developed a practical prediction model for high-risk colon adenomas in an average-risk population, including an expanded definition of high-risk polyps (≥3 nonadvanced adenomas), exposing higher than average-risk patients. We also compared results with previously created calculators. Patients aged 40 to 59 years, undergoing first-time average-risk screening or diagnostic colonoscopies were evaluated. Risk calculators for advanced adenomas and high-risk adenomas were created based on age, body mass index, sex, race, and smoking history. Previously established calculators with similar risk factors were selected for comparison of concordance statistic (c-statistic) and external validation. A total of 5063 patients were included. Advanced adenomas, and high-risk adenomas were seen in 5.7% and 7.4% of the patient population, respectively. The c-statistic for our calculator was 0.639 for the prediction of advanced adenomas, and 0.650 for high-risk adenomas. When applied to our population, all previous models had lower c-statistic results although one performed similarly. Our model compares favorably to previously established prediction models. Age and body mass index were used as continuous variables, likely improving the c-statistic. It also reports absolute predictive probabilities of advanced and high-risk polyps, allowing for more individualized risk assessment of CRC.

The Enduring Predictive Significance of Early Maternal Sensitivity: Social and Academic Competence through Age 32 Years

ERIC Educational Resources Information Center

Raby, K. Lee; Roisman, Glenn I.; Fraley, R. Chris; Simpson, Jeffry A.

2015-01-01

This study leveraged data from the Minnesota Longitudinal Study of Risk and Adaptation (N = 243) to investigate the predictive significance of maternal sensitivity during the first 3 years of life for social and academic competence through age 32 years. Structural model comparisons replicated previous findings that early maternal sensitivity…

Childhood Temperament and Family Environment as Predictors of Internalizing and Externalizing Trajectories from Ages 5 to 17

ERIC Educational Resources Information Center

Leve, Leslie D.; Kim, Hyoun K.; Pears, Katherine C.

2005-01-01

Childhood temperament and family environment have been shown to predict internalizing and externalizing behavior; however, less is known about how temperament and family environment interact to predict changes in problem behavior. We conducted latent growth curve modeling on a sample assessed at ages 5, 7, 10, 14, and 17 (N = 337). Externalizing…

The Interplay of Maternal Sensitivity and Toddler Engagement of Mother in Predicting Self-Regulation

ERIC Educational Resources Information Center

Ispa, Jean M.; Su-Russell, Chang; Palermo, Francisco; Carlo, Gustavo

2017-01-01

Using data from the Early Head Start Research and Evaluation Project, a cross-lag mediation model was tested to examine longitudinal relations among low-income mothers' sensitivity; toddlers' engagement of their mothers; and toddler's self-regulation at ages 1, 2, and 3 years (N = 2,958). Age 1 maternal sensitivity predicted self-regulation at…

Age-Dependent and Lineage-Dependent Speciation and Extinction in the Imbalance of Phylogenetic Trees.

PubMed

Holman, Eric W

2017-11-01

It is known that phylogenetic trees are more imbalanced than expected from a birth-death model with constant rates of speciation and extinction, and also that imbalance can be better fit by allowing the rate of speciation to decrease as the age of the parent species increases. If imbalance is measured in more detail, at nodes within trees as a function of the number of species descended from the nodes, age-dependent models predict levels of imbalance comparable to real trees for small numbers of descendent species, but predicted imbalance approaches an asymptote not found in real trees as the number of descendent species becomes large. Age-dependence must therefore be complemented by another process such as inheritance of different rates along different lineages, which is known to predict insufficient imbalance at nodes with few descendent species, but can predict increasing imbalance with increasing numbers of descendent species. [Crump-Mode-Jagers process; diversification; macroevolution; taxon sampling; tree of life.]. © The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A scoring system to predict breast cancer mortality at 5 and 10 years.

PubMed

Paredes-Aracil, Esther; Palazón-Bru, Antonio; Folgado-de la Rosa, David Manuel; Ots-Gutiérrez, José Ramón; Compañ-Rosique, Antonio Fernando; Gil-Guillén, Vicente Francisco

2017-03-24

Although predictive models exist for mortality in breast cancer (BC) (generally all cause-mortality), they are not applicable to all patients and their statistical methodology is not the most powerful to develop a predictive model. Consequently, we developed a predictive model specific for BC mortality at 5 and 10 years resolving the above issues. This cohort study included 287 patients diagnosed with BC in a Spanish region in 2003-2016. time-to-BC death. Secondary variables: age, personal history of breast surgery, personal history of any cancer/BC, premenopause, postmenopause, grade, estrogen receptor, progesterone receptor, c-erbB2, TNM stage, multicentricity/multifocality, diagnosis and treatment. A points system was constructed to predict BC mortality at 5 and 10 years. The model was internally validated by bootstrapping. The points system was integrated into a mobile application for Android. Mean follow-up was 8.6 ± 3.5 years and 55 patients died of BC. The points system included age, personal history of BC, grade, TNM stage and multicentricity. Validation was satisfactory, in both discrimination and calibration. In conclusion, we constructed and internally validated a scoring system for predicting BC mortality at 5 and 10 years. External validation studies are needed for its use in other geographical areas.

Bronchopulmonary Dysplasia and Perinatal Characteristics Predict 1-Year Respiratory Outcomes in Newborns Born at Extremely Low Gestational Age: A Prospective Cohort Study.

PubMed

Keller, Roberta L; Feng, Rui; DeMauro, Sara B; Ferkol, Thomas; Hardie, William; Rogers, Elizabeth E; Stevens, Timothy P; Voynow, Judith A; Bellamy, Scarlett L; Shaw, Pamela A; Moore, Paul E

2017-08-01

To assess the utility of clinical predictors of persistent respiratory morbidity in extremely low gestational age newborns (ELGANs). We enrolled ELGANs (<29 weeks' gestation) at ≤7 postnatal days and collected antenatal and neonatal clinical data through 36 weeks' postmenstrual age. We surveyed caregivers at 3, 6, 9, and 12 months' corrected age to identify postdischarge respiratory morbidity, defined as hospitalization, home support (oxygen, tracheostomy, ventilation), medications, or symptoms (cough/wheeze). Infants were classified as having postprematurity respiratory disease (PRD, the primary study outcome) if respiratory morbidity persisted over ≥2 questionnaires. Infants were classified with severe respiratory morbidity if there were multiple hospitalizations, exposure to systemic steroids or pulmonary vasodilators, home oxygen after 3 months or mechanical ventilation, or symptoms despite inhaled corticosteroids. Mixed-effects models generated with data available at 1 day (perinatal) and 36 weeks' postmenstrual age were assessed for predictive accuracy. Of 724 infants (918 ± 234 g, 26.7 ± 1.4 weeks' gestational age) classified for the primary outcome, 68.6% had PRD; 245 of 704 (34.8%) were classified as severe. Male sex, intrauterine growth restriction, maternal smoking, race/ethnicity, intubation at birth, and public insurance were retained in perinatal and 36-week models for both PRD and respiratory morbidity severity. The perinatal model accurately predicted PRD (c-statistic 0.858). Neither the 36-week model nor the addition of bronchopulmonary dysplasia to the perinatal model improved accuracy (0.856, 0.860); c-statistic for BPD alone was 0.907. Both bronchopulmonary dysplasia and perinatal clinical data accurately identify ELGANs at risk for persistent and severe respiratory morbidity at 1 year. ClinicalTrials.gov: NCT01435187. Copyright © 2017 Elsevier Inc. All rights reserved.

Enteric disease episodes and the risk of acquiring a future sexually transmitted infection: a prediction model in Montreal residents.

PubMed

Caron, Melissa; Allard, Robert; Bédard, Lucie; Latreille, Jérôme; Buckeridge, David L

2016-11-01

The sexual transmission of enteric diseases poses an important public health challenge. We aimed to build a prediction model capable of identifying individuals with a reported enteric disease who could be at risk of acquiring future sexually transmitted infections (STIs). Passive surveillance data on Montreal residents with at least 1 enteric disease report was used to construct the prediction model. Cases were defined as all subjects with at least 1 STI report following their initial enteric disease episode. A final logistic regression prediction model was chosen using forward stepwise selection. The prediction model with the greatest validity included age, sex, residential location, number of STI episodes experienced prior to the first enteric disease episode, type of enteric disease acquired, and an interaction term between age and male sex. This model had an area under the curve of 0.77 and had acceptable calibration. A coordinated public health response to the sexual transmission of enteric diseases requires that a distinction be made between cases of enteric diseases transmitted through sexual activity from those transmitted through contaminated food or water. A prediction model can aid public health officials in identifying individuals who may have a higher risk of sexually acquiring a reportable disease. Once identified, these individuals could receive specialized intervention to prevent future infection. The information produced from a prediction model capable of identifying higher risk individuals can be used to guide efforts in investigating and controlling reported cases of enteric diseases and STIs. © 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.

Association of Coronary Artery Calcification with Estimated Coronary Heart Disease Risk from Prediction Models in a Community-Based Sample of Japanese Men: The Shiga Epidemiological Study of Subclinical Atherosclerosis (SESSA).

PubMed

Fujiyoshi, Akira; Arima, Hisatomi; Tanaka-Mizuno, Sachiko; Hisamatsu, Takahashi; Kadowaki, Sayaka; Kadota, Aya; Zaid, Maryam; Sekikawa, Akira; Yamamoto, Takashi; Horie, Minoru; Miura, Katsuyuki; Ueshima, Hirotsugu

2017-12-05

The clinical significance of coronary artery calcification (CAC) is not fully determined in general East Asian populations where background coronary heart disease (CHD) is less common than in USA/Western countries. We cross-sectionally assessed the association between CAC and estimated CHD risk as well as each major risk factor in general Japanese men. Participants were 996 randomly selected Japanese men aged 40-79 y, free of stroke, myocardial infarction, or revascularization. We examined an independent relationship between each risk factor used in prediction models and CAC score ≥100 by logistic regression. We then divided the participants into quintiles of estimated CHD risk per prediction model to calculate odds ratio of having CAC score ≥100. Receiver operating characteristic curve and c-index were used to examine discriminative ability of prevalent CAC for each prediction model. Age, smoking status, and systolic blood pressure were significantly associated with CAC score ≥100 in the multivariable analysis. The odds of having CAC score ≥100 were higher for those in higher quintiles in all prediction models (p-values for trend across quintiles ＜0.0001 for all models). All prediction models showed fair and similar discriminative abilities to detect CAC score ≥100, with similar c-statistics (around 0.70). In a community-based sample of Japanese men free of CHD and stroke, CAC score ≥100 was significantly associated with higher estimated CHD risk by prediction models. This finding supports the potential utility of CAC as a biomarker for CHD in a general Japanese male population.

Poverty and behavior problems trajectories from 1.5 to 8 years of age: Is the gap widening between poor and non-poor children?

PubMed

Mazza, Julia Rachel S E; Boivin, Michel; Tremblay, Richard E; Michel, Gregory; Salla, Julie; Lambert, Jean; Zunzunegui, Maria Victoria; Côté, Sylvana M

2016-08-01

Poverty has been associated with high levels of behavior problems across childhood, yet patterns of associations over time remain understudied. This study aims: (a) to examine whether poverty predicts changes in behavior problems between 1.5 and 8 years of age; (b) to estimate potential selection bias for the observed associations. We used the 1998-2006 waves of the Quebec Longitudinal Study of Child Development (N = 2120). Main outcomes were maternal ratings of hyperactivity, opposition and physical aggression from 1.5 to 8 years of age. Linear mixed-effects models were used to assess the longitudinal association between poverty and behavior problems. Models were re-estimated adjusting for wave nonresponse and using multiple imputation to account for attrition. Poverty predicted higher levels of behavior problems between 1.5 and 8 years of age. Poverty predicted hyperactivity and opposition in a time dependent manner. Hyperactivity [Bpoverty*age = 0.052; CI 95 % (0.002; 0.101)] and opposition [Bpoverty*age = 0.049; CI 95 % (0.018; 0.079)] increased at a faster rate up to age 5 years, and then decreased at a slower rate for poor than non-poor children. Physical aggression decreased at a steady rate over time for all children [Bpoverty*age = -0.030; p = 0.064). Estimates remained similar when accounting for attrition. Poverty predicted higher levels of behavior problems between 1.5 and 8 years of age. The difference between poor and non-poor children was stable over time for physical aggression, but increased with age for hyperactivity and opposition. Attrition among poor children did not compromise the validity of results.

Predictive Modeling of Polymer Mechanical Behavior Coupled to Chemical Change/ Technique Development for Measuring Polymer Physical Aging.

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

Kropka, Jamie Michael; Stavig, Mark E.; Arechederra, Gabe Kenneth

Develop an understanding of the evolution of glassy polymer mechanical response during aging and the mechanisms associated with that evolution. That understanding will be used to develop constitutive models to assess the impact of stress evolution in encapsulants on NW designs.

Development of Predictive Model for bridge deck cracking : final report.

DOT National Transportation Integrated Search

2017-04-01

Early-age bridge deck cracking has been found to be a prevalent problem worldwide. While early-age : cracking will not cause failure of a bridge deck system independently, the penetration of deleterious substances : through the early-age cracks into ...

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

Does warmth moderate longitudinal associations between maternal spanking and child aggression in early childhood?

PubMed

Lee, Shawna J; Altschul, Inna; Gershoff, Elizabeth T

2013-11-01

This study examines whether maternal warmth moderates the association between maternal use of spanking and increased child aggression between ages 1 and 5. Participants were 3,279 pairs of mothers and their children from a cohort study of urban families from 20 U.S. cities. Maternal spanking was assessed when the child was 1 year, 3 years, and 5 years of age. Maternal warmth and child aggressive behavior were measured at 3 years and 5 years of age. Models controlled for demographic characteristics (measured at the child's birth), child emotionality (measured at age 1), and maternal psychosocial risk factors (measured when children were 3 years old). Cross-lagged path models examined the within-time and longitudinal associations between spanking and child aggression. Results indicated that maternal spanking at age 1 was associated with higher levels of child aggression at age 3; similarly, maternal spanking at age 3 predicted increases in child aggression by age 5. Maternal warmth when children were 3 years old did not predict changes in child aggression between 3 and 5 years old. Furthermore, maternal warmth did not moderate the association between spanking and increased child aggression over time. Beginning as early as age 1, maternal spanking is predictive of child behavior problems, and maternal warmth does not counteract the negative consequences of the use of spanking.

Inflammation, But Not Telomere Length, Predicts Successful Ageing at Extreme Old Age: A Longitudinal Study of Semi-supercentenarians.

PubMed

Arai, Yasumichi; Martin-Ruiz, Carmen M; Takayama, Michiyo; Abe, Yukiko; Takebayashi, Toru; Koyasu, Shigeo; Suematsu, Makoto; Hirose, Nobuyoshi; von Zglinicki, Thomas

2015-10-01

To determine the most important drivers of successful ageing at extreme old age, we combined community-based prospective cohorts: Tokyo Oldest Old Survey on Total Health (TOOTH), Tokyo Centenarians Study (TCS) and Japanese Semi-Supercentenarians Study (JSS) comprising 1554 individuals including 684 centenarians and (semi-)supercentenarians, 167 pairs of centenarian offspring and spouses, and 536 community-living very old (85 to 99 years). We combined z scores from multiple biomarkers to describe haematopoiesis, inflammation, lipid and glucose metabolism, liver function, renal function, and cellular senescence domains. In Cox proportional hazard models, inflammation predicted all-cause mortality with hazard ratios (95% CI) 1.89 (1.21 to 2.95) and 1.36 (1.05 to 1.78) in the very old and (semi-)supercentenarians, respectively. In linear forward stepwise models, inflammation predicted capability (10.8% variance explained) and cognition (8(.)6% variance explained) in (semi-)supercentenarians better than chronologic age or gender. The inflammation score was also lower in centenarian offspring compared to age-matched controls with Δ (95% CI) = - 0.795 (- 1.436 to - 0.154). Centenarians and their offspring were able to maintain long telomeres, but telomere length was not a predictor of successful ageing in centenarians and semi-supercentenarians. We conclude that inflammation is an important malleable driver of ageing up to extreme old age in humans.

Age-class separation of blue-winged ducks

USGS Publications Warehouse

Hohman, W.L.; Moore, J.L.; Twedt, D.J.; Mensik, John G.; Logerwell, E.

1995-01-01

Accurate determination of age is of fundamental importance to population and life history studies of waterfowl and their management. Therefore, we developed quantitative methods that separate adult and immature blue-winged teal (Anas discors), cinnamon teal (A. cyanoptera), and northern shovelers (A. clypeata) during spring and summer. To assess suitability of discriminant models using 9 remigial measurements, we compared model performance (% agreement between predicted age and age assigned to birds on the basis of definitive cloacal or rectral feather characteristics) in different flyways (Mississippi and Pacific) and between years (1990-91 and 1991-92). We also applied age-classification models to wings obtained from U.S. Fish and Wildlife Service harvest surveys in the Mississippi and Central-Pacific flyways (wing-bees) for which age had been determined using qualitative characteristics (i.e., remigial markings, shape, or wear). Except for male northern shovelers, models correctly aged lt 90% (range 70-86%) of blue-winged ducks. Model performance varied among species and differed between sexes and years. Proportions of individuals that were correctly aged were greater for males (range 63-86%) than females (range 39-69%). Models for northern shovelers performed better in flyway comparisons within year (1991-92, La. model applied to Calif. birds, and Calif. model applied to La. birds: 90 and 94% for M, and 89 and 76% for F, respectively) than in annual comparisons within the Mississippi Flyway (1991-92 model applied to 1990-91 data: 79% for M, 50% for F). Exclusion of measurements that varied by flyway or year did not improve model performance. Quantitative methods appear to be of limited value for age separation of female blue-winged ducks. Close agreement between predicted age and age assigned to wings from the wing-bees suggests that qualitative and quantitative methods may be equally accurate for age separation of male blue-winged ducks. We interpret annual and flyway differences in remigial measurements and reduced performance of age classification models as evidence of high variability in size of blue-winged ducks' remiges. Variability in remigial size of these and other small-bodied waterfowl may be related to nutrition during molt.

Estimating age from recapture data: integrating incremental growth measures with ancillary data to infer age-at-length

USGS Publications Warehouse

Eaton, Mitchell J.; Link, William A.

2011-01-01

Estimating the age of individuals in wild populations can be of fundamental importance for answering ecological questions, modeling population demographics, and managing exploited or threatened species. Significant effort has been devoted to determining age through the use of growth annuli, secondary physical characteristics related to age, and growth models. Many species, however, either do not exhibit physical characteristics useful for independent age validation or are too rare to justify sacrificing a large number of individuals to establish the relationship between size and age. Length-at-age models are well represented in the fisheries and other wildlife management literature. Many of these models overlook variation in growth rates of individuals and consider growth parameters as population parameters. More recent models have taken advantage of hierarchical structuring of parameters and Bayesian inference methods to allow for variation among individuals as functions of environmental covariates or individual-specific random effects. Here, we describe hierarchical models in which growth curves vary as individual-specific stochastic processes, and we show how these models can be fit using capture–recapture data for animals of unknown age along with data for animals of known age. We combine these independent data sources in a Bayesian analysis, distinguishing natural variation (among and within individuals) from measurement error. We illustrate using data for African dwarf crocodiles, comparing von Bertalanffy and logistic growth models. The analysis provides the means of predicting crocodile age, given a single measurement of head length. The von Bertalanffy was much better supported than the logistic growth model and predicted that dwarf crocodiles grow from 19.4 cm total length at birth to 32.9 cm in the first year and 45.3 cm by the end of their second year. Based on the minimum size of females observed with hatchlings, reproductive maturity was estimated to be at nine years. These size benchmarks are believed to represent thresholds for important demographic parameters; improved estimates of age, therefore, will increase the precision of population projection models. The modeling approach that we present can be applied to other species and offers significant advantages when multiple sources of data are available and traditional aging techniques are not practical.

Predicting breeding bird occurrence by stand- and microhabitat-scale features in even-aged stands in the Central Appalachians

USGS Publications Warehouse

McDermott, M.E.; Wood, P.B.; Miller, G.W.; Simpson, B.T.

2011-01-01

Spatial scale is an important consideration when managing forest wildlife habitat, and models can be used to improve our understanding of these habitats at relevant scales. Our objectives were to determine whether stand- or microhabitat-scale variables better predicted bird metrics (diversity, species presence, and abundance) and to examine breeding bird response to clearcut size and age in a highly forested landscape. In 2004-2007, vegetation data were collected from 62 even-aged stands that were 3.6-34.6. ha in size and harvested in 1963-1990 on the Monongahela National Forest, WV, USA. In 2005-2007, we also surveyed birds at vegetation plots. We used classification and regression trees to model breeding bird habitat use with a suite of stand and microhabitat variables. Among stand variables, elevation, stand age, and stand size were most commonly retained as important variables in guild and species models. Among microhabitat variables, medium-sized tree density and tree species diversity most commonly predicted bird presence or abundance. Early successional and generalist bird presence, abundance, and diversity were better predicted by microhabitat variables than stand variables. Thus, more intensive field sampling may be required to predict habitat use for these species, and management may be needed at a finer scale. Conversely, stand-level variables had greater utility in predicting late-successional species occurrence and abundance; thus management decisions and modeling at this scale may be suitable in areas with a uniform landscape, such as our study area. Our study suggests that late-successional breeding bird diversity can be maximized long-term by including harvests >10. ha in size into our study area and by increasing tree diversity. Some harvesting will need to be incorporated regularly, because after 15 years, the study stands did not provide habitat for most early successional breeding specialists. ?? 2010 Elsevier B.V.

The variability of lower third molar development in Northeast Malaysian population with application to age estimation.

PubMed

Johan, N A; Khamis, M F; Abdul Jamal, N Sk; Ahmad, B; Mahanani, E S

2012-07-01

This study aimed to assess the variability of the lower third molar (tooth 38 and 48) development in Northeast Malaysian population with respect to the side of dentition, to generate age prediction models and to compare the outcome with other studies. A total of 1080 orthopantomograms of Northeast Malaysian population aged between 14 and 25 years (540 males and 540 females) from the Hospital Universiti Sains Malaysia's archive which met the inclusion and exclusion criteria were selected and the maturity stages of tooth 38 and 48 were scored using Demirjian's stages (A-H). The findings showed a wide variation of the development of lower third molars in the Northeast Malaysian population. The roots developed earlier in males than in females. The development of the dentition on opposite sides of the mandible was synchronously in females and males. A multiple regression analysis shows that 71.1% of variance in age was explained by sex and developmental stage of tooth 48. An age prediction model was generated from the regression analysis: [Age = 7.117 + 1.907*(stage of tooth 48) - 0.432*(sex)] with mean prediction errors between -0.17 to 3.14 years. The obtained data in the current study are useful for references and determining age of unidentified human remains for identification investigation.

Prediction of the age at onset in spinocerebellar ataxia type 1, 2, 3 and 6

PubMed Central

Tezenas du Montcel, Sophie; Durr, Alexandra; Rakowicz, Maria; Nanetti, Lorenzo; Charles, Perrine; Sulek, Anna; Mariotti, Caterina; Rola, Rafal; Schols, Ludger; Bauer, Peter; Dufaure-Garé, Isabelle; Jacobi, Heike; Forlani, Sylvie; Schmitz-Hübsch, Tanja; Filla, Alessandro; Timmann, Dagmar; van de Warrenburg, Bart P; Marelli, Cecila; Kang, Jun-Suk; Giunti, Paola; Cook, Arron; Baliko, Laszlo; Bela, Melegh; Boesch, Sylvia; Szymanski, Sandra; Berciano, José; Infante, Jon; Buerk, Katrin; Masciullo, Marcella; Di Fabio, Roberto; Depondt, Chantal; Ratka, Susanne; Stevanin, Giovanni; Klockgether, Thomas; Brice, Alexis; Golmard, Jean-Louis

2014-01-01

Background The most common spinocerebellar ataxias (SCA)—SCA1, SCA2, SCA3, and SCA6—are caused by (CAG)n repeat expansion. While the number of repeats of the coding (CAG)n expansions is correlated with the age at onset, there are no appropriate models that include both affected and preclinical carriers allowing for the prediction of age at onset. Methods We combined data from two major European cohorts of SCA1, SCA2, SCA3, and SCA6 mutation carriers: 1187 affected individuals from the EUROSCA registry and 123 preclinical individuals from the RISCA cohort. For each SCA genotype, a regression model was fitted using a log-normal distribution for age at onset with the repeat length of the alleles as covariates. From these models, we calculated expected age at onset from birth and conditionally that this age is greater than the current age. Results For SCA2 and SCA3 genotypes, the expanded allele was a significant predictor of age at onset (−0.105±0.005 and −0.056±0.003) while for SCA1 and SCA6 genotypes both the size of the expanded and normal alleles were significant (expanded: −0.049±0.002 and −0.090±0.009, respectively; normal: +0.013±0.005 and −0.029±0.010, respectively). According to the model, we indicated the median values (90% critical region) and the expectancy (SD) of the predicted age at onset for each SCA genotype according to the CAG repeat size and current age. Conclusions These estimations can be valuable in clinical and research. However, results need to be confirmed in other independent cohorts and in future longitudinal studies. ClinicalTrials.gov, number NCT01037777 and NCT00136630 for the French patients. PMID:24780882

The risk factors of laryngeal pathology in Korean adults using a decision tree model.

PubMed

Byeon, Haewon

2015-01-01

The purpose of this study was to identify risk factors affecting laryngeal pathology in the Korean population and to evaluate the derived prediction model. Cross-sectional study. Data were drawn from the 2008 Korea National Health and Nutritional Examination Survey. The subjects were 3135 persons (1508 male and 2114 female) aged 19 years and older living in the community. The independent variables were age, sex, occupation, smoking, alcohol drinking, and self-reported voice problems. A decision tree analysis was done to identify risk factors for predicting a model of laryngeal pathology. The significant risk factors of laryngeal pathology were age, gender, occupation, smoking, and self-reported voice problem in decision tree model. Four significant paths were identified in the decision tree model for the prediction of laryngeal pathology. Those identified as high risk groups for laryngeal pathology included those who self-reported a voice problem, those who were males in their 50s who did not recognize a voice problem, those who were not economically active males in their 40s, and male workers aged 19 and over and under 50 or 60 and over who currently smoked. The results of this study suggest that individual risk factors, such as age, sex, occupation, health behavior, and self-reported voice problem, affect the onset of laryngeal pathology in a complex manner. Based on the results of this study, early management of the high-risk groups is needed for the prevention of laryngeal pathology. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Childhood growth predicts higher bone mass and greater bone area in early old age: findings among a subgroup of women from the Helsinki Birth Cohort Study.

PubMed

Mikkola, T M; von Bonsdorff, M B; Osmond, C; Salonen, M K; Kajantie, E; Cooper, C; Välimäki, M J; Eriksson, J G

2017-09-01

We examined the associations between childhood growth and bone properties among women at early old age. Early growth in height predicted greater bone area and higher bone mineral mass. However, information on growth did not improve prediction of bone properties beyond that predicted by body size at early old age. We examined the associations between body size at birth and childhood growth with bone area, bone mineral content (BMC), and areal bone mineral density (aBMD) in early old age. A subgroup of women (n = 178, mean 60.4 years) from the Helsinki Birth Cohort Study, born 1934-1944, participated in dual-energy X-ray absorptiometry (DXA) measurements of the lumbar spine and hip. Height and weight at 0, 2, 7, and 11 years, obtained from health care records, were reconstructed into conditional variables representing growth velocity independent of earlier growth. Weight was adjusted for corresponding height. Linear regression models were adjusted for multiple confounders. Birth length and growth in height before 7 years of age were positively associated with femoral neck area (p < 0.05) and growth in height at all age periods studied with spine bone area (p < 0.01). Growth in height before the age of 7 years was associated with BMC in the femoral neck (p < 0.01) and birth length and growth in height before the age of 7 years were associated with BMC in the spine (p < 0.05). After entering adult height into the models, nearly all associations disappeared. Weight gain during childhood was not associated with bone area or BMC, and aBMD was not associated with early growth. Optimal growth in height in girls is important for obtaining larger skeleton and consequently higher bone mass. However, when predicting bone mineral mass among elderly women, information on early growth does not improve prediction beyond that predicted by current height and weight.

Assessing age- and silt index-independent diameter growth models of individual-tree Southern Appalachian hardwoods

Treesearch

Henry W. Mcnab; Thomas F. Lloyd

1999-01-01

Models of forest vegetation dynamics based on characteristics of individual trees are more suitable to predicting growth of multiple species and age classes than those based on stands. The objective of this study was to assess age- and site index-independent relationships between periodic diameter increment and tree and site effects for 11 major hardwood tree species....

Predictive aging results in radiation environments

NASA Astrophysics Data System (ADS)

Gillen, Kenneth T.; Clough, Roger L.

1993-06-01

We have previously derived a time-temperature-dose rate superposition methodology, which, when applicable, can be used to predict polymer degradation versus dose rate, temperature and exposure time. This methodology results in predictive capabilities at the low dose rates and long time periods appropriate, for instance, to ambient nuclear power plant environments. The methodology was successfully applied to several polymeric cable materials and then verified for two of the materials by comparisons of the model predictions with 12 year, low-dose-rate aging data on these materials from a nuclear environment. In this paper, we provide a more detailed discussion of the methodology and apply it to data obtained on a number of additional nuclear power plant cable insulation (a hypalon, a silicone rubber and two ethylene-tetrafluoroethylenes) and jacket (a hypalon) materials. We then show that the predicted, low-dose-rate results for our materials are in excellent agreement with long-term (7-9 year) low-dose-rate results recently obtained for the same material types actually aged under bnuclear power plant conditions. Based on a combination of the modelling and long-term results, we find indications of reasonably similar degradation responses among several different commercial formulations for each of the following "generic" materials: hypalon, ethylene-tetrafluoroethylene, silicone rubber and PVC. If such "generic" behavior can be further substantiated through modelling and long-term results on additional formulations, predictions of cable life for other commercial materials of the same generic types would be greatly facilitated.

What Parents Don’t Know: Disclosure and Secrecy in a Sample of Urban Adolescents

PubMed Central

Jäggi, Lena; Drazdowski, Tess K.; Kliewer, Wendy

2016-01-01

Research with two-parent European households has suggested that secrecy, and not disclosure of information per se, predicts adolescent adjustment difficulties. The present study attempted to replicate this finding using data from a 4-wave study of 358 poor, urban adolescents (47% male; M age = 12 yrs) in the United States, most of whom (> 92%) were African American. Adolescents self-reported secrecy, disclosure, depressive symptoms, and delinquency at each wave. Confirmatory factor analyses revealed that a two-factor model with secrecy and disclosure as separate, but correlated, factors was a better fit than a one-factor model. However, predictive models differed from previous research. Secrecy did not predict depressive symptoms, rather depressive symptoms predicted secrecy. For delinquency, there were significant paths from both secrecy to delinquency and delinquency to secrecy, as well as from delinquency to disclosure. These results did not differ by age or sex. Comparisons with previous findings are discussed. PMID:27639590

Quantitative investigations of different vaccination policies for the control of congenital rubella syndrome (CRS) in the United Kingdom.

PubMed Central

Anderson, R. M.; Grenfell, B. T.

1986-01-01

The paper examines predictions of the impact of various one-, two- and three-stage vaccination policies on the incidence of congenital rubella syndrome (CRS) in the United Kingdom with the aid of a mathematical model of the transmission dynamics of rubella virus. Parameter estimates for the model are derived from either serological data or case notifications, and special attention is given to the significance of age-related changes in the rate of exposure to rubella infection and heterogeneous mixing between age groups. Where possible, model predictions are compared with observed epidemiological trends. The principal conclusion of the analyses is that benefit is to be gained in the UK, both in the short and long term, by the introduction of a multiple-stage vaccination policy involving high levels of vaccination coverage of young male and female children (at around two years of age) and teenage girls (between the ages of 10-15 years), plus continued surveillance and vaccination of adult women in the child-bearing age classes. Model predictions suggest that to reduce the incidence of CRS in future years, below the level generated by a continuation of the current UK policy (the vaccination of teenage girls), would require high rates of vaccination (greater than 60%) of both boys and girls at around two years of age. Numerical studies also suggest that uniform vaccination coverage levels of greater than 80-85% of young male and female children could, in the long term (40 years or more), eradicate rubella virus from the population. The robustness of these conclusions with respect to the accuracy of parameter estimates and various assumptions concerning the pattern of age-related change in exposure to infections and 'who acquires infection from whom' is discussed. PMID:3701044

[Establishing a noninvasive prediction model for type 2 diabetes mellitus based on a rural Chinese population].

PubMed

Zhang, H Y; Shi, W H; Zhang, M; Yin, L; Pang, C; Feng, T P; Zhang, L; Ren, Y C; Wang, B Y; Yang, X Y; Zhou, J M; Han, C Y; Zhao, Y; Zhao, J Z; Hu, D S

2016-05-01

To provide a noninvasive type 2 diabetes mellitus (T2DM) prediction model for a rural Chinese population. From July to August, 2007 and July to August, 2008, a total of 20 194 participants aged ≥18 years were selected by cluster sampling technique from a rural population in two townships of Henan province, China. Data were collected by questionnaire interview, anthropometric measurement, and fasting plasma glucose and lipid profile examination. A total 17 265 participants were followed up from July to August, 2013 and July to October, 2014. Finally, 12 285 participants were selected for analysis. Data for these participants were randomly divided into a derivation group (derivation dataset, n= 6 143) and validation group (validation dataset, n=6 142) by 1∶1, respectively. Randomization was carried out by the use of computer-generated random numbers. A Cox regression model was used to analyze risk factors of T2DM in the derivation dataset. A T2DM prediction model was established by multiplying β by 10 for each significant variable. After the total score was calculated by the model, analysis of the receiver operating characteristic (ROC) curve was performed. The area under the ROC curve (AUC) was used for evaluating model predictability. Furthermore, the model's predictability was validated in the validation dataset and compared with the Finnish Diabetes Risk Score (FINDRISC) model. A total 779 of 12 285 participants developed T2DM during the 6-year study period. The incidence rate was 6.12% in the derivation dataset (n=376) and 6.56% in the validation dataset (n=403). The difference was not statistically significant (χ(2)=1.00, P=0.316). A total of four noninvasive T2DM prediction models were established using the Cox regression model. The ROCs of the risk score calculated by the prediction models indicated that the AUCs of these models were similar (0.67-0.70). The AUC and Youden index of model 4 was the highest. The optimal cut-off value, sensitivity, specificity, and Youden index were scores of 25, 65.96%, 66.47%, and 0.32, respectively. Age, sleep time, BMI, waist circumference, and hypertension were selected as predictive variables. Using age<30 years as reference, β values were 1.07, 1.58, and 1.67 and assigned scores were 11, 16, and 17 for age groups 30-44, 45-59, and ≥60 years, respectively. Using sleep time<8.0 h/d as reference, the β value and assigned score were 0.27 and 3, respectively, for sleep time ≥10.0 h/d. Using BMI 18.5-23.9 kg/m(2) as reference, β values were 0.53 and 1.00 and assigned scores 5 and 10, respectively, for BMI 24.0-27.9 kg/m(2), and ≥28.0 kg/m(2). Using waist circumference <85 cm for males/< 80 cm for females as reference, β values were 0.44 and 0.65 and assigned scores 4 and 7, respectively, for 85 cm ≤ waist circumference <90 cm for males/80 cm≤ waist circumference <85 cm for females, and waist circumference ≥90 cm for males/≥85 cm for females. Using nonhypertension as reference, the respective β value and assigned score were 0.34 and 3 for hypertension. The AUC performance of this model and the FINDRISC model was 0.66 and 0.64 (P=0.135), respectively, in the validation dataset. Based on this cohort study, a noninvasive prediction model that included age, sleep time, BMI, waist circumference, and hypertension was established, which is equivalent to the FINDRISC model and applicable to a rural Chinese population.

Modelling uveal melanoma

PubMed Central

Foss, A.; Cree, I.; Dolin, P.; Hungerford, J.

1999-01-01

BACKGROUND/AIM—There has been no consistent pattern reported on how mortality for uveal melanoma varies with age. This information can be useful to model the complexity of the disease. The authors have examined ocular cancer trends, as an indirect measure for uveal melanoma mortality, to see how rates vary with age and to compare the results with their other studies on predicting metastatic disease.
METHODS—Age specific mortality was examined for England and Wales, the USA, and Canada. A log-log model was fitted to the data. The slopes of the log-log plots were used as measure of disease complexity and compared with the results of previous work on predicting metastatic disease.
RESULTS—The log-log model provided a good fit for the US and Canadian data, but the observed rates deviated for England and Wales among people over the age of 65 years. The log-log model for mortality data suggests that the underlying process depends upon four rate limiting steps, while a similar model for the incidence data suggests between three and four rate limiting steps. Further analysis of previous data on predicting metastatic disease on the basis of tumour size and blood vessel density would indicate a single rate limiting step between developing the primary tumour and developing metastatic disease.
CONCLUSIONS—There is significant underreporting or underdiagnosis of ocular melanoma for England and Wales in those over the age of 65 years. In those under the age of 65, a model is presented for ocular melanoma oncogenesis requiring three rate limiting steps to develop the primary tumour and a fourth rate limiting step to develop metastatic disease. The three steps in the generation of the primary tumour involve two key processes—namely, growth and angiogenesis within the primary tumour. The step from development of the primary to development of metastatic disease is likely to involve a single rate limiting process.

 PMID:10216060

A new method of estimating thermal performance of embryonic development rate yields accurate prediction of embryonic age in wild reptile nests.

PubMed

Rollinson, Njal; Holt, Sarah M; Massey, Melanie D; Holt, Richard C; Nancekivell, E Graham; Brooks, Ronald J

2018-05-01

Temperature has a strong effect on ectotherm development rate. It is therefore possible to construct predictive models of development that rely solely on temperature, which have applications in a range of biological fields. Here, we leverage a reference series of development stages for embryos of the turtle Chelydra serpentina, which was described at a constant temperature of 20 °C. The reference series acts to map each distinct developmental stage onto embryonic age (in days) at 20 °C. By extension, an embryo taken from any given incubation environment, once staged, can be assigned an equivalent age at 20 °C. We call this concept "Equivalent Development", as it maps the development stage of an embryo incubated at a given temperature to its equivalent age at a reference temperature. In the laboratory, we used the concept of Equivalent Development to estimate development rate of embryos of C. serpentina across a series of constant temperatures. Using these estimates of development rate, we created a thermal performance curve measured in units of Equivalent Development (TPC ED ). We then used the TPC ED to predict developmental stage of embryos in several natural turtle nests across six years. We found that 85% of the variation of development stage in natural nests could be explained. Further, we compared the predictive accuracy of the model based on the TPC ED to the predictive accuracy of a degree-day model, where development is assumed to be linearly related to temperature and the amount of accumulated heat is summed over time. Information theory suggested that the model based on the TPC ED better describes variation in developmental stage in wild nests than the degree-day model. We suggest the concept of Equivalent Development has several strengths and can be broadly applied. In particular, studies on temperature-dependent sex determination may be facilitated by the concept of Equivalent Development, as development age maps directly onto the developmental series of the organism, allowing critical periods of sex determination to be delineated without invasive sampling, even under fluctuating temperature. Copyright © 2018 Elsevier Ltd. All rights reserved.

Retention of community college students in online courses

NASA Astrophysics Data System (ADS)

Krajewski, Sarah

The issue of attrition in online courses at higher learning institutions remains a high priority in the United States. A recent rapid growth of online courses at community colleges has been instigated by student demand, as they meet the time constraints many nontraditional community college students have as a result of the need to work and care for dependents. Failure in an online course can cause students to become frustrated with the college experience, financially burdened, or to even give up and leave college. Attrition could be avoided by proper guidance of who is best suited for online courses. This study examined factors related to retention (i.e., course completion) and success (i.e., receiving a C or better) in an online biology course at a community college in the Midwest by operationalizing student characteristics (age, race, gender), student skills (whether or not the student met the criteria to be placed in an AFP course), and external factors (Pell recipient, full/part time status, first term) from the persistence model developed by Rovai. Internal factors from this model were not included in this study. Both univariate analyses and multivariate logistic regression were used to analyze the variables. Results suggest that race and Pell recipient were both predictive of course completion on univariate analyses. However, multivariate analyses showed that age, race, academic load and first term were predictive of completion and Pell recipient was no longer predictive. The univariate results for the C or better showed that age, race, Pell recipient, academic load, and meeting AFP criteria were predictive of success. Multivariate analyses showed that only age, race, and Pell recipient were significant predictors of success. Both regression models explained very little (<15%) of the variability within the outcome variables of retention and success. Therefore, although significant predictors were identified for course completion and retention, there are still many factors that remain unaccounted for in both regression models. Further research into the operationalization of Rovai's model, including internal factors, to predict completion and success is necessary.

An Experimental Evaluation of Competing Age-Predictions of Future Time Perspective between Workplace and Retirement Domains.

PubMed

Kerry, Matthew J; Embretson, Susan E

2017-01-01

Future time perspective (FTP) is defined as "perceptions of the future as being limited or open-ended" (Lang and Carstensen, 2002; p. 125). The construct figures prominently in both workplace and retirement domains, but the age-predictions are competing: Workplace research predicts decreasing FTP age-change, in contrast, retirement scholars predict increasing FTP age-change. For the first time, these competing predictions are pitted in an experimental manipulation of subjective life expectancy (SLE). A sample of N = 207 older adults (age 45-60) working full-time (>30-h/week) were randomly assigned to SLE questions framed as either 'Live-to' or 'Die-by' to evaluate competing predictions for FTP. Results indicate general support for decreasing age-change in FTP, indicated by independent-sample t -tests showing lower FTP in the 'Die-by' framing condition. Further general-linear model analyses were conducted to test for interaction effects of retirement planning with experimental framings on FTP and intended retirement; While retirement planning buffered FTP's decrease, simple-effects also revealed that retirement planning increased intentions for sooner retirement, but lack of planning increased intentions for later retirement. Discussion centers on practical implications of our findings and consequences validity evidence in future empirical research of FTP in both workplace and retirement domains.

Prediction of brain maturity based on cortical thickness at different spatial resolutions.

PubMed

Khundrakpam, Budhachandra S; Tohka, Jussi; Evans, Alan C

2015-05-01

Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n=308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R=0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity. Copyright © 2015 Elsevier Inc. All rights reserved.

Age-Specific Prostate Specific Antigen Cutoffs for Guiding Biopsy Decision in Chinese Population

PubMed Central

Xu, Jianfeng; Jiang, Haowen; Ding, Qiang

2013-01-01

Background Age-specific prostate specific antigen (PSA) cutoffs for prostate biopsy have been widely used in the USA and European countries. However, the application of age-specific PSA remains poorly understood in China. Methods Between 2003 and 2012, 1,848 men over the age of 40, underwent prostate biopsy for prostate cancer (PCa) at Huashan Hospital, Shanghai, China. Clinical information and blood samples were collected prior to biopsy for each patient. Men were divided into three age groups (≤60, 61 to 80, and >80) for analyses. Digital rectal examination (DRE), transrectal ultrasound (prostate volume and nodule), total PSA (tPSA), and free PSA (fPSA) were also included in the analyses. Logistic regression was used to build the multi-variate model. Results Serum tPSA levels were age-dependent (P = 0.008), while %fPSA (P = 0.051) and PSAD (P = 0.284) were age-independent. At a specificity of 80%, the sensitivities for predicting PCa were 83%, 71% and 68% with tPSA cutoff values of 19.0 ng/mL (age≤60),21.0 ng/mL (age 61–80), and 23.0 ng/mL (age≥81). Also, sensitivities at the same tPSA levels were able to reach relatively high levels (70%–88%) for predicting high-grade PCa. Area (AUC) under the receive operating curves (ROCs) of tPSA, %fPSA, PSAD and multi-variate model were different in age groups. When predicting PCa, the AUC of tPSA, %fPSA, PSAD and multi-variate model were 0.90, 0.57, 0.93 and 0.87 respectively in men ≤60 yr; 0.82, 0.70, 0.88 and 0.86 respectively in men 61–80 yr; 0.79, 0.78, 0.87 and 0.88 respectively in men>80 yr. When predicting Gleason Score ≥7 or 8 PCa, there were no significant differences between AUCs of each variable. Conclusion Age-specific PSA cutoff values for prostate biopsy should be considered in the Chinese population. Indications for prostate biopsies (tPSA, %fPSA and PSAD) should be considered based on age in the Chinese population. PMID:23825670

The future impact of population growth and aging on coronary heart disease in China: projections from the Coronary Heart Disease Policy Model-China

PubMed Central

Moran, Andrew; Zhao, Dong; Gu, Dongfeng; Coxson, Pamela; Chen, Chung-Shiuan; Cheng, Jun; Liu, Jing; He, Jiang; Goldman, Lee

2008-01-01

Background China will experience an overall growth and aging of its adult population in coming decades. We used a computer model to forecast the future impact of these demographic changes on coronary heart disease (CHD) in China. Methods The CHD Policy Model is a validated state-transition, computer simulation of CHD on a national scale. China-specific CHD risk factor, incidence, case-fatality, and prevalence data were incorporated, and a CHD prediction model was generated from a Chinese cohort study and calibrated to age-specific Chinese mortality rates. Disability-adjusted life years (DALYs) due to CHD were calculated using standard methods. The projected population of China aged 35–84 years was entered, and CHD events, deaths, and DALYs were simulated over 2000–2029. CHD risk factors other than age and case-fatality were held at year 2000 levels. Sensitivity analyses tested uncertainty regarding CHD mortality coding, the proportion of total deaths attributable to CHD, and case-fatality. Results We predicted 7.8 million excess CHD events (a 69% increase) and 3.4 million excess CHD deaths (a 64% increase) in the decade 2020–2029 compared with 2000–2009. For 2030, we predicted 71% of almost one million annual CHD deaths will occur in persons ≥65 years old, while 67% of the growing annual burden of CHD death and disability will weigh on adults <65 years old. Substituting alternate CHD mortality assumptions led to 17–20% more predicted CHD deaths over 2000–2029, though the pattern of increases in CHD events and deaths over time remained. Conclusion We forecast that absolute numbers of CHD events and deaths will increase dramatically in China over 2010–2029, due to a growing and aging population alone. Recent data suggest CHD risk factor levels are increasing, so our projections may underestimate the extent of the potential CHD epidemic in China. PMID:19036167

Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia

USGS Publications Warehouse

Jennings, Cecil A.; Sundmark, Aaron P.

2017-01-01

The relationships between environmental variables and the growth rates of fishes are important and rapidly expanding topics in fisheries ecology. We used an informationtheoretic approach to evaluate the influence of lake surface area and total phosphorus on the age-specific growth rates of Lepomis macrochirus (Bluegill) in 6 small impoundments in central Georgia. We used model averaging to create composite models and determine the relative importance of the variables within each model. Results indicated that surface area was the most important factor in the models predicting growth of Bluegills aged 1–4 years; total phosphorus was also an important predictor for the same age-classes. These results suggest that managers can use water quality and lake morphometry variables to create predictive models specific to their waterbody or region to help develop lake-specific management plans that select for and optimize local-level habitat factors for enhancing Bluegill growth.

Prediction of five-year all-cause mortality in Chinese patients with type 2 diabetes mellitus - A population-based retrospective cohort study.

PubMed

Wan, Eric Yuk Fai; Fong, Daniel Yee Tak; Fung, Colman Siu Cheung; Yu, Esther Yee Tak; Chin, Weng Yee; Chan, Anca Ka Chun; Lam, Cindy Lo Kuen

2017-06-01

This study aimed to develop and validate an all-cause mortality risk prediction model for Chinese primary care patients with type 2 diabetes mellitus(T2DM) in Hong Kong. A population-based retrospective cohort study was conducted on 132,462 Chinese patients who had received public primary care services during 2010. Each gender sample was randomly split on a 2:1 basis into derivation and validation cohorts and was followed-up for a median period of 5years. Gender-specific mortality risk prediction models showing the interaction effect between predictors and age were derived using Cox proportional hazards regression with forward stepwise approach. Developed models were compared with pre-existing models by Harrell's C-statistic and calibration plot using validation cohort. Common predictors of increased mortality risk in both genders included: age; smoking habit; diabetes duration; use of anti-hypertensive agents, insulin and lipid-lowering drugs; body mass index; hemoglobin A1c; systolic blood pressure(BP); total cholesterol to high-density lipoprotein-cholesterol ratio; urine albumin to creatinine ratio(urine ACR); and estimated glomerular filtration rate(eGFR). Prediction models showed better discrimination with Harrell"'s C-statistics of 0.768(males) and 0.782(females) and calibration power from the plots than previously established models. Our newly developed gender-specific models provide a more accurate predicted 5-year mortality risk for Chinese diabetic patients than other established models. Copyright © 2017 Elsevier Inc. All rights reserved.

Lung function parameters improve prediction of VO2peak in an elderly population: The Generation 100 study.

PubMed

Hassel, Erlend; Stensvold, Dorthe; Halvorsen, Thomas; Wisløff, Ulrik; Langhammer, Arnulf; Steinshamn, Sigurd

2017-01-01

Peak oxygen uptake (VO2peak) is an indicator of cardiovascular health and a useful tool for risk stratification. Direct measurement of VO2peak is resource-demanding and may be contraindicated. There exist several non-exercise models to estimate VO2peak that utilize easily obtainable health parameters, but none of them includes lung function measures or hemoglobin concentrations. We aimed to test whether addition of these parameters could improve prediction of VO2peak compared to an established model that includes age, waist circumference, self-reported physical activity and resting heart rate. We included 1431 subjects aged 69-77 years that completed a laboratory test of VO2peak, spirometry, and a gas diffusion test. Prediction models for VO2peak were developed with multiple linear regression, and goodness of fit was evaluated. Forced expiratory volume in one second (FEV1), diffusing capacity of the lung for carbon monoxide and blood hemoglobin concentration significantly improved the ability of the established model to predict VO2peak. The explained variance of the model increased from 31% to 48% for men and from 32% to 38% for women (p<0.001). FEV1, diffusing capacity of the lungs for carbon monoxide and hemoglobin concentration substantially improved the accuracy of VO2peak prediction when added to an established model in an elderly population.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Predicting VO[subscript 2max] in College-Aged Participants Using Cycle Ergometry and Perceived Functional Ability

ERIC Educational Resources Information Center

Nielson, David E.; George, James D.; Vehrs, Pat R.; Hager, Ron L.; Webb, Carrie V.

2010-01-01

The purpose of this study was to develop a multiple linear regression model to predict treadmill VO[subscript 2max] scores using both exercise and non-exercise data. One hundred five college-aged participants (53 male, 52 female) successfully completed a submaximal cycle ergometer test and a maximal graded exercise test on a motorized treadmill.…

Classroom Dimensions Predict Early Peer Interaction when Children Are Diverse in Ethnicity, Race, and Home Language

ERIC Educational Resources Information Center

Howes, Carollee; Guerra, Alison Wishard; Fuligni, Allison; Zucker, Eleanor; Lee, Linda; Obregon, Nora B.; Spivak, Asha

2011-01-01

The purpose of this study was to test a model for predicting preschool-age children's behaviors with peers from dimensions of the classroom and teacher-child relationship quality when the children were from diverse race, ethnic, and home language backgrounds. Eight hundred children, (M=age 63 months, SD=8.1 months), part of the National Evaluation…

A Maximal Graded Exercise Test to Accurately Predict VO2max in 18-65-Year-Old Adults

ERIC Educational Resources Information Center

George, James D.; Bradshaw, Danielle I.; Hyde, Annette; Vehrs, Pat R.; Hager, Ronald L.; Yanowitz, Frank G.

2007-01-01

The purpose of this study was to develop an age-generalized regression model to predict maximal oxygen uptake (VO sub 2 max) based on a maximal treadmill graded exercise test (GXT; George, 1996). Participants (N = 100), ages 18-65 years, reached a maximal level of exertion (mean plus or minus standard deviation [SD]; maximal heart rate [HR sub…

Mathematical modelling of respiratory syncytial virus (RSV): vaccination strategies and budget applications.

PubMed

Acedo, L; Díez-Domingo, J; Moraño, J-A; Villanueva, R-J

2010-06-01

We propose an age-structured mathematical model for respiratory syncytial virus in which children aged <1 year are especially considered. Real data on hospitalized children in the Spanish region of Valencia were used in order to determine some seasonal parameters of the model. Weekly predictions of the number of children aged <1 year that will be hospitalized in the following years in Valencia are presented using this model. Results are applied to estimate the regional cost of paediatric hospitalizations and to perform a cost-effectiveness analysis of possible vaccination strategies.

“Teens are from Mars, Adults are from Venus”: Analyzing and Predicting Age Groups with Behavioral Characteristics in Instagram

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

Han, Kyungsik; Lee, Sanghack; Jang, Jin

We present behavioral characteristics of teens and adults in Instagram and prediction of them from their behaviors. Based on two independently created datasets from user profiles and tags, we identify teens and adults, and carry out comparative analyses on their online behaviors. Our study reveals: (1) significant behavioral differences between two age groups; (2) the empirical evidence of classifying teens and adults with up to 82% accuracy, using traditional predictive models, while two baseline methods achieve 68% at best; and (3) the robustness of our models by achieving 76%—81% when tested against an independent dataset obtained without using user profilesmore » or tags.« less

Prediction of new onset of end stage renal disease in Chinese patients with type 2 diabetes mellitus - a population-based retrospective cohort study.

PubMed

Wan, Eric Yuk Fai; Fong, Daniel Yee Tak; Fung, Colman Siu Cheung; Yu, Esther Yee Tak; Chin, Weng Yee; Chan, Anca Ka Chun; Lam, Cindy Lo Kuen

2017-08-01

Since diabetes mellitus (DM) is the leading cause of end stage renal disease (ESRD), this study aimed to develop a 5-year ESRD risk prediction model among Chinese patients with Type 2 DM (T2DM) in primary care. A retrospective cohort study was conducted on 149,333 Chinese adult T2DM primary care patients without ESRD in 2010. Using the derivation cohort over a median of 5 years follow-up, the gender-specific models including the interaction effect between predictors and age were derived using Cox regression with a forward stepwise approach. Harrell's C-statistic and calibration plot were applied to the validation cohort to assess discrimination and calibration of the models. Prediction models showed better discrimination with Harrell's C-statistics of 0.866 (males) and 0.862 (females) and calibration power from the plots than other established models. The predictors included age, usages of anti-hypertensive drugs, anti-glucose drugs, and Hemogloblin A1c, blood pressure, urine albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). Specific predictors for male were smoking and presence of sight threatening diabetic retinopathy while additional predictors for female included longer duration of diabetes and quadratic effect of body mass index. Interaction factors with age showed a greater weighting of insulin and urine ACR in younger males, and eGFR in younger females. Our newly developed gender-specific models provide a more accurate 5-year ESRD risk predictions for Chinese diabetic primary care patients than other existing models. The models included several modifiable risk factors that clinicians can use to counsel patients, and to target at in the delivery of care to patients.

Individual risk of cutaneous melanoma in New Zealand: developing a clinical prediction aid.

PubMed

Sneyd, Mary Jane; Cameron, Claire; Cox, Brian

2014-05-22

New Zealand and Australia have the highest melanoma incidence rates worldwide. In New Zealand, both the incidence and thickness have been increasing. Clinical decisions require accurate risk prediction but a simple list of genetic, phenotypic and behavioural risk factors is inadequate to estimate individual risk as the risk factors for melanoma have complex interactions. In order to offer tailored clinical management strategies, we developed a New Zealand prediction model to estimate individual 5-year absolute risk of melanoma. A population-based case-control study (368 cases and 270 controls) of melanoma risk factors provided estimates of relative risks for fair-skinned New Zealanders aged 20-79 years. Model selection techniques and multivariate logistic regression were used to determine the important predictors. The relative risks for predictors were combined with baseline melanoma incidence rates and non-melanoma mortality rates to calculate individual probabilities of developing melanoma within 5 years. For women, the best model included skin colour, number of moles > =5 mm on the right arm, having a 1st degree relative with large moles, and a personal history of non-melanoma skin cancer (NMSC). The model correctly classified 68% of participants; the C-statistic was 0.74. For men, the best model included age, place of occupation up to age 18 years, number of moles > =5 mm on the right arm, birthplace, and a history of NMSC. The model correctly classified 67% of cases; the C-statistic was 0.71. We have developed the first New Zealand risk prediction model that calculates individual absolute 5-year risk of melanoma. This model will aid physicians to identify individuals at high risk, allowing them to individually target surveillance and other management strategies, and thereby reduce the high melanoma burden in New Zealand.

Predictive value of age for coping: the role of self-efficacy, social support satisfaction and perceived stress.

PubMed

Trouillet, Raphaël; Gana, Kamel; Lourel, Marcel; Fort, Isabelle

2009-05-01

The present study was prompted by the lack of agreement on how coping changes with age. We postulate that the effect of age on coping is mediated by coping resources, such as self-efficacy, perceived stress and social support satisfaction. The participants in the study were community dwelling and aged between 22 and 88 years old. Data were collected using the General Self Efficacy Scale, the Social Support Questionnaire, the Perceived Stress Scale, the Geriatric Depression Scale, the Social Readjustment Rating Scale (life-events) and the Way of Coping Checklist. We performed path analyses for two competitive structural models: M1 (age does not directly affect coping processes) and M2 (age directly affects coping processes). Our results supported a modified version of M2. Age was not found to predict either of two coping strategies: problem-focused coping is predicted by self-efficacy and social support satisfaction; emotion-focused coping is predicted by social support satisfaction and perceived stress. Changes in coping over the lifespan reflect the effectiveness with which a person's adaptive processes deal with age-associated changes in self-referred beliefs and environment perception.

Thermal Modeling of Al-Al and Al-Steel Friction Stir Spot Welding

NASA Astrophysics Data System (ADS)

Jedrasiak, P.; Shercliff, H. R.; Reilly, A.; McShane, G. J.; Chen, Y. C.; Wang, L.; Robson, J.; Prangnell, P.

2016-09-01

This paper presents a finite element thermal model for similar and dissimilar alloy friction stir spot welding (FSSW). The model is calibrated and validated using instrumented lap joints in Al-Al and Al-Fe automotive sheet alloys. The model successfully predicts the thermal histories for a range of process conditions. The resulting temperature histories are used to predict the growth of intermetallic phases at the interface in Al-Fe welds. Temperature predictions were used to study the evolution of hardness of a precipitation-hardened aluminum alloy during post-weld aging after FSSW.

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.

Eco-genetic modeling of contemporary life-history evolution.

PubMed

Dunlop, Erin S; Heino, Mikko; Dieckmann, Ulf

2009-10-01

We present eco-genetic modeling as a flexible tool for exploring the course and rates of multi-trait life-history evolution in natural populations. We build on existing modeling approaches by combining features that facilitate studying the ecological and evolutionary dynamics of realistically structured populations. In particular, the joint consideration of age and size structure enables the analysis of phenotypically plastic populations with more than a single growth trajectory, and ecological feedback is readily included in the form of density dependence and frequency dependence. Stochasticity and life-history trade-offs can also be implemented. Critically, eco-genetic models permit the incorporation of salient genetic detail such as a population's genetic variances and covariances and the corresponding heritabilities, as well as the probabilistic inheritance and phenotypic expression of quantitative traits. These inclusions are crucial for predicting rates of evolutionary change on both contemporary and longer timescales. An eco-genetic model can be tightly coupled with empirical data and therefore may have considerable practical relevance, in terms of generating testable predictions and evaluating alternative management measures. To illustrate the utility of these models, we present as an example an eco-genetic model used to study harvest-induced evolution of multiple traits in Atlantic cod. The predictions of our model (most notably that harvesting induces a genetic reduction in age and size at maturation, an increase or decrease in growth capacity depending on the minimum-length limit, and an increase in reproductive investment) are corroborated by patterns observed in wild populations. The predicted genetic changes occur together with plastic changes that could phenotypically mask the former. Importantly, our analysis predicts that evolutionary changes show little signs of reversal following a harvest moratorium. This illustrates how predictions offered by eco-genetic models can enable and guide evolutionarily sustainable resource management.

Growth in perceived control across 25 years from the late teens to midlife: the role of personal and parents' education.

PubMed

Vargas Lascano, Dayuma I; Galambos, Nancy L; Krahn, Harvey J; Lachman, Margie E

2015-01-01

This study examined trajectories of perceived control and their association with parents' education and personal educational experience (educational attainment and years of full-time postsecondary education) in 971 Canadian high school seniors tracked 7 times across 25 years. Latent growth models showed that, on average, perceived control increased from age 18 to age 25 and decreased by age 32, with a further slower decrease by age 43. Parents' education contributed to a growing gap in perceived control, however, such that among individuals with at least 1 university-educated parent, perceived control increased across 25 years, reaching its highest level at age 43. Personal educational attainment (completion of a university degree or not) was not associated with growth in perceived control, but individuals who were higher on perceived control at age 18 were more likely to complete a university degree. Parallel process modeling found that perceived control at age 19 predicted gains through age 32 in years of postsecondary education. Postsecondary enrollment at age 19 did not predict gains in perceived control over time. Parents' education predicted both higher levels of perceived control and enrollment in full-time postsecondary education at age 19. Family socioeconomic status contributes to perceived control early in the transition to adulthood and may lead to diverging trajectories over the next 25 years, and perceived control contributes to subsequent postsecondary educational experience. Further longitudinal research should explore the development and determinants of perceived control across the full life span.

Personality Plasticity After Age 30

PubMed Central

Terracciano, Antonio; Costa, Paul T.; McCrae, Robert R.

2009-01-01

Rank-order consistency of personality traits increases from childhood to age 30. After that, different summaries of the literature predict a plateau at age 30, or at age 50, or a curvilinear peak in consistency at age 50. These predictions were evaluated at group and individual levels using longitudinal data from the Guilford-Zimmerman Temperament Survey and the Revised NEO Personality Inventory over periods of up to 42 years. Consistency declined toward a non-zero asymptote with increasing time-interval. Although some scales showed increasing stability after age 30, the rank-order consistencies of the major dimensions and most facets of the Five-Factor Model were unrelated to age. Ipsative stability, assessed with the California Adult Q-Set, was also unrelated to age. These data strengthen claims of predominant personality stability after age 30. PMID:16861305

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

PubMed

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

2015-03-01

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

Modelling the molecular mechanisms of aging

PubMed Central

Mc Auley, Mark T.; Guimera, Alvaro Martinez; Hodgson, David; Mcdonald, Neil; Mooney, Kathleen M.; Morgan, Amy E.

2017-01-01

The aging process is driven at the cellular level by random molecular damage that slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the aging process. The complexity of the aging process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards and discusses many specific examples of models that have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field. PMID:28096317

Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies.

PubMed

Sowislo, Julia Friederike; Orth, Ulrich

2013-01-01

Low self-esteem and depression are strongly related, but there is not yet consistent evidence on the nature of the relation. Whereas the vulnerability model states that low self-esteem contributes to depression, the scar model states that depression erodes self-esteem. Furthermore, it is unknown whether the models are specific for depression or whether they are also valid for anxiety. We evaluated the vulnerability and scar models of low self-esteem and depression, and low self-esteem and anxiety, by meta-analyzing the available longitudinal data (covering 77 studies on depression and 18 studies on anxiety). The mean age of the samples ranged from childhood to old age. In the analyses, we used a random-effects model and examined prospective effects between the variables, controlling for prior levels of the predicted variables. For depression, the findings supported the vulnerability model: The effect of self-esteem on depression (β = -.16) was significantly stronger than the effect of depression on self-esteem (β = -.08). In contrast, the effects between low self-esteem and anxiety were relatively balanced: Self-esteem predicted anxiety with β = -.10, and anxiety predicted self-esteem with β = -.08. Moderator analyses were conducted for the effect of low self-esteem on depression; these suggested that the effect is not significantly influenced by gender, age, measures of self-esteem and depression, or time lag between assessments. If future research supports the hypothesized causality of the vulnerability effect of low self-esteem on depression, interventions aimed at increasing self-esteem might be useful in reducing the risk of depression.

Intellectual Development within Transracial Adoptive Families: Retesting the Confluence Model.

ERIC Educational Resources Information Center

Berbaum, Michael L.; Moreland, Richard L.

1985-01-01

Estimates confluence model of intellectual development for a within-family sample of 321 children from 101 transracial adoptive families. Mental ages of children and their parents and birth or adoption intervals were used in a nonlinear least-squares estimation procedure to obtain children's predicted mental ages. Results suggest efficiency of the…

Age structure is critical to the population dynamics and survival of honeybee colonies

PubMed Central

Betti, M. I.; Wahl, L. M.

2016-01-01

Age structure is an important feature of the division of labour within honeybee colonies, but its effects on colony dynamics have rarely been explored. We present a model of a honeybee colony that incorporates this key feature, and use this model to explore the effects of both winter and disease on the fate of the colony. The model offers a novel explanation for the frequently observed phenomenon of ‘spring dwindle’, which emerges as a natural consequence of the age-structured dynamics. Furthermore, the results indicate that a model taking age structure into account markedly affects the predicted timing and severity of disease within a bee colony. The timing of the onset of disease with respect to the changing seasons may also have a substantial impact on the fate of a honeybee colony. Finally, simulations predict that an infection may persist in a honeybee colony over several years, with effects that compound over time. Thus, the ultimate collapse of the colony may be the result of events several years past. PMID:28018627

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint

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

Smith, Kandler A; Saxon, Aron R; Keyser, Matthew A

Life Prediction Model for Grid-Connected Li-ion Battery Energy Storage System: Preprint Lithium-ion (Li-ion) batteries are being deployed on the electrical grid for a variety of purposes, such as to smooth fluctuations in solar renewable power generation. The lifetime of these batteries will vary depending on their thermal environment and how they are charged and discharged. To optimal utilization of a battery over its lifetime requires characterization of its performance degradation under different storage and cycling conditions. Aging tests were conducted on commercial graphite/nickel-manganese-cobalt (NMC) Li-ion cells. A general lifetime prognostic model framework is applied to model changes in capacity andmore » resistance as the battery degrades. Across 9 aging test conditions from 0oC to 55oC, the model predicts capacity fade with 1.4 percent RMS error and resistance growth with 15 percent RMS error. The model, recast in state variable form with 8 states representing separate fade mechanisms, is used to extrapolate lifetime for example applications of the energy storage system integrated with renewable photovoltaic (PV) power generation.« less

Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data.

PubMed

Walters, K; Hardoon, S; Petersen, I; Iliffe, S; Omar, R Z; Nazareth, I; Rait, G

2016-01-21

Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data. We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60-95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60-79 and 80-95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables. Dementia incidence was 1.88 (95% CI, 1.83-1.93) and 16.53 (95% CI, 16.15-16.92) per 1000 PYAR for those aged 60-79 (n = 6017) and 80-95 years (n = 7104), respectively. Predictors for those aged 60-79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60-79 years model; D statistic 2.03 (95% CI, 1.95-2.11), C index 0.84 (95% CI, 0.81-0.87), and calibration slope 0.98 (95% CI, 0.93-1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80-95 years model. Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60-79, but not those aged 80+. This algorithm can identify higher risk populations for dementia in primary care. The risk score has a high negative predictive value and may be most helpful in 'ruling out' those at very low risk from further testing or intensive preventative activities.

Possible Selves and Self-Regulatory Beliefs: Exploring the Relationship Between Health Selves, Health Efficacy, and Psychological Well-Being.

PubMed

Dark-Freudeman, Alissa; West, Robin L

2016-03-01

The present study identified middle-aged (ages 40-64) and older individuals (ages 65-90) who reported a highly important possible self related to health. The relationship between age, physical health, health efficacy, and psychological well-being were examined among these individuals. We tested a model in which health efficacy predicted both positive and negative psychological well-being. For both age groups, self-reported health predicted health self-efficacy; however, the direct effects of health efficacy on both positive and negative psychological well-being were also significant. Higher levels of health efficacy were associated with higher levels of positive psychological well-being and lower levels of negative well-being, as predicted. Physical health indirectly predicted well-being through its impact on health self-efficacy for middle-aged and older individuals who valued their health highly. Overall, these results support the notion that health efficacy related to a most important health self is a predictor of psychological well-being in mid and late life. © The Author(s) 2016.

Importance of Multimodal MRI in Characterizing Brain Tissue and Its Potential Application for Individual Age Prediction.

PubMed

Cherubini, Andrea; Caligiuri, Maria Eugenia; Peran, Patrice; Sabatini, Umberto; Cosentino, Carlo; Amato, Francesco

2016-09-01

This study presents a voxel-based multiple regression analysis of different magnetic resonance image modalities, including anatomical T1-weighted, T2(*) relaxometry, and diffusion tensor imaging. Quantitative parameters sensitive to complementary brain tissue alterations, including morphometric atrophy, mineralization, microstructural damage, and anisotropy loss, were compared in a linear physiological aging model in 140 healthy subjects (range 20-74 years). The performance of different predictors and the identification of the best biomarker of age-induced structural variation were compared without a priori anatomical knowledge. The best quantitative predictors in several brain regions were iron deposition and microstructural damage, rather than macroscopic tissue atrophy. Age variations were best resolved with a combination of markers, suggesting that multiple predictors better capture age-induced tissue alterations. The results of the linear model were used to predict apparent age in different regions of individual brain. This approach pointed to a number of novel applications that could potentially help highlighting areas particularly vulnerable to disease.

Estimating gestational age at birth from fundal height and additional anthropometrics: a prospective cohort study.

PubMed

Pugh, S J; Ortega-Villa, A M; Grobman, W; Newman, R B; Owen, J; Wing, D A; Albert, P S; Grantz, K L

2018-02-23

Accurate assessment of gestational age (GA) is critical to paediatric care, but is limited in developing countries without access to ultrasound. Our objectives were to assess the accuracy of prediction of GA at birth and preterm birth classification using routinely collected anthropometry measures. Prospective cohort study. United States. A total of 2334 non-obese and 468 obese pregnant women. Enrolment GA was determined based on last menstrual period, confirmed by first-trimester ultrasound. Maternal anthropometry and fundal height (FH) were measured by a standardised protocol at study visits; FH alone was additionally abstracted from medical charts. Neonatal anthropometry measurements were obtained at birth. To estimate GA at delivery, we developed three predictor models using longitudinal FH alone and with maternal and neonatal anthropometry. For all predictors, we repeatedly sampled observations to construct training (60%) and test (40%) sets. Linear mixed models incorporated longitudinal maternal anthropometry and a shared parameter model incorporated neonatal anthropometry. We assessed models' accuracy under varied scenarios. Estimated GA at delivery. Prediction error for various combinations of anthropometric measures ranged between 13.9 and 14.9 days. Longitudinal FH alone predicted GA within 14.9 days with relatively stable prediction errors across individual race/ethnicities [whites (13.9 days), blacks (15.1 days), Hispanics (15.5 days) and Asians (13.1 days)], and correctly identified 75% of preterm births. The model was robust to additional scenarios. In low-risk, non-obese women, longitudinal FH measures alone can provide a reasonably accurate assessment of GA when ultrasound measures are not available. Longitudinal fundal height alone predicts gestational age at birth when ultrasound measures are unavailable. © 2018 Royal College of Obstetricians and Gynaecologists.

1/f neural noise and electrophysiological indices of contextual prediction in aging.

PubMed

Dave, S; Brothers, T A; Swaab, T Y

2018-07-15

Prediction of upcoming words during reading has been suggested to enhance the efficiency of discourse processing. Emerging models have postulated that predictive mechanisms require synchronous firing of neural networks, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity - and thereby synchronous neuronal firing - is 1/f neural noise extracted from EEG spectral power. Previous research has indicated that this measure of 1/f neural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/f neural noise and whether this measure predicted ERP correlates of successful lexical prediction during discourse comprehension. 1/f neural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/f noise was a significant predictor of N400 effects of successful lexical prediction; however, noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/f noise across research populations. Copyright © 2018 Elsevier B.V. All rights reserved.

Prediction of Emotional Understanding and Emotion Regulation Skills of 4-5 Age Group Children with Parent-Child Relations

ERIC Educational Resources Information Center

Dereli, Esra

2016-01-01

The objective of the present study is to examine whether personal attributes, family characteristics of the child and parent-child relations predict children's emotional understanding and emotion regulation skills. The study was conducted with relational screening model, one of the screening models. Study sample included 423 children between the…

Leisure-Time Physical Activity and Academic Performance: Cross-Lagged Associations from Adolescence to Young Adulthood

PubMed Central

Aaltonen, Sari; Latvala, Antti; Rose, Richard J.; Kujala, Urho M.; Kaprio, Jaakko; Silventoinen, Karri

2016-01-01

Physical activity and academic performance are positively associated, but the direction of the association is poorly understood. This longitudinal study examined the direction and magnitude of the associations between leisure-time physical activity and academic performance throughout adolescence and young adulthood. The participants were Finnish twins (from 2,859 to 4,190 individuals/study wave) and their families. In a cross-lagged path model, higher academic performance at ages 12, 14 and 17 predicted higher leisure-time physical activity at subsequent time-points (standardized path coefficient at age 14: 0.07 (p < 0.001), age 17: 0.12 (p < 0.001) and age 24: 0.06 (p < 0.05)), whereas physical activity did not predict future academic performance. A cross-lagged model of co-twin differences suggested that academic performance and subsequent physical activity were not associated due to the environmental factors shared by co-twins. Our findings suggest that better academic performance in adolescence modestly predicts more frequent leisure-time physical activity in late adolescence and young adulthood. PMID:27976699

Leisure-Time Physical Activity and Academic Performance: Cross-Lagged Associations from Adolescence to Young Adulthood.

PubMed

Aaltonen, Sari; Latvala, Antti; Rose, Richard J; Kujala, Urho M; Kaprio, Jaakko; Silventoinen, Karri

2016-12-15

Physical activity and academic performance are positively associated, but the direction of the association is poorly understood. This longitudinal study examined the direction and magnitude of the associations between leisure-time physical activity and academic performance throughout adolescence and young adulthood. The participants were Finnish twins (from 2,859 to 4,190 individuals/study wave) and their families. In a cross-lagged path model, higher academic performance at ages 12, 14 and 17 predicted higher leisure-time physical activity at subsequent time-points (standardized path coefficient at age 14: 0.07 (p < 0.001), age 17: 0.12 (p < 0.001) and age 24: 0.06 (p < 0.05)), whereas physical activity did not predict future academic performance. A cross-lagged model of co-twin differences suggested that academic performance and subsequent physical activity were not associated due to the environmental factors shared by co-twins. Our findings suggest that better academic performance in adolescence modestly predicts more frequent leisure-time physical activity in late adolescence and young adulthood.

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

Smith, Kandler A; Santhanagopalan, Shriram; Yang, Chuanbo

Computer models are helping to accelerate the design and validation of next generation batteries and provide valuable insights not possible through experimental testing alone. Validated 3-D physics-based models exist for predicting electrochemical performance, thermal and mechanical response of cells and packs under normal and abuse scenarios. The talk describes present efforts to make the models better suited for engineering design, including improving their computation speed, developing faster processes for model parameter identification including under aging, and predicting the performance of a proposed electrode material recipe a priori using microstructure models.

Use of visible and near-infrared spectroscopy to predict pork longissimus lean color stability.

PubMed

King, D A; Shackelford, S D; Wheeler, T L

2011-12-01

This study evaluated the use of visible and near-infrared (VISNIR) spectroscopy to predict lean color stability in pork loin chops. Spectra were collected immediately after and approximately 1 h after rib removal on 1,208 loins. Loins were aged for 14 d before a 2.54-cm chop was placed in simulated retail display. Spectra were collected on aged loins immediately after removal from the vacuum package and on chops 10 min after cutting. Instrumental color measurements [L*, a*, b*, hue angle, chroma, and E (overall color change)] were determined on d 0, 1, 7, 11, and 14 of display. Principal components analysis of display d 0 and 14 values of these traits identified a factor (first principal component; PC1) explaining 67% of the variance that was related to color change. Partial least squares regression was used to develop 3 models to predict PC1 values by using VISNIR spectra collected in the plant, on aged loins, and on chops. Loins with predicted PC1 values less than 0 were classified as having a stable color, whereas values greater than 0 were classified as having a labile lean color. Loins classified as stable by the in-plant model had smaller (P < 0.05) L* values than those classified as labile. Hue angle and ΔE values were less (P < 0.05) and a* and chroma values were greater (P < 0.05) after d 7 of display in loins predicted to have a stable color than in loins predicted to have a labile lean color. Similarly, chops from loins classified as stable using the aged loin model had smaller (P < 0.05) L* values than those from loins classified as labile. Furthermore, loins predicted to be stable had smaller (P < 0.05) hue angle and ΔE values and greater (P < 0.05) a* and chroma values after d 7 of display than did loins predicted to be labile. Results for the chop model were similar to those from the 2 loin models. Chops predicted to have a stable lean color had smaller (P < 0.05) L* values than did those predicted to have a labile lean color. Chops classified as stable had smaller (P < 0.05) hue angle and ΔE values and greater (P < 0.05) a* and chroma values after d 7 of display compared with chops classified as labile. All 3 models effectively segregated chops based on color stability, particularly with regard to redness. Regardless of the model being used, d 14 display values for a*, hue angle, and ΔE in loins classified as stable were similar to the d 7 values of loins classified as labile. Thus, these results suggest that VISNIR spectroscopy would be an effective technology for sorting pork loins with regard to lean color stability.

The burden of typhoid fever in low- and middle-income countries: A meta-regression approach.

PubMed

Antillón, Marina; Warren, Joshua L; Crawford, Forrest W; Weinberger, Daniel M; Kürüm, Esra; Pak, Gi Deok; Marks, Florian; Pitzer, Virginia E

2017-02-01

Upcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data. We developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model. We estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9-48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2-4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models. Recent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases.

A fiber-based constitutive model predicts changes in amount and organization of matrix proteins with development and disease in the mouse aorta

PubMed Central

Cheng, Jeffrey K.; Stoilov, Ivan; Mecham, Robert P.

2013-01-01

Decreased elastin in mice (Eln+/−) yields a functioning vascular system with elevated blood pressure and increased arterial stiffness that is morphologically distinct from wild-type mice (WT). Yet, function is retained enough that there is no appreciable effect on life span and some mechanical properties are maintained constant. It is not understood how the mouse modifies the normal developmental process to produce a functioning vascular system despite a deficiency in elastin. To quantify changes in mechanical properties, we have applied a fiber-based constitutive model to mechanical data from the ascending aorta during postnatal development of WT and Eln+/− mice. Results indicate that the fiber-based constitutive model is capable of distinguishing elastin amounts and identifying trends during development. We observe an increase in predicted circumferential stress contribution from elastin with age, which correlates with increased elastin amounts from protein quantification data. The model also predicts changes in the unloaded collagen fiber orientation with age, which must be verified in future work. In Eln+/− mice, elastin amounts are decreased at each age, along with the predicted circumferential stress contribution of elastin. Collagen amounts in Eln+/− aorta are comparable to WT, but the predicted circumferential stress contribution of collagen is increased. This may be due to altered organization or structure of the collagen fibers. Relating quantifiable changes in arterial mechanics with changes in extracellular matrix (ECM) protein amounts will help in understanding developmental remodeling and in producing treatments for human diseases affecting ECM proteins. PMID:22790326

Growth model for uneven-aged loblolly pine stands : simulations and management implications

Treesearch

C.-R. Lin; J. Buongiorno; Jeffrey P. Prestemon; K. E. Skog

1998-01-01

A density-dependent matrix growth model of uneven-aged loblolly pine stands was developed with data from 991 permanent plots in the southern United States. The model predicts the number of pine, soft hardwood, and hard hardwood trees in 13 diameter classes, based on equations for ingrowth, upgrowth, and mortality. Projections of 6 to 10 years agreed with the growth...

Deterioration, death and the evolution of reproductive restraint in late life.

PubMed

McNamara, John M; Houston, Alasdair I; Barta, Zoltan; Scheuerlein, Alexander; Fromhage, Lutz

2009-11-22

Explaining why organisms schedule reproduction over their lifetimes in the various ways that they do is an enduring challenge in biology. An influential theoretical prediction states that organisms should increasingly invest in reproduction as they approach the end of their life. An apparent mismatch of empirical data with this prediction has been attributed to age-related constraints on the ability to reproduce. Here we present a general framework for the evolution of age-related reproductive trajectories. Instead of characterizing an organism by its age, we characterize it by its physiological condition. We develop a common currency that if maximized at each time guarantees the whole life history is optimal. This currency integrates reproduction, mortality and changes in condition. We predict that under broad conditions it will be optimal for organisms to invest less in reproduction as they age, thus challenging traditional interpretations of age-related traits and renewing debate about the extent to which observed life histories are shaped by constraint versus adaptation. Our analysis gives a striking illustration of the differences between an age-based and a condition-based approach to life-history theory. It also provides a unified account of not only standard life-history models but of related models involving the allocation of limited resources.

Initial Cognitive Performance Predicts Longitudinal Aviator Performance

PubMed Central

Jo, Booil; Adamson, Maheen M.; Kennedy, Quinn; Noda, Art; Hernandez, Beatriz; Zeitzer, Jamie M.; Friedman, Leah F.; Fairchild, Kaci; Scanlon, Blake K.; Murphy, Greer M.; Taylor, Joy L.

2011-01-01

Objectives. The goal of the study was to improve prediction of longitudinal flight simulator performance by studying cognitive factors that may moderate the influence of chronological age. Method. We examined age-related change in aviation performance in aircraft pilots in relation to baseline cognitive ability measures and aviation expertise. Participants were aircraft pilots (N = 276) aged 40–77.9. Flight simulator performance and cognition were tested yearly; there were an average of 4.3 (± 2.7; range 1–13) data points per participant. Each participant was classified into one of the three levels of aviation expertise based on Federal Aviation Administration pilot proficiency ratings: least, moderate, or high expertise. Results. Addition of measures of cognitive processing speed and executive function to a model of age-related change in aviation performance significantly improved the model. Processing speed and executive function performance interacted such that the slowest rate of decline in flight simulator performance was found in aviators with the highest scores on tests of these abilities. Expertise was beneficial to pilots across the age range studied; however, expertise did not show evidence of reducing the effect of age. Discussion. These data suggest that longitudinal performance on an important real-world activity can be predicted by initial assessment of relevant cognitive abilities. PMID:21586627

Early Executive Function at Age Two Predicts Emergent Mathematics and Literacy at Age Five

PubMed Central

Mulder, Hanna; Verhagen, Josje; Van der Ven, Sanne H. G.; Slot, Pauline L.; Leseman, Paul P. M.

2017-01-01

Previous work has shown that individual differences in executive function (EF) are predictive of academic skills in preschoolers, kindergartners, and older children. Across studies, EF is a stronger predictor of emergent mathematics than literacy. However, research on EF in children below age three is scarce, and it is currently unknown whether EF, as assessed in toddlerhood, predicts emergent academic skills a few years later. This longitudinal study investigates whether early EF, assessed at two years, predicts (emergent) academic skills, at five years. It examines, furthermore, whether early EF is a significantly stronger predictor of emergent mathematics than of emergent literacy, as has been found in previous work on older children. A sample of 552 children was assessed on various EF and EF-precursor tasks at two years. At age five, these children performed several emergent mathematics and literacy tasks. Structural Equation Modeling was used to investigate the relationships between early EF and academic skills, modeled as latent factors. Results showed that early EF at age two was a significant and relatively strong predictor of both emergent mathematics and literacy at age five, after controlling for receptive vocabulary, parental education, and home language. Predictive relations were significantly stronger for mathematics than literacy, but only when a verbal short-term memory measure was left out as an indicator to the latent early EF construct. These findings show that individual differences in emergent academic skills just prior to entry into the formal education system can be traced back to individual differences in early EF in toddlerhood. In addition, these results highlight the importance of task selection when assessing early EF as a predictor of later outcomes, and call for further studies to elucidate the mechanisms through which individual differences in early EF and precursors to EF come about. PMID:29075209

Early Executive Function at Age Two Predicts Emergent Mathematics and Literacy at Age Five.

PubMed

Mulder, Hanna; Verhagen, Josje; Van der Ven, Sanne H G; Slot, Pauline L; Leseman, Paul P M

2017-01-01

Previous work has shown that individual differences in executive function (EF) are predictive of academic skills in preschoolers, kindergartners, and older children. Across studies, EF is a stronger predictor of emergent mathematics than literacy. However, research on EF in children below age three is scarce, and it is currently unknown whether EF, as assessed in toddlerhood, predicts emergent academic skills a few years later. This longitudinal study investigates whether early EF, assessed at two years, predicts (emergent) academic skills, at five years. It examines, furthermore, whether early EF is a significantly stronger predictor of emergent mathematics than of emergent literacy, as has been found in previous work on older children. A sample of 552 children was assessed on various EF and EF-precursor tasks at two years. At age five, these children performed several emergent mathematics and literacy tasks. Structural Equation Modeling was used to investigate the relationships between early EF and academic skills, modeled as latent factors. Results showed that early EF at age two was a significant and relatively strong predictor of both emergent mathematics and literacy at age five, after controlling for receptive vocabulary, parental education, and home language. Predictive relations were significantly stronger for mathematics than literacy, but only when a verbal short-term memory measure was left out as an indicator to the latent early EF construct. These findings show that individual differences in emergent academic skills just prior to entry into the formal education system can be traced back to individual differences in early EF in toddlerhood. In addition, these results highlight the importance of task selection when assessing early EF as a predictor of later outcomes, and call for further studies to elucidate the mechanisms through which individual differences in early EF and precursors to EF come about.

Estimating the color of maxillary central incisors based on age and gender

PubMed Central

Gozalo-Diaz, David; Johnston, William M.; Wee, Alvin G.

2008-01-01

Statement of problem There is no scientific information regarding the selection of the color of teeth for edentulous patients. Purpose The purpose of this study was to evaluate linear regression models that may be used to predict color parameters for central incisors of edentulous patients based on some characteristics of dentate subjects. Material and methods A spectroradiometer and an external light source were set in a noncontacting 45/0 degree (45-degree illumination and 0-degree observer) optical configuration to measure the color of subjects’ vital craniofacial structures (maxillary central incisor, attached gingiva, and facial skin). The subjects (n=120) were stratified into 5 age groups with 4 racial groups and balanced for gender. Linear first-order regression was used to determine the significant factors (α=.05) in the prediction model for each color direction of the color of the maxillary central incisor. Age, gender, and color of the other craniofacial structures were studied as potential predictors. Final predictions in each color direction were based only on the statistically significant factors, and then the color differences between observed and predicted CIELAB values for the central incisors were calculated and summarized. Results The statistically significant predictors of age and gender accounted for 36% of the total variability in L*. The statistically significant predictor of age accounted for 16% of the total variability in a*. The statistically significant predictors of age and gender accounted for 21% of the variability in b*. The mean ΔE (SD) between predicted and observed CIELAB values for the central incisor was 5.8 (3.2). Conclusions Age and gender were found to be statistically significant determinants in predicting the natural color of central incisors. Although the precision of these predictions was less than the median color difference found for all pairs of teeth studied, and may be considered an acceptable precision, further study is needed to reduce this precision to the limit of detection. Clinical Implications Age is highly correlated with the natural color of the central incisors. When age increases, the central incisor becomes darker, more reddish, and more yellow. Also, the women subjects in this study had lighter and less yellow central incisors than the men. PMID:18672125

Goal clarity and financial planning activities as determinants of retirement savings contributions.

PubMed

Stawski, Robert S; Hershey, Douglas A; Jacobs-Lawson, Joy M

2007-01-01

Retirement counselors, financial service professionals, and retirement intervention specialists routinely emphasize the importance of developing clear goals for the future; however, few empirical studies have focused on the benefits of retirement goal setting. In the present study, the extent to which goal clarity and financial planning activities predict retirement savings practices was examined among 100 working adults. Path analysis techniques were used to test two competing models, both of which were designed to predict savings contributions. Findings provide support for the model in which retirement goal clarity is a significant predictor of planning practices, and planning, in turn, predicts savings tendencies. Two demographic variables-income and age-were also revealed to be important elements of the model, with income accounting for roughly half of the explained variance in savings contributions. The results of this study have implications for the development of age-based models of planning, as well as implications for retirement counselors and financial planners who advise workers on long-term saving strategies.

A model to predict disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD): the ADPKD Outcomes Model.

PubMed

McEwan, Phil; Bennett Wilton, Hayley; Ong, Albert C M; Ørskov, Bjarne; Sandford, Richard; Scolari, Francesco; Cabrera, Maria-Cristina V; Walz, Gerd; O'Reilly, Karl; Robinson, Paul

2018-02-13

Autosomal dominant polycystic kidney disease (ADPKD) is the leading inheritable cause of end-stage renal disease (ESRD); however, the natural course of disease progression is heterogeneous between patients. This study aimed to develop a natural history model of ADPKD that predicted progression rates and long-term outcomes in patients with differing baseline characteristics. The ADPKD Outcomes Model (ADPKD-OM) was developed using available patient-level data from the placebo arm of the Tolvaptan Efficacy and Safety in Management of ADPKD and its Outcomes Study (TEMPO 3:4; ClinicalTrials.gov identifier NCT00428948). Multivariable regression equations estimating annual rates of ADPKD progression, in terms of total kidney volume (TKV) and estimated glomerular filtration rate, formed the basis of the lifetime patient-level simulation model. Outputs of the ADPKD-OM were compared against external data sources to validate model accuracy and generalisability to other ADPKD patient populations, then used to predict long-term outcomes in a cohort matched to the overall TEMPO 3:4 study population. A cohort with baseline patient characteristics consistent with TEMPO 3:4 was predicted to reach ESRD at a mean age of 52 years. Most patients (85%) were predicted to reach ESRD by the age of 65 years, with many progressing to ESRD earlier in life (18, 36 and 56% by the age of 45, 50 and 55 years, respectively). Consistent with previous research and clinical opinion, analyses supported the selection of baseline TKV as a prognostic factor for ADPKD progression, and demonstrated its value as a strong predictor of future ESRD risk. Validation exercises and illustrative analyses confirmed the ability of the ADPKD-OM to accurately predict disease progression towards ESRD across a range of clinically-relevant patient profiles. The ADPKD-OM represents a robust tool to predict natural disease progression and long-term outcomes in ADPKD patients, based on readily available and/or measurable clinical characteristics. In conjunction with clinical judgement, it has the potential to support decision-making in research and clinical practice.

Predicting Adolescents' Bullying Participation from Developmental Trajectories of Social Status and Behavior.

PubMed

Pouwels, J Loes; Salmivalli, Christina; Saarento, Silja; van den Berg, Yvonne H M; Lansu, Tessa A M; Cillessen, Antonius H N

2017-03-28

The aim of this study was to determine how trajectory clusters of social status (social preference and perceived popularity) and behavior (direct aggression and prosocial behavior) from age 9 to age 14 predicted adolescents' bullying participant roles at age 16 and 17 (n = 266). Clusters were identified with multivariate growth mixture modeling (GMM). The findings showed that participants' developmental trajectories of social status and social behavior across childhood and early adolescence predicted their bullying participant role involvement in adolescence. Practical implications and suggestions for further research are discussed. © 2017 The Authors. Child Development published by Wiley Periodicals, Inc. on behalf of Society for Research in Child Development.

Dual Pathways from Reactive Aggression to Depressive Symptoms in Children: Further Examination of the Failure Model.

PubMed

Evans, Spencer C; Fite, Paula J

2018-04-13

The failure model posits that peer rejection and poor academic performance are dual pathways in the association between early aggressive behavior and subsequent depressive symptoms. We examined this model using an accelerated longitudinal design while also incorporating proactive and reactive aggression and gender moderation. Children in 1st, 3rd, and 5th grades (n = 912; ages 6-12; 48% female) were rated three times annually by their primary teachers on measures of proactive and reactive aggression, peer rejection, academic performance, and depressive symptoms. Using Bayesian cross-classified estimation to account for nested and planned-missing data, path models were estimated to examine whether early reactive aggression predicted subsequent peer rejection and academic performance, and whether these, in turn, predicted subsequent depressive symptoms. From 1st to 3rd grade, reactive aggression predicted peer rejection (not academic performance), proactive aggression predicted academic performance (not peer rejection), and academic performance and peer rejection both predicted depressive symptoms. From 3rd to 5th grade, however, neither peer rejection nor academic performance predicted subsequent depressive symptoms. Results were not moderated by gender. Overall, these findings provide mixed and limited support for the failure model among school-age children. Early reactive aggression may be a key risk factor for social problems, whereas proactive aggression may be linked to improved academic functioning. The "dual pathways" of peer rejection and academic performance may operate during early but not later elementary school. Limitations and implications are discussed.

Can stress biomarkers predict preterm birth in women with threatened preterm labor?

PubMed

García-Blanco, Ana; Diago, Vicente; Serrano De La Cruz, Verónica; Hervás, David; Cháfer-Pericás, Consuelo; Vento, Máximo

2017-09-01

Preterm birth is a major paediatric challenge difficult to prevent and with major adverse outcomes. Prenatal stress plays an important role on preterm birth; however, there are few stress-related models to predict preterm birth in women with Threatened Preterm Labor (TPL). The aim of this work is to study the influence of stress biomarkers on time until birth in TPL women. Eligible participants were pregnant women between 24 and 31 gestational weeks admitted to the hospital with TPL diagnosis (n=166). Stress-related biomarkers (α-amylase and cortisol) were determined in saliva samples after TPL diagnosis. Participants were followed-up until labor. A parametric survival model was constructed based on α-amylase, cortisol), TPL gestational week, age, parity, and multiple pregnancy. The model was adjusted using a logistic distribution and it was implemented as a nomogram to predict the labor probability at 7- and 14-day term. The time until labor was associated with cortisol (p=0.001), gestational week at TPL diagnosis (p=0.004), and age (p=0.02). Importantly, high cortisol levels at TPL diagnosis were predictive of latency to labor. Validation of the model yielded an optimum corrected AUC value of 0.63. High cortisol levels at TPL diagnosis may have an important role in the preterm birth prediction. Our statistical model implemented as a nomogram provided accurate predictions of individual prognosis of pregnant women. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-02-01

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

Age of first use and ongoing patterns of legal and illegal drug use in a sample of young Londoners.

PubMed

McCambridge, Jim; Strang, John

2005-01-01

The significance of ages of first use of cigarettes, alcohol, cannabis, and stimulant drugs were investigated in a sample of young drug users entering an intervention study in London. Age of first cigarette smoking emerges as a robust predictor of age of first cannabis use, and age of first cannabis use in turn is predictive of age of first stimulant use, among those using both drugs. In this sample, ages of first use of cigarettes, alcohol, and cannabis are not predictive of whether stimulant drugs are used. In a series of regression models that take account of the influence of other factors, age of first use is found to have no relationship to levels of ongoing consumption of cigarettes, alcohol, and cannabis.

Cardiovascular risk factors predictive for survival and morbidity-free survival in the oldest-old Framingham Heart Study participants.

PubMed

Terry, Dellara F; Pencina, Michael J; Vasan, Ramachandran S; Murabito, Joanne M; Wolf, Philip A; Hayes, Margaret Kelly; Levy, Daniel; D'Agostino, Ralph B; Benjamin, Emelia J

2005-11-01

To examine whether midlife cardiovascular risk factors predict survival and survival free of major comorbidities to the age of 85. Prospective community-based cohort study. Framingham Heart Study, Massachusetts. Two thousand five hundred thirty-one individuals (1,422 women) who attended at least two examinations between the ages of 40 and 50. Risk factors were classified at routine examinations performed between the ages of 40 and 50. Stepwise sex-adjusted logistic regression models predicting the outcomes of survival and survival free of morbidity to age 85 were selected from the following risk factors: systolic and diastolic blood pressure, total serum cholesterol, glucose intolerance, cigarette smoking, education, body mass index, physical activity index, pulse pressure, antihypertensive medication, and electrocardiographic left ventricular hypertrophy. More than one-third of the study sample survived to age 85, and 22% of the original study sample survived free of morbidity. Lower midlife blood pressure and total cholesterol levels, absence of glucose intolerance, nonsmoking status, higher educational attainment, and female sex predicted overall and morbidity-free survival. The predicted probability of survival to age 85 fell in the presence of accumulating risk factors: 37% for men with no risk factors to 2% with all five risk factors and 65% for women with no risk factors to 14% with all five risk factors. Lower levels of key cardiovascular risk factors in middle age predicted overall survival and major morbidity-free survival to age 85. Recognizing and modifying these factors may delay, if not prevent, age-related morbidity and mortality.

Parental depressive history, parenting styles, and child psychopathology over 6 years: The contribution of each parent's depressive history to the other's parenting styles.

PubMed

Kopala-Sibley, Daniel C; Jelinek, Caitlin; Kessel, Ellen M; Frost, Allison; Allmann, Anna E S; Klein, Daniel N

2017-10-01

The link between parental depressive history and parenting styles is well established, as is the association of parenting with child psychopathology. However, little research has examined whether a depressive history in one parent predicts the parenting style of the other parent. As well, relatively little research has tested transactional models of the parenting-child psychopathology relationship in the context of parents' depressive histories. In this study, mothers and fathers of 392 children were assessed for a lifetime history of major depression when their children were 3 years old. They then completed measures of permissiveness and authoritarianism and their child's internalizing and externalizing symptoms when children were 3, 6, and 9 years old. The results showed that a depressive history in one parent predicted the other parent's permissiveness. Analyses then showed that child externalizing symptoms at age 3 predicted maternal permissiveness and authoritarianism and paternal permissiveness at age 6. Maternal permissiveness at age 6 predicted child externalizing symptoms at age 9. No relationships in either direction were found between parenting styles and child internalizing symptoms. The results highlight the importance of considering both parents' depressive histories when understanding parenting styles, and support transactional models of parenting styles and child externalizing symptoms.

Parental depressive history, parenting styles, and child psychopathology over six years: The contribution of each parent’s depressive history to the other’s parenting styles

PubMed Central

Kopala-Sibley, Daniel C.; Jelinek, Caitlin; Kessel, Ellen; Frost, Allison; Allmann, Anna E.S.; Klein, Daniel N.

2017-01-01

The link between parental depressive history and parenting styles is well established, as is the association of parenting with child psychopathology. However, little research has examined whether a depressive history in one parent predicts the parenting style of the other parent. As well, relatively little research has tested transactional models of the parenting-child psychopathology relationship in the context of parents’ depressive histories. In this study, mothers and fathers of 392 children were assessed for a lifetime history of major depression when their children were 3 years old. They then completed measures of permissiveness and authoritarianism and their child’s internalizing and externalizing symptoms when children were 3, 6, and 9 years old. Results showed that a depressive history in one parent predicted the other parent’s permissiveness. Analyses then showed that child externalizing symptoms at age 3 predicted maternal permissiveness and authoritarianism and paternal permissiveness at age 6. Maternal permissiveness at age 6 predicted child externalizing symptoms at age 9. No relationships in either direction were found between parenting styles and child internalizing symptoms. Results highlight the importance of considering both parents’ depressive histories when understanding parenting styles, and support transactional models of parenting styles and child externalizing symptoms. PMID:28414019

The cardiovascular event reduction tool (CERT)--a simplified cardiac risk prediction model developed from the West of Scotland Coronary Prevention Study (WOSCOPS).

PubMed

L'Italien, G; Ford, I; Norrie, J; LaPuerta, P; Ehreth, J; Jackson, J; Shepherd, J

2000-03-15

The clinical decision to treat hypercholesterolemia is premised on an awareness of patient risk, and cardiac risk prediction models offer a practical means of determining such risk. However, these models are based on observational cohorts where estimates of the treatment benefit are largely inferred. The West of Scotland Coronary Prevention Study (WOSCOPS) provides an opportunity to develop a risk-benefit prediction model from the actual observed primary event reduction seen in the trial. Five-year Cox model risk estimates were derived from all WOSCOPS subjects (n = 6,595 men, aged 45 to 64 years old at baseline) using factors previously shown to be predictive of definite fatal coronary heart disease or nonfatal myocardial infarction. Model risk factors included age, diastolic blood pressure, total cholesterol/ high-density lipoprotein ratio (TC/HDL), current smoking, diabetes, family history of fatal coronary heart disease, nitrate use or angina, and treatment (placebo/ 40-mg pravastatin). All risk factors were expressed as categorical variables to facilitate risk assessment. Risk estimates were incorporated into a simple, hand-held slide rule or risk tool. Risk estimates were identified for 5-year age bands (45 to 65 years), 4 categories of TC/HDL ratio (<5.5, 5.5 to <6.5, 6.5 to <7.5, > or = 7.5), 2 levels of diastolic blood pressure (<90, > or = 90 mm Hg), from 0 to 3 additional risk factors (current smoking, diabetes, family history of premature fatal coronary heart disease, nitrate use or angina), and pravastatin treatment. Five-year risk estimates ranged from 2% in very low-risk subjects to 61% in the very high-risk subjects. Risk reduction due to pravastatin treatment averaged 31%. Thus, the Cardiovascular Event Reduction Tool (CERT) is a risk prediction model derived from the WOSCOPS trial. Its use will help physicians identify patients who will benefit from cholesterol reduction.

Inflammation, But Not Telomere Length, Predicts Successful Ageing at Extreme Old Age: A Longitudinal Study of Semi-supercentenarians

PubMed Central

Arai, Yasumichi; Martin-Ruiz, Carmen M.; Takayama, Michiyo; Abe, Yukiko; Takebayashi, Toru; Koyasu, Shigeo; Suematsu, Makoto; Hirose, Nobuyoshi; von Zglinicki, Thomas

2015-01-01

To determine the most important drivers of successful ageing at extreme old age, we combined community-based prospective cohorts: Tokyo Oldest Old Survey on Total Health (TOOTH), Tokyo Centenarians Study (TCS) and Japanese Semi-Supercentenarians Study (JSS) comprising 1554 individuals including 684 centenarians and (semi-)supercentenarians, 167 pairs of centenarian offspring and spouses, and 536 community-living very old (85 to 99 years). We combined z scores from multiple biomarkers to describe haematopoiesis, inflammation, lipid and glucose metabolism, liver function, renal function, and cellular senescence domains. In Cox proportional hazard models, inflammation predicted all-cause mortality with hazard ratios (95% CI) 1.89 (1.21 to 2.95) and 1.36 (1.05 to 1.78) in the very old and (semi-)supercentenarians, respectively. In linear forward stepwise models, inflammation predicted capability (10.8% variance explained) and cognition (8.6% variance explained) in (semi-)supercentenarians better than chronologic age or gender. The inflammation score was also lower in centenarian offspring compared to age-matched controls with Δ (95% CI) = − 0.795 (− 1.436 to − 0.154). Centenarians and their offspring were able to maintain long telomeres, but telomere length was not a predictor of successful ageing in centenarians and semi-supercentenarians. We conclude that inflammation is an important malleable driver of ageing up to extreme old age in humans. PMID:26629551

Predicting relatedness and self-definition depressive experiences in aging women based on personality traits: a preliminary study.

PubMed

Henriques-Calado, Joana; Duarte-Silva, Maria Eugénia; Campos, Rui C; Sacoto, Carlota; Keong, Ana Marta; Junqueira, Diana

2013-01-01

As part of the research relating personality and depression, this study seeks to predict depressive experiences in aging women according to Sidney Blatt's perspective based on the Five-Factor Model of Personality. The NEO-Five Factor Inventory and the Depressive Experiences Questionnaire were administered. The domains Neuroticism, Agreeableness, and Conscientiousness predicted self-criticism, explaining 68% of the variance; the domains Neuroticism and Extraversion predicted dependency, explaining 62% of the variance. The subfactors Neediness and Connectedness were differently related to personality traits. These findings are relevant to the research relating personality and anaclitic / introjective depressive experiences in late adulthood.

Age and Comorbid Illness Are Associated With Late Rectal Toxicity Following Dose-Escalated Radiation Therapy for Prostate Cancer

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

Hamstra, Daniel A.; Stenmark, Matt H.; Ritter, Tim

2013-04-01

Purpose: To assess the impacts of patient age and comorbid illness on rectal toxicity following external beam radiation therapy (EBRT) for prostate cancer and to assess the Qualitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) normal tissue complication probability (NTCP) model in this context. Methods and Materials: Rectal toxicity was analyzed in 718 men previously treated for prostate cancer with EBRT (≥75 Gy). Comorbid illness was scored using the Charlson Comorbidity Index (CCMI), and the NTCP was evaluated with the QUANTEC model. The influence of clinical and treatment-related parameters on rectal toxicity was assessed by Kaplan-Meier and Coxmore » proportional hazards models. Results: The cumulative incidence of rectal toxicity grade ≥2 was 9.5% and 11.6% at 3 and 5 years and 3.3% and 3.9% at 3 and 5 years for grade ≥3 toxicity, respectively. Each year of age predicted an increasing relative risk of grade ≥2 (P<.03; hazard ratio [HR], 1.04 [95% confidence interval (CI), 1.01-1.06]) and ≥3 rectal toxicity (P<.0001; HR, 1.14 [95% CI,1.07-1.22]). Increasing CCMI predicted rectal toxicity where a history of either myocardial infarction (MI) (P<.0001; HR, 5.1 [95% CI, 1.9-13.7]) or congestive heart failure (CHF) (P<.0006; HR, 5.4 [95% CI, 0.6-47.5]) predicted grade ≥3 rectal toxicity, with lesser correlation with grade ≥2 toxicity (P<.02 for MI, and P<.09 for CHF). An age comorbidity model to predict rectal toxicity was developed and confirmed in a validation cohort. The use of anticoagulants increased toxicity independent of age and comorbidity. NTCP was prognostic for grade ≥3 (P=.015) but not grade ≥2 (P=.49) toxicity. On multivariate analysis, age, MI, CHF, and an NTCP >20% all correlated with late rectal toxicity. Conclusions: Patient age and a history of MI or CHF significantly impact rectal toxicity following EBRT for the treatment of prostate cancer, even after controlling for NTCP.« less

Variation in probability of first reproduction of Weddell seals

USGS Publications Warehouse

Hadley, G.L.; Rotella, J.J.; Garrott, R.A.; Nichols, J.D.

2006-01-01

Summary 1. For many species, when to begin reproduction is an important life-history decision that varies by individual and can have substantial implications for lifetime reproductive success and fitness. 2. We estimated age-specific probabilities of first-time breeding and modelled variation in these rates to determine age at first reproduction and understand why it varies in a population of Weddell seals in Erebus Bay, Antarctica. We used multistate mark?recapture modelling methods and encounter histories of 4965 known-age female seals to test predictions about age-related variation in probability of first reproduction and the effects of annual variation, cohort and population density. 3. Mean age at first reproduction in this southerly located study population (7.62 years of age, SD =1.71) was greater than age at first reproduction for a Weddell seal population at a more northerly and typical latitude for breeding Weddell seals (mean =4?5 years of age). This difference suggests that age at first reproduction may be influenced by whether a population inhabits the core or periphery of its range. 4. Age at first reproduction varied from 4 to 14 years, but there was no age by which all seals recruited to the breeding population, suggesting that individual heterogeneity exists among females in this population. 5. In the best model, the probability of breeding for the first time varied by age and year, and the amount of annual variation varied with age (average variance ratio for age-specific rates =4.3%). 6. Our results affirmed the predictions of life-history theory that age at first reproduction in long-lived mammals will be sensitive to environmental variation. In terms of life history evolution, this variability suggests that Weddell seals display flexibility in age at first reproduction in order to maximize reproductive output under varying environmental conditions. Future analyses will attempt to test predictions regarding relationships between environmental covariates and annual variation in age at first reproduction and evaluate the relationship between age at first reproduction and lifetime reproductive success.

Longitudinal Change in Happiness during Aging: The Predictive Role of Positive Expectancies

ERIC Educational Resources Information Center

Holahan, Carole K.; Holahan, Charles J.; Velasquez, Katherine E.; North, Rebecca J.

2008-01-01

This study employed hierarchical linear modeling to document the time course of happiness across 20 years from average ages of 66 to 86 among 717 members of the Terman Study of the Gifted. In addition, the study examined the role of positive expectancies about aging, assessed at an average age of 61, in enhancing happiness in aging. The results…

Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye

PubMed Central

Yoshioka, Nayuta; Zangerl, Barbara; Nivison-Smith, Lisa; Khuu, Sieu K.; Jones, Bryan W.; Pfeiffer, Rebecca L.; Marc, Robert E.; Kalloniatis, Michael

2017-01-01

Purpose To characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease. Methods Single eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20–85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort. Results Pattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition–derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and −0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm). Conclusions Pattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease. PMID:28632847

Predictive model for survival in patients with gastric cancer.

PubMed

Goshayeshi, Ladan; Hoseini, Benyamin; Yousefli, Zahra; Khooie, Alireza; Etminani, Kobra; Esmaeilzadeh, Abbas; Golabpour, Amin

2017-12-01

Gastric cancer is one of the most prevalent cancers in the world. Characterized by poor prognosis, it is a frequent cause of cancer in Iran. The aim of the study was to design a predictive model of survival time for patients suffering from gastric cancer. This was a historical cohort conducted between 2011 and 2016. Study population were 277 patients suffering from gastric cancer. Data were gathered from the Iranian Cancer Registry and the laboratory of Emam Reza Hospital in Mashhad, Iran. Patients or their relatives underwent interviews where it was needed. Missing values were imputed by data mining techniques. Fifteen factors were analyzed. Survival was addressed as a dependent variable. Then, the predictive model was designed by combining both genetic algorithm and logistic regression. Matlab 2014 software was used to combine them. Of the 277 patients, only survival of 80 patients was available whose data were used for designing the predictive model. Mean ?SD of missing values for each patient was 4.43?.41 combined predictive model achieved 72.57% accuracy. Sex, birth year, age at diagnosis time, age at diagnosis time of patients' family, family history of gastric cancer, and family history of other gastrointestinal cancers were six parameters associated with patient survival. The study revealed that imputing missing values by data mining techniques have a good accuracy. And it also revealed six parameters extracted by genetic algorithm effect on the survival of patients with gastric cancer. Our combined predictive model, with a good accuracy, is appropriate to forecast the survival of patients suffering from Gastric cancer. So, we suggest policy makers and specialists to apply it for prediction of patients' survival.

Anthropometric measures in cardiovascular disease prediction: comparison of laboratory-based versus non-laboratory-based model.

PubMed

Dhana, Klodian; Ikram, M Arfan; Hofman, Albert; Franco, Oscar H; Kavousi, Maryam

2015-03-01

Body mass index (BMI) has been used to simplify cardiovascular risk prediction models by substituting total cholesterol and high-density lipoprotein cholesterol. In the elderly, the ability of BMI as a predictor of cardiovascular disease (CVD) declines. We aimed to find the most predictive anthropometric measure for CVD risk to construct a non-laboratory-based model and to compare it with the model including laboratory measurements. The study included 2675 women and 1902 men aged 55-79 years from the prospective population-based Rotterdam Study. We used Cox proportional hazard regression analysis to evaluate the association of BMI, waist circumference, waist-to-hip ratio and a body shape index (ABSI) with CVD, including coronary heart disease and stroke. The performance of the laboratory-based and non-laboratory-based models was evaluated by studying the discrimination, calibration, correlation and risk agreement. Among men, ABSI was the most informative measure associated with CVD, therefore ABSI was used to construct the non-laboratory-based model. Discrimination of the non-laboratory-based model was not different than laboratory-based model (c-statistic: 0.680-vs-0.683, p=0.71); both models were well calibrated (15.3% observed CVD risk vs 16.9% and 17.0% predicted CVD risks by the non-laboratory-based and laboratory-based models, respectively) and Spearman rank correlation and the agreement between non-laboratory-based and laboratory-based models were 0.89 and 91.7%, respectively. Among women, none of the anthropometric measures were independently associated with CVD. Among middle-aged and elderly where the ability of BMI to predict CVD declines, the non-laboratory-based model, based on ABSI, could predict CVD risk as accurately as the laboratory-based model among men. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts

PubMed Central

Genders, Tessa S S; Steyerberg, Ewout W; Nieman, Koen; Galema, Tjebbe W; Mollet, Nico R; de Feyter, Pim J; Krestin, Gabriel P; Alkadhi, Hatem; Leschka, Sebastian; Desbiolles, Lotus; Meijs, Matthijs F L; Cramer, Maarten J; Knuuti, Juhani; Kajander, Sami; Bogaert, Jan; Goetschalckx, Kaatje; Cademartiri, Filippo; Maffei, Erica; Martini, Chiara; Seitun, Sara; Aldrovandi, Annachiara; Wildermuth, Simon; Stinn, Björn; Fornaro, Jürgen; Feuchtner, Gudrun; De Zordo, Tobias; Auer, Thomas; Plank, Fabian; Friedrich, Guy; Pugliese, Francesca; Petersen, Steffen E; Davies, L Ceri; Schoepf, U Joseph; Rowe, Garrett W; van Mieghem, Carlos A G; van Driessche, Luc; Sinitsyn, Valentin; Gopalan, Deepa; Nikolaou, Konstantin; Bamberg, Fabian; Cury, Ricardo C; Battle, Juan; Maurovich-Horvat, Pál; Bartykowszki, Andrea; Merkely, Bela; Becker, Dávid; Hadamitzky, Martin; Hausleiter, Jörg; Dewey, Marc; Zimmermann, Elke; Laule, Michael

2012-01-01

Objectives To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations. Design Retrospective pooled analysis of individual patient data. Setting 18 hospitals in Europe and the United States. Participants Patients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively). Main outcome measures Obstructive coronary artery disease (≥50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model factors and use of the CT based coronary calcium score). We assessed discrimination (c statistic), calibration, and continuous net reclassification improvement by cross validation for the four largest low prevalence datasets separately and the smaller remaining low prevalence datasets combined. Results We included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended model was a major predictor (0.79 to 0.88, 102%). Calibration for low prevalence datasets was satisfactory. Conclusions Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates. PMID:22692650

External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches.

PubMed

Morales, Daniel R; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K; Zutis, Kris

2018-05-01

Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1-3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. The age-standardised mortality rate was 32.8 (95%CI 32.5-33.1) and 25.2 (95%CI 25.4-25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1-3 years. Comparable results were observed using SVM. In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Copyright © 2018. Published by Elsevier Ltd.

Deep phenotyping to predict live birth outcomes in in vitro fertilization

PubMed Central

Banerjee, Prajna; Choi, Bokyung; Shahine, Lora K.; Jun, Sunny H.; O’Leary, Kathleen; Lathi, Ruth B.; Westphal, Lynn M.; Wong, Wing H.; Yao, Mylene W. M.

2010-01-01

Nearly 75% of in vitro fertilization (IVF) treatments do not result in live births and patients are largely guided by a generalized age-based prognostic stratification. We sought to provide personalized and validated prognosis by using available clinical and embryo data from prior, failed treatments to predict live birth probabilities in the subsequent treatment. We generated a boosted tree model, IVFBT, by training it with IVF outcomes data from 1,676 first cycles (C1s) from 2003–2006, followed by external validation with 634 cycles from 2007–2008, respectively. We tested whether this model could predict the probability of having a live birth in the subsequent treatment (C2). By using nondeterministic methods to identify prognostic factors and their relative nonredundant contribution, we generated a prediction model, IVFBT, that was superior to the age-based control by providing over 1,000-fold improvement to fit new data (p < 0.05), and increased discrimination by receiver–operative characteristic analysis (area-under-the-curve, 0.80 vs. 0.68 for C1, 0.68 vs. 0.58 for C2). IVFBT provided predictions that were more accurate for ∼83% of C1 and ∼60% of C2 cycles that were out of the range predicted by age. Over half of those patients were reclassified to have higher live birth probabilities. We showed that data from a prior cycle could be used effectively to provide personalized and validated live birth probabilities in a subsequent cycle. Our approach may be replicated and further validated in other IVF clinics. PMID:20643955

A risk tertiles model for predicting mortality in patients with acute respiratory distress syndrome: age, plateau pressure, and P(aO(2))/F(IO(2)) at ARDS onset can predict mortality.

PubMed

Villar, Jesús; Pérez-Méndez, Lina; Basaldúa, Santiago; Blanco, Jesús; Aguilar, Gerardo; Toral, Darío; Zavala, Elizabeth; Romera, Miguel A; González-Díaz, Gumersindo; Nogal, Frutos Del; Santos-Bouza, Antonio; Ramos, Luís; Macías, Santiago; Kacmarek, Robert M

2011-04-01

Predicting mortality has become a necessary step for selecting patients for clinical trials and defining outcomes. We examined whether stratification by tertiles of respiratory and ventilatory variables at the onset of acute respiratory distress syndrome (ARDS) identifies patients with different risks of death in the intensive care unit. We performed a secondary analysis of data from 220 patients included in 2 multicenter prospective independent trials of ARDS patients mechanically ventilated with a lung-protective strategy. Using demographic, pulmonary, and ventilation data collected at ARDS onset, we derived and validated a simple prediction model based on a population-based stratification of variable values into low, middle, and high tertiles. The derivation cohort included 170 patients (all from one trial) and the validation cohort included 50 patients (all from a second trial). Tertile distribution for age, plateau airway pressure (P(plat)), and P(aO(2))/F(IO(2)) at ARDS onset identified subgroups with different mortalities, particularly for the highest-risk tertiles: age (> 62 years), P(plat) (> 29 cm H(2)O), and P(aO(2))/F(IO(2)) (< 112 mm Hg). Risk was defined by the number of coexisting high-risk tertiles: patients with no high-risk tertiles had a mortality of 12%, whereas patients with 3 high-risk tertiles had 90% mortality (P < .001). A prediction model based on tertiles of patient age, P(plat), and P(aO(2))/F(IO(2)) at the time the patient meets ARDS criteria identifies patients with the lowest and highest risk of intensive care unit death.

Derivation of genetic biomarkers for cancer risk stratification in Barrett's oesophagus: a prospective cohort study

PubMed Central

Timmer, Margriet R.; Martinez, Pierre; Lau, Chiu T.; Westra, Wytske M.; Calpe, Silvia; Rygiel, Agnieszka M.; Rosmolen, Wilda D.; Meijer, Sybren L.; ten Kate, Fiebo J.W.; Dijkgraaf, Marcel G.W.; Mallant-Hent, Rosalie C.; Naber, Anton H.J.; van Oijen, Arnoud H.A.M.; Baak, Lubbertus C.; Scholten, Pieter; Böhmer, Clarisse J.M.; Fockens, Paul; Maley, Carlo C.; Graham, Trevor A.; Bergman, Jacques J.G.H.M.; Krishnadath, Kausilia K.

2016-01-01

Objective The risk of developing adenocarcinoma in non-dysplastic Barrett's oesophagus is low and difficult to predict. Accurate tools for risk stratification are needed to increase the efficiency of surveillance. We aimed to develop a prediction model for progression using clinical variables and genetic markers. Methods In a prospective cohort of patients with non-dysplastic Barrett's oesophagus, we evaluated six molecular markers: p16, p53, Her-2/neu, 20q, MYC, and aneusomy by DNA fluorescence in situ hybridisation on brush cytology specimens. Primary study outcomes were the development of high-grade dysplasia or oesophageal adenocarcinoma. The most predictive clinical variables and markers were determined using Cox proportional-hazards models, receiver-operating-characteristic curves and a leave-one-out analysis. Results A total of 428 patients participated (345 men; median age 60 years) with a cumulative follow-up of 2019 patient-years (median 45 months per patient). Of these patients, 22 progressed; nine developed high-grade dysplasia and 13 oesophageal adenocarcinoma. The clinical variables, age and circumferential Barrett's length, and the markers, p16 loss, MYC gain, and aneusomy, were significantly associated with progression on univariate analysis. We defined an ‘Abnormal Marker Count’ that counted abnormalities in p16, MYC and aneusomy, which significantly improved risk prediction beyond using just age and Barrett's length. In multivariate analysis, these three factors identified a high-risk group with an 8.7-fold (95% CI, 2.6 to 29.8) increased hazard ratio compared with the low-risk group, with an area under the curve of 0.76 (95% CI, 0.66 to 0.86). Conclusion A prediction model based on age, Barrett's length, and the markers p16, MYC, and aneusomy determines progression risk in non-dysplastic Barrett's oesophagus. PMID:26104750

Repair rather than segregation of damage is the optimal unicellular aging strategy.

PubMed

Clegg, Robert J; Dyson, Rosemary J; Kreft, Jan-Ulrich

2014-08-16

How aging, being unfavourable for the individual, can evolve is one of the fundamental problems of biology. Evidence for aging in unicellular organisms is far from conclusive. Some studies found aging even in symmetrically dividing unicellular species; others did not find aging in the same, or in different, unicellular species, or only under stress. Mathematical models suggested that segregation of non-genetic damage, as an aging strategy, would increase fitness. However, these models failed to consider repair as an alternative strategy or did not properly account for the benefits of repair. We used a new and improved individual-based model to examine rigorously the effect of a range of aging strategies on fitness in various environments. Repair of damage emerges as the best strategy despite its fitness costs, since it immediately increases growth rate. There is an optimal investment in repair that outperforms damage segregation in well-mixed, lasting and benign environments over a wide range of parameter values. Damage segregation becomes beneficial, and only in combination with repair, when three factors are combined: (i) the rate of damage accumulation is high, (ii) damage is toxic and (iii) efficiency of repair is low. In contrast to previous models, our model predicts that unicellular organisms should have active mechanisms to repair damage rather than age by segregating damage. Indeed, as predicted, all organisms have evolved active mechanisms of repair whilst aging in unicellular organisms is absent or minimal under benign conditions, apart from microorganisms with a different ecology, inhabiting short-lived environments strongly favouring early reproduction rather than longevity. Aging confers no fitness advantage for unicellular organisms in lasting environments under benign conditions, since repair of non-genetic damage is better than damage segregation.

[Anthropometric model for the prediction of appendicular skeletal muscle mass in Chilean older adults].

PubMed

Lera, Lydia; Albala, Cecilia; Ángel, Bárbara; Sánchez, Hugo; Picrin, Yaisy; Hormazabal, María José; Quiero, Andrea

2014-03-01

To develop a predictive model of appendicular skeletal muscle mass (ASM) based on anthropometric measurements in elderly from Santiago, Chile. 616 community dwelling, non-disabled subjects ≥ 60 years (mean 69.9 ± 5.2 years) living in Santiago, 64.6% female, participating in ALEXANDROS study. Anthropometric measurements, handgrip strength, mobility tests and DEXA were performed. Step by step linear regression models were used to associate ASM from DEXA with anthropometric variables, age and sex. The sample was divided at random into two to obtain prediction equations for both subsamples, which were mutually validated by double cross-validation. The high correlation between the values of observed and predicted MMAE in both sub-samples and the low degree of shrinkage allowed developing the final prediction equation with the total sample. The cross-validity coefficient between prediction models from the subsamples (0.941 and 0.9409) and the shrinkage (0.004 and 0.006) were similar in both equations. The final prediction model obtained from the total sample was: ASM (kg) = 0.107(weight in kg) + 0.251( knee height in cm) + 0.197 (Calf Circumference in cm) +0.047 (dynamometry in kg) - 0.034 (Hip Circumference in cm) + 3.417 (Man) - 0.020 (age years) - 7.646 (R2 = 0.89). The mean ASM obtained by the prediction equation and the DEXA measurement were similar (16.8 ± 4.0 vs 16.9 ± 3.7) and highly concordant according Bland and Altman (95% CI: -2.6 -2.7) and Lin (concordance correlation coefficient = 0.94) methods. We obtained a low cost anthropometric equation to determine the appendicular skeletal muscle mass useful for the screening of sarcopenia in older adults. Copyright AULA MEDICA EDICIONES 2014. Published by AULA MEDICA. All rights reserved.

Model to predict hyperbilirubinemia in healthy term and near-term newborns with exclusive breast feeding.

PubMed

Huang, Hsin-Chung; Yang, Hwai-I; Chang, Yu-Hsun; Chang, Rui-Jane; Chen, Mei-Huei; Chen, Chien-Yi; Chou, Hung-Chieh; Hsieh, Wu-Shiun; Tsao, Po-Nien

2012-12-01

The aim of this study was to identify high-risk newborns who will subsequently develop significant hyperbilirubinemia Days 4 to 10 of life by using the clinical data from the first three days of life. We retrospectively collected exclusively breastfeeding healthy term and near-term newborns born in our nursery between May 1, 2002, to June 30, 2005. Clinical data, including serum bilirubin were collected and the significant predictors were identified. Bilirubin level ≥15mg/dL during Days 4 to 10 of life was defined as significant hyperbilirubinemia. A prediction model to predict subsequent hyperbilirubinemia was established. This model was externally validated in another group of newborns who were enrolled by the same criteria to test its discrimination capability. Totally, 1979 neonates were collected and 1208 cases were excluded by our exclusion criteria. Finally, 771 newborns were enrolled and 182 (23.6%) cases developed significant hyperbilirubinemia during Days 4 to 10 of life. In the logistic regression analysis, gestational age, maximal body weight loss percentage, and peak bilirubin level during the first 72 hours of life were significantly associated with subsequent hyperbilirubinemia. A prediction model was derived with the area under receiver operating characteristic (AUROC) curve of 0.788. Model validation in the separate study (N = 209) showed similar discrimination capability (AUROC = 0.8340). Gestational age, maximal body weight loss percentage, and peak serum bilirubin level during the first 3 days of life have highest predictive value of subsequent significant hyperbilirubinemia. We provide a good model to predict the risk of subsequent significant hyperbilirubinemia. Copyright © 2012. Published by Elsevier B.V.

Predicting Adverse Outcomes After Myocardial Infarction Among Patients With Diabetes Mellitus.

PubMed

Arnold, Suzanne V; Spertus, John A; Jones, Philip G; McGuire, Darren K; Lipska, Kasia J; Xu, Yaping; Stolker, Joshua M; Goyal, Abhinav; Kosiborod, Mikhail

2016-07-01

Although patients with diabetes mellitus experience high rates of adverse events after acute myocardial infarction (AMI), including death and recurrent ischemia, some diabetic patients are likely at low risk, whereas others are at high risk. We sought to develop prediction models to stratify risk after AMI in patients with diabetes mellitus. We developed prediction models for long-term mortality and angina among 1613 patients with diabetes mellitus discharged alive after AMI from 24 US hospitals and then validated the models in a separate, multicenter registry of 786 patients with diabetes mellitus. Event rates in the derivation cohort were 27% for 5-year mortality and 27% for 1-year angina. Parsimonious prediction models demonstrated good discrimination (c-indices=0.78 and 0.69, respectively) and excellent calibration. Within the context of the predictors we estimated, the strongest predictors for mortality were higher creatinine, not working at the time of the AMI, older age, lower hemoglobin, left ventricular dysfunction, and chronic heart failure. The strongest predictors for angina were angina burden in the 4 weeks before the AMI, younger age, history of prior coronary bypass graft surgery, and non-white race. The lowest and highest deciles of predicted risk ranged from 4% to 80% for mortality and 12% to 59% for angina. The models also performed well in external validation (c-indices=0.78 and 0.73, respectively). We found a wide range of risk for adverse outcomes after AMI in diabetic patients. Predictive models can identify patients with diabetes mellitus for whom closer follow-up and aggressive secondary prevention strategies should be considered. © 2016 American Heart Association, Inc.

A predictive model for diagnosing bipolar disorder based on the clinical characteristics of major depressive episodes in Chinese population.

PubMed

Gan, Zhaoyu; Diao, Feici; Wei, Qinling; Wu, Xiaoli; Cheng, Minfeng; Guan, Nianhong; Zhang, Ming; Zhang, Jinbei

2011-11-01

A correct timely diagnosis of bipolar depression remains a big challenge for clinicians. This study aimed to develop a clinical characteristic based model to predict the diagnosis of bipolar disorder among patients with current major depressive episodes. A prospective study was carried out on 344 patients with current major depressive episodes, with 268 completing 1-year follow-up. Data were collected through structured interviews. Univariate binary logistic regression was conducted to select potential predictive variables among 19 initial variables, and then multivariate binary logistic regression was performed to analyze the combination of risk factors and build a predictive model. Receiver operating characteristic (ROC) curve was plotted. Of 19 initial variables, 13 variables were preliminarily selected, and then forward stepwise exercise produced a final model consisting of 6 variables: age at first onset, maximum duration of depressive episodes, somatalgia, hypersomnia, diurnal variation of mood, irritability. The correct prediction rate of this model was 78% (95%CI: 75%-86%) and the area under the ROC curve was 0.85 (95%CI: 0.80-0.90). The cut-off point for age at first onset was 28.5 years old, while the cut-off point for maximum duration of depressive episode was 7.5 months. The limitations of this study include small sample size, relatively short follow-up period and lack of treatment information. Our predictive models based on six clinical characteristics of major depressive episodes prove to be robust and can help differentiate bipolar depression from unipolar depression. Copyright © 2011 Elsevier B.V. All rights reserved.

First learned words are not forgotten: Age-of-acquisition effects in the tip-of-the-tongue experience.

PubMed

Navarrete, Eduardo; Pastore, Massimiliano; Valentini, Rosa; Peressotti, Francesca

2015-10-01

A large body of evidence indicates that the age at which a word is acquired predicts the time required to retrieve that word during speech production. Here we explored whether age of acquisition also predicts the experience of being unable to produce a known word at a particular moment. Italian speakers named a sequence of pictures in Experiment 1 or retrieved a word as a response to a definition in Experiment 2. In both experiments, the participants were instructed to indicate when they were in a tip-of-the-tongue (TOT) state. Generalized mixed-effects models performed on the TOT and correct responses revealed that word frequency and age of acquisition predicted the TOT states. Specifically, low-frequency words elicited more TOTs than did high-frequency words, replicating previous findings. In addition, late-acquired words elicited more TOTs than did early-acquired words. Further analyses revealed that the age of acquisition was a better predictor of TOTs than was word frequency. The effects of age of acquisition were similar with subjective and objective measures of age of acquisition, and persisted when several psycholinguistic variables were taken into consideration as predictors in the generalized mixed-effects models. We explained these results in terms of weaker semantic-to-phonological connections in the speech production system for late-acquired words.

Predictive value of serum sST2 in preschool wheezers for development of asthma with high FeNO.

PubMed

Ketelaar, M E; van de Kant, K D; Dijk, F N; Klaassen, E M; Grotenboer, N S; Nawijn, M C; Dompeling, E; Koppelman, G H

2017-11-01

Wheezing is common in childhood. However, current prediction models of pediatric asthma have only modest accuracy. Novel biomarkers and definition of subphenotypes may improve asthma prediction. Interleukin-1-receptor-like-1 (IL1RL1 or ST2) is a well-replicated asthma gene and associates with eosinophilia. We investigated whether serum sST2 predicts asthma and asthma with elevated exhaled NO (FeNO), compared to the commonly used Asthma Prediction Index (API). Using logistic regression modeling, we found that serum sST2 levels in 2-3 years-old wheezers do not predict doctors' diagnosed asthma at age 6 years. Instead, sST2 predicts a subphenotype of asthma characterized by increased levels of FeNO, a marker for eosinophilic airway inflammation. Herein, sST2 improved the predictive value of the API (AUC=0.70, 95% CI 0.56-0.84), but had also significant predictive value on its own (AUC=0.65, 95% CI 0.52-0.79). Our study indicates that sST2 in preschool wheezers has predictive value for the development of eosinophilic airway inflammation in asthmatic children at school age. © 2017 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

Testing an idealized dynamic cascade model of the development of serious violence in adolescence.

PubMed

Dodge, Kenneth A; Greenberg, Mark T; Malone, Patrick S

2008-01-01

A dynamic cascade model of development of serious adolescent violence was proposed and tested through prospective inquiry with 754 children (50% male; 43% African American) from 27 schools at 4 geographic sites followed annually from kindergarten through Grade 11 (ages 5-18). Self, parent, teacher, peer, observer, and administrative reports provided data. Partial least squares analyses revealed a cascade of prediction and mediation: An early social context of disadvantage predicts harsh-inconsistent parenting, which predicts social and cognitive deficits, which predicts conduct problem behavior, which predicts elementary school social and academic failure, which predicts parental withdrawal from supervision and monitoring, which predicts deviant peer associations, which ultimately predicts adolescent violence. Findings suggest targets for in-depth inquiry and preventive intervention.

Perceptions of Parent-Child Attachment Relationships and Friendship Qualities: Predictors of Romantic Relationship Involvement and Quality in Adolescence.

PubMed

Kochendorfer, Logan B; Kerns, Kathryn A

2017-05-01

Relationships with parents and friends are important contexts for developing romantic relationship skills. Parents and friends may influence both the timing of involvement and the quality of romantic relationships. Three models of the joint influence of parents and friends (direct effects model, mediation model, and moderator model) have been proposed. The present study uses data from a longitudinal study (n = 1012; 49.8% female; 81.1% Caucasian) to examine how attachment and friendship quality at age 10 years predict romantic relationship involvement and quality at ages 12 and 15 years. The results supported the direct effects model, with attachment and friendship quality uniquely predicting different romantic relationship outcomes. The findings provide further support for the important influence of family and friends on early romantic relationships.

The effect of time-dependent macromolecular crowding on the kinetics of protein aggregation: a simple model for the onset of age-related neurodegenerative disease

NASA Astrophysics Data System (ADS)

Minton, Allen

2014-08-01

A linear increase in the concentration of "inert" macromolecules with time is incorporated into simple excluded volume models for protein condensation or fibrillation. Such models predict a long latent period during which no significant amount of protein aggregates, followed by a steep increase in the total amount of aggregate. The elapsed time at which these models predict half-conversion of model protein to aggregate varies by less than a factor of two when the intrinsic rate constant for condensation or fibril growth of the protein is varied over many orders of magnitude. It is suggested that this concept can explain why the symptoms of neurodegenerative diseases associated with the aggregation of very different proteins and peptides appear at approximately the same advanced age in humans.

Longitudinal development of number line estimation and mathematics performance in primary school children.

PubMed

Friso-van den Bos, Ilona; Kroesbergen, Evelyn H; Van Luit, Johannes E H; Xenidou-Dervou, Iro; Jonkman, Lisa M; Van der Schoot, Menno; Van Lieshout, Ernest C D M

2015-06-01

Children's ability to relate number to a continuous quantity abstraction visualized as a number line is widely accepted to be predictive of mathematics achievement. However, a debate has emerged with respect to how children's placements are distributed on this number line across development. In the current study, different models were applied to children's longitudinal number placement data to get more insight into the development of number line representations in kindergarten and early primary school years. In addition, longitudinal developmental relations between number line placements and mathematical achievement, measured with a national test of mathematics, were investigated using cross-lagged panel modeling. A group of 442 children participated in a 3-year longitudinal study (ages 5-8 years) in which they completed a number-to-position task every 6 months. Individual number line placements were fitted to various models, of which a one-anchor power model provided the best fit for many of the placements at a younger age (5 or 6 years) and a two-anchor power model provided better fit for many of the children at an older age (7 or 8 years). The number of children who made linear placements also grew with age. Cross-lagged panel analyses indicated that the best fit was provided with a model in which number line acuity and mathematics performance were mutually predictive of each other rather than models in which one ability predicted the other in a non-reciprocal way. This indicates that number line acuity should not be seen as a predictor of math but that both skills influence each other during the developmental process. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Variations of leaf longevity in tropical moist forests predicted by a trait-driven carbon optimality model

DOE PAGES

Xu, Xiangtao; Medvigy, David; Wright, Stuart Joseph; ...

2017-07-04

Leaf longevity (LL) varies more than 20-fold in tropical evergreen forests, but it remains unclear how to capture these variations using predictive models. Current theories of LL that are based on carbon optimisation principles are challenging to quantitatively assess because of uncertainty across species in the ‘ageing rate:’ the rate at which leaf photosynthetic capacity declines with age. Here in this paper, we present a meta-analysis of 49 species across temperate and tropical biomes, demonstrating that the ageing rate of photosynthetic capacity is positively correlated with the mass-based carboxylation rate of mature leaves. We assess an improved trait-driven carbon optimalitymore » model with in situLL data for 105 species in two Panamanian forests. Additionally, we show that our model explains over 40% of the cross-species variation in LL under contrasting light environment. Collectively, our results reveal how variation in LL emerges from carbon optimisation constrained by both leaf structural traits and abiotic environment.« less

Rheological properties of aging thermosensitive suspensions.

PubMed

Purnomo, Eko H; van den Ende, Dirk; Mellema, Jorrit; Mugele, Frieder

2007-08-01

Aging observed in soft glassy materials inherently affects the rheological properties of these systems and has been described by the soft glassy rheology (SGR) model [S. M. Fielding, J. Rheol. 44, 323 (2000)]. In this paper, we report the measured linear rheological behavior of thermosensitive microgel suspensions and compare it quantitatively with the predictions of the SGR model. The dynamic moduli [G'(omega,t) and G''(omega,t)] obtained from oscillatory measurements are in good agreement with the model. The model also predicts quantitatively the creep compliance J(t - t(w),t(w)), obtained from step stress experiments, for the short time regime [(t - t(w)) < t(w)]. The relative effective temperature X/X(g) obtained from both the oscillatory and the step stress experiments is indeed less than 1 (XX(g) < 1) in agreement with the definition of aging. Moreover, the elasticity of the compressed particles (G(p)) increases with increased compression, i.e., the degree of hindrance and consequently also the bulk elasticity (G' and 1/J) increases with the degree of compression.

Rheological properties of aging thermosensitive suspensions

NASA Astrophysics Data System (ADS)

Purnomo, Eko H.; van den Ende, Dirk; Mellema, Jorrit; Mugele, Frieder

2007-08-01

Aging observed in soft glassy materials inherently affects the rheological properties of these systems and has been described by the soft glassy rheology (SGR) model [S. M. Fielding , J. Rheol. 44, 323 (2000)]. In this paper, we report the measured linear rheological behavior of thermosensitive microgel suspensions and compare it quantitatively with the predictions of the SGR model. The dynamic moduli [ G'(ω,t) and G″(ω,t) ] obtained from oscillatory measurements are in good agreement with the model. The model also predicts quantitatively the creep compliance J(t-tw,tw) , obtained from step stress experiments, for the short time regime [(t-tw)

Variations of leaf longevity in tropical moist forests predicted by a trait-driven carbon optimality model

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

Xu, Xiangtao; Medvigy, David; Wright, Stuart Joseph

Leaf longevity (LL) varies more than 20-fold in tropical evergreen forests, but it remains unclear how to capture these variations using predictive models. Current theories of LL that are based on carbon optimisation principles are challenging to quantitatively assess because of uncertainty across species in the ‘ageing rate:’ the rate at which leaf photosynthetic capacity declines with age. Here in this paper, we present a meta-analysis of 49 species across temperate and tropical biomes, demonstrating that the ageing rate of photosynthetic capacity is positively correlated with the mass-based carboxylation rate of mature leaves. We assess an improved trait-driven carbon optimalitymore » model with in situLL data for 105 species in two Panamanian forests. Additionally, we show that our model explains over 40% of the cross-species variation in LL under contrasting light environment. Collectively, our results reveal how variation in LL emerges from carbon optimisation constrained by both leaf structural traits and abiotic environment.« less

Brain properties predict proximity to symptom onset in sporadic Alzheimer's disease.

PubMed

Vogel, Jacob W; Vachon-Presseau, Etienne; Pichet Binette, Alexa; Tam, Angela; Orban, Pierre; La Joie, Renaud; Savard, Mélissa; Picard, Cynthia; Poirier, Judes; Bellec, Pierre; Breitner, John C S; Villeneuve, Sylvia

2018-06-01

See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.

An Experimental Evaluation of Competing Age-Predictions of Future Time Perspective between Workplace and Retirement Domains

PubMed Central

Kerry, Matthew J.; Embretson, Susan E.

2018-01-01

Future time perspective (FTP) is defined as “perceptions of the future as being limited or open-ended” (Lang and Carstensen, 2002; p. 125). The construct figures prominently in both workplace and retirement domains, but the age-predictions are competing: Workplace research predicts decreasing FTP age-change, in contrast, retirement scholars predict increasing FTP age-change. For the first time, these competing predictions are pitted in an experimental manipulation of subjective life expectancy (SLE). A sample of N = 207 older adults (age 45–60) working full-time (>30-h/week) were randomly assigned to SLE questions framed as either ‘Live-to’ or ‘Die-by’ to evaluate competing predictions for FTP. Results indicate general support for decreasing age-change in FTP, indicated by independent-sample t-tests showing lower FTP in the ‘Die-by’ framing condition. Further general-linear model analyses were conducted to test for interaction effects of retirement planning with experimental framings on FTP and intended retirement; While retirement planning buffered FTP’s decrease, simple-effects also revealed that retirement planning increased intentions for sooner retirement, but lack of planning increased intentions for later retirement. Discussion centers on practical implications of our findings and consequences validity evidence in future empirical research of FTP in both workplace and retirement domains. PMID:29375435

Incremental Value of Repeated Risk Factor Measurements for Cardiovascular Disease Prediction in Middle-Aged Korean Adults: Results From the NHIS-HEALS (National Health Insurance System-National Health Screening Cohort).

PubMed

Cho, In-Jeong; Sung, Ji Min; Chang, Hyuk-Jae; Chung, Namsik; Kim, Hyeon Chang

2017-11-01

Increasing evidence suggests that repeatedly measured cardiovascular disease (CVD) risk factors may have an additive predictive value compared with single measured levels. Thus, we evaluated the incremental predictive value of incorporating periodic health screening data for CVD prediction in a large nationwide cohort with periodic health screening tests. A total of 467 708 persons aged 40 to 79 years and free from CVD were randomly divided into development (70%) and validation subcohorts (30%). We developed 3 different CVD prediction models: a single measure model using single time point screening data; a longitudinal average model using average risk factor values from periodic screening data; and a longitudinal summary model using average values and the variability of risk factors. The development subcohort included 327 396 persons who had 3.2 health screenings on average and 25 765 cases of CVD over 12 years. The C statistics (95% confidence interval [CI]) for the single measure, longitudinal average, and longitudinal summary models were 0.690 (95% CI, 0.682-0.698), 0.695 (95% CI, 0.687-0.703), and 0.752 (95% CI, 0.744-0.760) in men and 0.732 (95% CI, 0.722-0.742), 0.735 (95% CI, 0.725-0.745), and 0.790 (95% CI, 0.780-0.800) in women, respectively. The net reclassification index from the single measure model to the longitudinal average model was 1.78% in men and 1.33% in women, and the index from the longitudinal average model to the longitudinal summary model was 32.71% in men and 34.98% in women. Using averages of repeatedly measured risk factor values modestly improves CVD predictability compared with single measurement values. Incorporating the average and variability information of repeated measurements can lead to great improvements in disease prediction. URL: https://www.clinicaltrials.gov. Unique identifier: NCT02931500. © 2017 American Heart Association, Inc.

Predicting lettuce canopy photosynthesis with statistical and neural network models

NASA Technical Reports Server (NTRS)

Frick, J.; Precetti, C.; Mitchell, C. A.

1998-01-01

An artificial neural network (NN) and a statistical regression model were developed to predict canopy photosynthetic rates (Pn) for 'Waldman's Green' leaf lettuce (Latuca sativa L.). All data used to develop and test the models were collected for crop stands grown hydroponically and under controlled-environment conditions. In the NN and regression models, canopy Pn was predicted as a function of three independent variables: shootzone CO2 concentration (600 to 1500 micromoles mol-1), photosynthetic photon flux (PPF) (600 to 1100 micromoles m-2 s-1), and canopy age (10 to 20 days after planting). The models were used to determine the combinations of CO2 and PPF setpoints required each day to maintain maximum canopy Pn. The statistical model (a third-order polynomial) predicted Pn more accurately than the simple NN (a three-layer, fully connected net). Over an 11-day validation period, average percent difference between predicted and actual Pn was 12.3% and 24.6% for the statistical and NN models, respectively. Both models lost considerable accuracy when used to determine relatively long-range Pn predictions (> or = 6 days into the future).

Association between placentome size, measured using transrectal ultrasonography, and gestational age in cattle.

PubMed

Adeyinka, F D; Laven, R A; Lawrence, K E; van Den Bosch, M; Blankenvoorde, G; Parkinson, T J

2014-03-01

The aim of this study was to estimate whether fetal age could be accurately estimated using placentome size. Fifty-eight cows with confirmed conception dates in two herds were used for the study. The length of the long axis and cross-sectional area of placentomes close to the cervix were measured once every 10 days between approximately 60-130 days of gestation and once every 15 days between 130-160 days of gestation. Four to six placentomes were measured using transrectal ultrasonography in each uterine horn. A linear mixed model was used to establish the factors that were significantly associated with log mean placentome length and to create an equation to predict gestational age from mean placentome length. Limits of agreement analysis was then used to evaluate whether the predictions were sufficiently accurate for mean placentome length to be used, in practice, as a method of determining gestational age. Only age of gestation (p<0.001) and uterine horn (p=0.048) were found to have a significant effect on log mean placentome length. From the three models used to predict gestational age the one that used log mean placentome length of all placentomes, adjusting for the effect of horn, had the smallest 95% limits of agreement; ±33 days. That is, predicted gestational age had a 95% chance of being between 33 days greater and 33.7 days less than actual age. This is approximately twice that reported in studies using measurement of fetal size. Measurement of placentomes near to the cervix using transrectal ultrasonography was easily achieved. There was a significant association between placentome size and gestational age, but between-cow variation in placentome size and growth resulted in poor agreement between placentome size and gestational age. Although placentomes can be easily visualised during diagnosis of pregnancy using transrectal ultrasonography, mean placentome size should not be used to estimate gestational age.

Prediction of BMI at age 11 in a longitudinal sample of the Ulm Birth Cohort Study

PubMed Central

Walter, Viola; Wabitsch, Martin; Rothenbacher, Dietrich; Brenner, Hermann; Schimmelmann, Benno G.

2017-01-01

Obesity is one of the greatest public health challenges in the world with childhood prevalence rates between 20–26% and numerous associated health risks. The aim of the current study was to analyze the 11-year follow-up data of the Ulm Birth Cohort Study (UBCS), to identify whether abnormal eating behavior patterns, especially restrained eating, predict body mass index (BMI) at 11 years of age and to explore other factors known to be longitudinally associated with it. Of the original UBCS, n = 422 children (~ 40% of the original sample) and their parents participated in the 11-year follow-up. BMI at age 8 and 11 as well as information on restrained eating, psychological problems, depressive symptoms, lifestyle, and IQ at age 8 were assessed. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to predict children’s BMI scores at age 11. PLS-SEM explained 68% of the variance of BMI at age 11, with BMI at age 8 being the most important predictor. Restrained eating, via BMI at age 8 as well as parental BMI, had further weak associations with BMI at age 11; no other predictor was statistically significant. Since established overweight at age 8 already predicts BMI scores at age 11 longitudinally, obesity interventions should be implemented in early childhood. PMID:28832593

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

Diagnosis-Based Risk Adjustment for Medicare Capitation Payments

PubMed Central

Ellis, Randall P.; Pope, Gregory C.; Iezzoni, Lisa I.; Ayanian, John Z.; Bates, David W.; Burstin, Helen; Ash, Arlene S.

1996-01-01

Using 1991-92 data for a 5-percent Medicare sample, we develop, estimate, and evaluate risk-adjustment models that utilize diagnostic information from both inpatient and ambulatory claims to adjust payments for aged and disabled Medicare enrollees. Hierarchical coexisting conditions (HCC) models achieve greater explanatory power than diagnostic cost group (DCG) models by taking account of multiple coexisting medical conditions. Prospective models predict average costs of individuals with chronic conditions nearly as well as concurrent models. All models predict medical costs far more accurately than the current health maintenance organization (HMO) payment formula. PMID:10172666

Depression and anger across 25 years: changing vulnerabilities in the VSA model.

PubMed

Johnson, Matthew D; Galambos, Nancy L; Krahn, Harvey J

2014-04-01

Guided by the vulnerability-stress adaptation (VSA) model of marriage and a developmental systems perspective, the current study examined the association of mental health trajectories (depressive symptoms and expressed anger) across the transition to adulthood (ages 18 to 25) with perceived life stress in young adulthood (age 32) and adaptive interaction with a romantic partner and relationship risk at midlife (age 43), accounting for concurrent age 43 mental health. Data from a 25-year prospective, longitudinal study of 341 Canadians (178 women and 163 men) show age 18 levels of both mental health variables predicted perceived life stress and intimate relationship outcomes. The slopes for expressed anger and depressive symptoms were associated with perceived life stress, and relationship risk was also predicted by the slope of expressed anger. Higher perceived life stress at age 32 was associated with less adaptive interaction and increased relationship risk at age 43. Evidence for mediating effects was also found. Implications for theory development, future research, and clinical intervention are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Predicting age at menopause from serum antimüllerian hormone concentration.

PubMed

Tehrani, Fahimeh Ramezani; Shakeri, Nezhat; Solaymani-Dodaran, Masoud; Azizi, Fereidoun

2011-07-01

We aimed to estimate age at menopause using serum antimüllerian hormone (AMH) concentration. We randomly selected 266 study participants from a pool of 1,265 eligible women in the Tehran Lipid and Glucose Study cohort. We measured AMH levels three times at about 3-year intervals. There were 63 occurrences of menopause in our participants over an average of 6-year follow-up. We built an accelerated failure time model using serum AMH level at the start of follow-up to estimate age at menopause. The goodness of fit for the model was tested using Cox-Snell residuals and the Bland-Altman plot. We estimated ages at menopause for different levels of serum AMH concentration among women aged 20 to 49 years. For those who reached menopause, serum AMH concentrations about 6 years before the event provided fairly accurate estimates of the age at menopause. The Bland-Altman plot showed an acceptable agreement between predicted and observed values. Serum AMH concentrations can reasonably forecast the age at menopause for individual women.

A population pharmacokinetic model of valproic acid in pediatric patients with epilepsy: a non-linear pharmacokinetic model based on protein-binding saturation.

PubMed

Ding, Junjie; Wang, Yi; Lin, Weiwei; Wang, Changlian; Zhao, Limei; Li, Xingang; Zhao, Zhigang; Miao, Liyan; Jiao, Zheng

2015-03-01

Valproic acid (VPA) follows a non-linear pharmacokinetic profile in terms of protein-binding saturation. The total daily dose regarding VPA clearance is a simple power function, which may partially explain the non-linearity of the pharmacokinetic profile; however, it may be confounded by the therapeutic drug monitoring effect. The aim of this study was to develop a population pharmacokinetic model for VPA based on protein-binding saturation in pediatric patients with epilepsy. A total of 1,107 VPA serum trough concentrations at steady state were collected from 902 epileptic pediatric patients aged from 3 weeks to 14 years at three hospitals. The population pharmacokinetic model was developed using NONMEM(®) software. The ability of three candidate models (the simple power exponent model, the dose-dependent maximum effect [DDE] model, and the protein-binding model) to describe the non-linear pharmacokinetic profile of VPA was investigated, and potential covariates were screened using a stepwise approach. Bootstrap, normalized prediction distribution errors and external evaluations from two independent studies were performed to determine the stability and predictive performance of the candidate models. The age-dependent exponent model described the effects of body weight and age on the clearance well. Co-medication with carbamazepine was identified as a significant covariate. The DDE model best fitted the aim of this study, although there were no obvious differences in the predictive performances. The condition number was less than 500, and the precision of the parameter estimates was less than 30 %, indicating stability and validity of the final model. The DDE model successfully described the non-linear pharmacokinetics of VPA. Furthermore, the proposed population pharmacokinetic model of VPA can be used to design rational dosage regimens to achieve desirable serum concentrations.

Effects of thermal aging on mechanical performance of paper

Treesearch

B.T. Hotle; J.M. Considine; M.J. Wald; R.E. Rowlands; K.T. Turner

2008-01-01

A missing element of paper aging research is a description of mechanical performance with aging. Tensile strength cannot be predicted directly from DP measurements, and existing models do not represent the effects of aging on strength and stiffness. The primary aim of the present work is to characterize changes of mechanical properties, such as tensile response and...

Physical Activity, Sleep, and Nutrition Do Not Predict Cognitive Performance in Young and Middle-Aged Adults.

PubMed

Gijselaers, Hieronymus J M; Elena, Barberà; Kirschner, Paul A; de Groot, Renate H M

2016-01-01

Biological lifestyle factors (BLFs) such as physical activity, sleep, and nutrition play a role in cognitive functioning. Research concerning the relation between BLFs and cognitive performance is scarce however, especially in young and middle-aged adults. Research has not yet focused on a multidisciplinary approach with respect to this relation in the abovementioned population, where lifestyle habits are more stable. The aim of this study was to examine the contribution of these BLFs to cognitive performance. Path analysis was conducted in an observational study in which 1131 adults were analyzed using a cross-validation approach. Participants provided information on physical activity, sedentary behavior, chronotype, sleep duration, sleep quality, and the consumption of breakfast, fish, and caffeine via a survey. Their cognitive performance was measured using objective digital cognitive tests. Exploration yielded a predictive cohesive model that fitted the data properly, χ(2) /df = 0.8, CFI = 1.00, RMSEA < 0.001, SRMR = 0.016. Validation of the developed model indicated that the model fitted the data satisfactorily, χ(2) /df = 2.75, CFI = 0.95, RMSEA < 0.056, SRMR = 0.035. None of the variables within the BLFs were predictive for any of the cognitive performance measures, except for sedentary behavior. Although sedentary behavior was positively predictive for processing speed its contribution was small and unclear. The results indicate that the variables within the BLFs do not predict cognitive performance in young and middle-aged adults.

Early Conventional MRI for Prediction of Neurodevelopmental Impairment in Extremely-Low-Birth-Weight Infants.

PubMed

Slaughter, Laurel A; Bonfante-Mejia, Eliana; Hintz, Susan R; Dvorchik, Igor; Parikh, Nehal A

2016-01-01

Extremely-low-birth-weight (ELBW; ≤1,000 g) infants are at high risk for neurodevelopmental impairments. Conventional brain MRI at term-equivalent age is increasingly used for prediction of outcomes. However, optimal prediction models remain to be determined, especially for cognitive outcomes. The aim was to evaluate the accuracy of a data-driven MRI scoring system to predict neurodevelopmental impairments. 122 ELBW infants had a brain MRI performed at term-equivalent age. Conventional MRI findings were scored with a standardized algorithm and tested using a multivariable regression model to predict neurodevelopmental impairment, defined as one or more of the following at 18-24 months' corrected age: cerebral palsy, bilateral blindness, bilateral deafness requiring amplification, and/or cognitive/language delay. Results were compared with a commonly cited scoring system. In multivariable analyses, only moderate-to-severe gyral maturational delay was a significant predictor of overall neurodevelopmental impairment (OR: 12.6, 95% CI: 2.6, 62.0; p < 0.001). Moderate-to-severe gyral maturational delay also predicted cognitive delay, cognitive delay/death, and neurodevelopmental impairment/death. Diffuse cystic abnormality was a significant predictor of cerebral palsy (OR: 33.6, 95% CI: 4.9, 229.7; p < 0.001). These predictors exhibited high specificity (range: 94-99%) but low sensitivity (30-67%) for the above outcomes. White or gray matter scores, determined using a commonly cited scoring system, did not show significant association with neurodevelopmental impairment. In our cohort, conventional MRI at term-equivalent age exhibited high specificity in predicting neurodevelopmental outcomes. However, sensitivity was suboptimal, suggesting additional clinical factors and biomarkers are needed to enable accurate prognostication. © 2016 S. Karger AG, Basel.

Comparing aboveground biomass predictions for an uneven-aged pine-dominated stand using local, regional, and national models

Treesearch

D.C. Bragg; K.M. McElligott

2013-01-01

Sequestration by Arkansas forests removes carbon dioxide from the atmosphere, storing this carbon in biomass that fills a number of critical ecological and socioeconomic functions. We need a better understanding of the contribution of forests to the carbon cycle, including the accurate quantification of tree biomass. Models have long been developed to predict...

Regional calibration models for predicting loblolly pine tracheid properties using near-infrared spectroscopy

Treesearch

Mohamad Nabavi; Joseph Dahlen; Laurence Schimleck; Thomas L. Eberhardt; Cristian Montes

2018-01-01

This study developed regional calibration models for the prediction of loblolly pine (Pinus taeda) tracheid properties using near-infrared (NIR) spectroscopy. A total of 1842 pith-to-bark radial strips, aged 19–31 years, were acquired from 268 trees from 109 stands across the southeastern USA. Diffuse reflectance NIR spectra were collected at 10-mm...

Validation of Accelerometer-Based Energy Expenditure Prediction Models in Structured and Simulated Free-Living Settings

ERIC Educational Resources Information Center

Montoye, Alexander H. K.; Conger, Scott A.; Connolly, Christopher P.; Imboden, Mary T.; Nelson, M. Benjamin; Bock, Josh M.; Kaminsky, Leonard A.

2017-01-01

This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a…

Factor Structure and Predictive Utility of the 2 x 2 Achievement Goal Model in a Sample of Taiwan Students

ERIC Educational Resources Information Center

Chiang, Yu-Tzu; Yeh, Yu-Chen; Lin, Sunny S. J.; Hwang, Fang-Ming

2011-01-01

This study examined structure and predictive utility of the 2 x 2 achievement goal model among Taiwan pre-university school students (ages 10 to 16) who learned Chinese language arts. The confirmatory factor analyses of Achievement Goal Questionnaire-Chinese version provided good fitting between the factorial and dimensional structures with the…

Baby Budgeting: Oocyte Cryopreservation in Women Delaying Reproduction Can Reduce Cost per Live Birth

PubMed Central

Devine, Kate; Mumford, Sunni L.; Goldman, Kara N.; Hodes-Wertz, Brooke; Druckenmiller, Sarah; Propst, Anthony M.; Noyes, Nicole

2015-01-01

Objective To determine whether oocyte cryopreservation (OC) for deferred reproduction is cost-effective per live birth using a model constructed from observed clinical practice. Design Decision-tree mathematical model with sensitivity analyses. Setting Not applicable. Patients A simulated cohort of women wishing to delay childbearing until age 40 years. Interventions Not applicable. Main Outcome Measure Cost per live birth. Results Our primary model predicted that OC at age 35 years by women planning to defer pregnancy attempts until age 40 would decrease cost per live birth to $39,946 (and increase odds of live birth to 62% by the end of the model),indicating OC to be a cost-effective strategy relative to forgoing OC, which was associated with a predicted cost per live birth of $55,060 (and 42% chance of live birth). If fresh autologous ART was added at age 40 prior to thawing oocytes, 74% obtained a live birth, though at an increased cost of $61,887. Separate sensitivity analyses demonstrated that OC remained cost-effective so long as patients underwent OC prior to age 38, more than 49% of those not obtaining a spontaneously conceived live birth returned to thaw oocytes, and likelihood of obtaining a spontaneously conceived live birth after six months’ attempts at age 40 was less than 35%. Conclusions In women who plan to delay childbearing until age 40, oocyte cryopreservation before 38 years of age reduces the cost to obtain a live birth. PMID:25813281

Predicting intention to attend and actual attendance at a universal parent-training programme: a comparison of social cognition models.

PubMed

Thornton, Sarah; Calam, Rachel

2011-07-01

The predictive validity of the Health Belief Model (HBM) and the Theory of Planned Behaviour (TPB) were examined in relation to 'intention to attend' and 'actual attendance' at a universal parent-training intervention for parents of children with behavioural difficulties. A validation and reliability study was conducted to develop two questionnaires (N = 108 parents of children aged 4-7).These questionnaires were then used to investigate the predictive validity of the two models in relation to 'intention to attend' and 'actual attendance' at a parent-training intervention ( N = 53 parents of children aged 4-7). Both models significantly predicted 'intention to attend a parent-training group'; however, the TPB accounted for more variance in the outcome variable compared to the HBM. Preliminary investigations highlighted that attendees were more likely to intend to attend the groups, have positive attitudes towards the groups, perceive important others as having positive attitudes towards the groups, and report elevated child problem behaviour scores. These findings provide useful information regarding the belief-based factors that affect attendance at universal parent-training groups. Possible interventions aimed at increasing 'intention to attend' and 'actual attendance' at parent-training groups are discussed.

Prediction of community prevalence of human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach.

PubMed Central

Carabin, Hélène; Escalona, Marisela; Marshall, Clare; Vivas-Martínez, Sarai; Botto, Carlos; Joseph, Lawrence; Basáñez, María-Gloria

2003-01-01

OBJECTIVE: To develop a Bayesian hierarchical model for human onchocerciasis with which to explore the factors that influence prevalence of microfilariae in the Amazonian focus of onchocerciasis and predict the probability of any community being at least mesoendemic (>20% prevalence of microfilariae), and thus in need of priority ivermectin treatment. METHODS: Models were developed with data from 732 individuals aged > or =15 years who lived in 29 Yanomami communities along four rivers of the south Venezuelan Orinoco basin. The models' abilities to predict prevalences of microfilariae in communities were compared. The deviance information criterion, Bayesian P-values, and residual values were used to select the best model with an approximate cross-validation procedure. FINDINGS: A three-level model that acknowledged clustering of infection within communities performed best, with host age and sex included at the individual level, a river-dependent altitude effect at the community level, and additional clustering of communities along rivers. This model correctly classified 25/29 (86%) villages with respect to their need for priority ivermectin treatment. CONCLUSION: Bayesian methods are a flexible and useful approach for public health research and control planning. Our model acknowledges the clustering of infection within communities, allows investigation of links between individual- or community-specific characteristics and infection, incorporates additional uncertainty due to missing covariate data, and informs policy decisions by predicting the probability that a new community is at least mesoendemic. PMID:12973640

Prediction of community prevalence of human onchocerciasis in the Amazonian onchocerciasis focus: Bayesian approach.

PubMed

Carabin, Hélène; Escalona, Marisela; Marshall, Clare; Vivas-Martínez, Sarai; Botto, Carlos; Joseph, Lawrence; Basáñez, María-Gloria

2003-01-01

To develop a Bayesian hierarchical model for human onchocerciasis with which to explore the factors that influence prevalence of microfilariae in the Amazonian focus of onchocerciasis and predict the probability of any community being at least mesoendemic (>20% prevalence of microfilariae), and thus in need of priority ivermectin treatment. Models were developed with data from 732 individuals aged > or =15 years who lived in 29 Yanomami communities along four rivers of the south Venezuelan Orinoco basin. The models' abilities to predict prevalences of microfilariae in communities were compared. The deviance information criterion, Bayesian P-values, and residual values were used to select the best model with an approximate cross-validation procedure. A three-level model that acknowledged clustering of infection within communities performed best, with host age and sex included at the individual level, a river-dependent altitude effect at the community level, and additional clustering of communities along rivers. This model correctly classified 25/29 (86%) villages with respect to their need for priority ivermectin treatment. Bayesian methods are a flexible and useful approach for public health research and control planning. Our model acknowledges the clustering of infection within communities, allows investigation of links between individual- or community-specific characteristics and infection, incorporates additional uncertainty due to missing covariate data, and informs policy decisions by predicting the probability that a new community is at least mesoendemic.

Modelling Precipitation Kinetics During Aging of Al-Mg-Si Alloys

NASA Astrophysics Data System (ADS)

Du, Qiang; Friis, Jepser

A classical Kaufmann-Wagner numerical model is employed to predict the evolution of precipitate size distribution during the aging treatment of Al-Mg-Si alloys. One feature of the model is its fully coupling with CALPHAD database, and with the input of interfacial energy from ab-initial calculation, it is able to capture the morphological change of the precipitates. The simulation results will be compared with the experimental measurements.

Effortful Control Moderates Bidirectional Effects Between Children’s Externalizing Behavior and their Mothers’ Depressive Symptoms

PubMed Central

Choe, Daniel Ewon; Olson, Sheryl L.; Sameroff, Arnold J.

2013-01-01

This study examined bidirectional associations between mothers’ depressive symptoms and children’s externalizing behavior and whether they were moderated by preschool-age effortful control and gender. Mothers and teachers reported on 224 primarily White, middle-class children at ages 3, 5, and 10. Effortful control was assessed via behavioral battery and mother ratings. Structural equation modeling indicated that maternal depressive symptoms at child age 3 predicted more externalizing behavior at age 10 among children with low effortful control and among boys. Externalizing behavior at age 3 predicted fewer depressive symptoms at the age 10 assessments among mothers of children with high effortful control. Boys with suboptimal self-regulation exposed to high levels of maternal depressive symptoms were at greatest risk for school-age behavioral problems. PMID:23668713

Predictive Modeling of Risk Factors and Complications of Cataract Surgery

PubMed Central

Gaskin, Gregory L; Pershing, Suzann; Cole, Tyler S; Shah, Nigam H

2016-01-01

Purpose To quantify the relationship between aggregated preoperative risk factors and cataract surgery complications, as well as to build a model predicting outcomes on an individual-level—given a constellation of demographic, baseline, preoperative, and intraoperative patient characteristics. Setting Stanford Hospital and Clinics between 1994 and 2013. Design Retrospective cohort study Methods Patients age 40 or older who received cataract surgery between 1994 and 2013. Risk factors, complications, and demographic information were extracted from the Electronic Health Record (EHR), based on International Classification of Diseases, 9th edition (ICD-9) codes, Current Procedural Terminology (CPT) codes, drug prescription information, and text data mining using natural language processing. We used a bootstrapped least absolute shrinkage and selection operator (LASSO) model to identify highly-predictive variables. We built random forest classifiers for each complication to create predictive models. Results Our data corroborated existing literature on postoperative complications—including the association of intraoperative complications, complex cataract surgery, black race, and/or prior eye surgery with an increased risk of any postoperative complications. We also found a number of other, less well-described risk factors, including systemic diabetes mellitus, young age (<60 years old), and hyperopia as risk factors for complex cataract surgery and intra- and post-operative complications. Our predictive models based on aggregated outperformed existing published models. Conclusions The constellations of risk factors and complications described here can guide new avenues of research and provide specific, personalized risk assessment for a patient considering cataract surgery. The predictive capacity of our models can enable risk stratification of patients, which has utility as a teaching tool as well as informing quality/value-based reimbursements. PMID:26692059

Prediction of skull fracture risk for children 0-9 months old through validated parametric finite element model and cadaver test reconstruction.

PubMed

Li, Zhigang; Liu, Weiguo; Zhang, Jinhuan; Hu, Jingwen

2015-09-01

Skull fracture is one of the most common pediatric traumas. However, injury assessment tools for predicting pediatric skull fracture risk is not well established mainly due to the lack of cadaver tests. Weber conducted 50 pediatric cadaver drop tests for forensic research on child abuse in the mid-1980s (Experimental studies of skull fractures in infants, Z Rechtsmed. 92: 87-94, 1984; Biomechanical fragility of the infant skull, Z Rechtsmed. 94: 93-101, 1985). To our knowledge, these studies contained the largest sample size among pediatric cadaver tests in the literature. However, the lack of injury measurements limited their direct application in investigating pediatric skull fracture risks. In this study, 50 pediatric cadaver tests from Weber's studies were reconstructed using a parametric pediatric head finite element (FE) model which were morphed into subjects with ages, head sizes/shapes, and skull thickness values that reported in the tests. The skull fracture risk curves for infants from 0 to 9 months old were developed based on the model-predicted head injury measures through logistic regression analysis. It was found that the model-predicted stress responses in the skull (maximal von Mises stress, maximal shear stress, and maximal first principal stress) were better predictors than global kinematic-based injury measures (peak head acceleration and head injury criterion (HIC)) in predicting pediatric skull fracture. This study demonstrated the feasibility of using age- and size/shape-appropriate head FE models to predict pediatric head injuries. Such models can account for the morphological variations among the subjects, which cannot be considered by a single FE human model.

Development and Evaluation of Models for the Relationship between Tree Height and Diameter at Breast Height for Chinese-Fir Plantations in Subtropical China.

PubMed

Li, Yan-qiong; Deng, Xiang-wen; Huang, Zhi-hong; Xiang, Wen-hua; Yan, Wen-de; Lei, Pi-feng; Zhou, Xiao-lu; Peng, Chang-hui

2015-01-01

Tree diameter at breast height (dbh) and height are the most important variables used in forest inventory and management as well as forest carbon-stock estimation. In order to identify the key stand variables that influence the tree height-dbh relationship and to develop and validate a suit of models for predicting tree height, data from 5961 tree samples aged from 6 years to 53 years and collected from 80 Chinese-fir plantation plots were used to fit 39 models, including 33 nonlinear models and 6 linear models, were developed and evaluated into two groups. The results showed that composite models performed better in height estimate than one-independent-variable models. Nonlinear composite Model 34 and linear composite Model 6 were recommended for predicting tree height in Chinese fir plantations with a dbh range between 4 cm and 40 cm when the dbh data for each tree and the quadratic mean dbh of the stand (Dq) and mean height of the stand (Hm) were available. Moreover, Hm could be estimated by using the formula Hm = 11.707 × l n(Dq)-18.032. Clearly, Dq was the primary stand variable that influenced the height-dbh relationship. The parameters of the models varied according to stand age and site. The inappropriate application of provincial or regional height-dbh models for predicting small tree height at local scale may result in larger uncertainties. The method and the recommended models developed in this study were statistically reliable for applications in growth and yield estimation for even-aged Chinese-fir plantation in Huitong and Changsha. The models could be extended to other regions and to other tree species only after verification in subtropical China.

Development and Evaluation of Models for the Relationship between Tree Height and Diameter at Breast Height for Chinese-Fir Plantations in Subtropical China

PubMed Central

Li, Yan-qiong; Deng, Xiang-wen; Huang, Zhi-hong; Xiang, Wen-hua; Yan, Wen-de; Lei, Pi-feng; Zhou, Xiao-lu; Peng, Chang-hui

2015-01-01

Tree diameter at breast height (dbh) and height are the most important variables used in forest inventory and management as well as forest carbon-stock estimation. In order to identify the key stand variables that influence the tree height-dbh relationship and to develop and validate a suit of models for predicting tree height, data from 5961 tree samples aged from 6 years to 53 years and collected from 80 Chinese-fir plantation plots were used to fit 39 models, including 33 nonlinear models and 6 linear models, were developed and evaluated into two groups. The results showed that composite models performed better in height estimate than one-independent-variable models. Nonlinear composite Model 34 and linear composite Model 6 were recommended for predicting tree height in Chinese fir plantations with a dbh range between 4 cm and 40 cm when the dbh data for each tree and the quadratic mean dbh of the stand (Dq) and mean height of the stand (Hm) were available. Moreover, Hm could be estimated by using the formula Hm=11.707×ln(Dq)-18.032. Clearly, Dq was the primary stand variable that influenced the height-dbh relationship. The parameters of the models varied according to stand age and site. The inappropriate application of provincial or regional height-dbh models for predicting small tree height at local scale may result in larger uncertainties. The method and the recommended models developed in this study were statistically reliable for applications in growth and yield estimation for even-aged Chinese-fir plantation in Huitong and Changsha. The models could be extended to other regions and to other tree species only after verification in subtropical China. PMID:25905458

What parents don't know: Disclosure and secrecy in a sample of urban adolescents.

PubMed

Jäggi, Lena; Drazdowski, Tess K; Kliewer, Wendy

2016-12-01

Research with two-parent European households has suggested that secrecy, and not disclosure of information per se, predicts adolescent adjustment difficulties. The present study attempted to replicate this finding using data from a 4-wave study of 358 poor, urban adolescents (47% male; M age = 12 yrs) in the United States, most of whom (>92%) were African American. Adolescents self-reported secrecy, disclosure, depressive symptoms, and delinquency at each wave. Confirmatory factor analyses revealed that a two-factor model with secrecy and disclosure as separate, but correlated, factors was a better fit than a one-factor model. However, predictive models differed from previous research. Secrecy did not predict depressive symptoms, rather depressive symptoms predicted secrecy. For delinquency, there were significant paths from both secrecy to delinquency and delinquency to secrecy, as well as from delinquency to disclosure. These results did not differ by age or sex. Comparisons with previous findings are discussed. Copyright © 2016 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Longitudinal Study-Based Dementia Prediction for Public Health

PubMed Central

Kim, HeeChel; Chun, Hong-Woo; Kim, Seonho; Coh, Byoung-Youl; Kwon, Oh-Jin; Moon, Yeong-Ho

2017-01-01

The issue of public health in Korea has attracted significant attention given the aging of the country’s population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical “big data”-based disease prediction model may assist the diagnosis of any disease more correctly. PMID:28867810

Predicting Young’s Modulus of Glass/Ceramic Sealant for Solid Oxide Fuel Cell Considering the Combined Effects of Aging, Micro-Voids and Self-Healing

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

Liu, Wenning N.; Sun, Xin; Khaleel, Mohammad A.

We study the temperature dependent Young’s modulus for the glass/ceramic seal material used in Solid Oxide Fuel Cells (SOFCs). With longer heat treatment or aging time during operation, further devitrification may reduce the residual glass content in the seal material while boosting the ceramic crystalline content. In the meantime, micro-voids induced by the cooling process from the high operating temperature to room temperature can potentially degrade the mechanical properties of the glass/ceramic sealant. Upon reheating to the SOFC operating temperature, possible self-healing phenomenon may occur in the glass/ceramic sealant which can potentially restore some of its mechanical properties. A phenomenologicalmore » model is developed to model the temperature dependent Young’s modulus of glass/ceramic seal considering the combined effects of aging, micro-voids, and possible self-healing. An aging-time-dependent crystalline content model is first developed to describe the increase of the crystalline content due to the continuing devitrification under high operating temperature. A continuum damage mechanics (CDM) model is then adapted to model the effects of both cooling induced micro-voids and reheating induced self-healing. This model is applied to model the glass-ceramic G18, a candidate SOFC seal material previously developed at PNNL. Experimentally determined temperature dependent Young’s modulus is used to validate the model predictions« less

An artificial neural network improves prediction of observed survival in patients with laryngeal squamous carcinoma.

PubMed

Jones, Andrew S; Taktak, Azzam G F; Helliwell, Timothy R; Fenton, John E; Birchall, Martin A; Husband, David J; Fisher, Anthony C

2006-06-01

The accepted method of modelling and predicting failure/survival, Cox's proportional hazards model, is theoretically inferior to neural network derived models for analysing highly complex systems with large datasets. A blinded comparison of the neural network versus the Cox's model in predicting survival utilising data from 873 treated patients with laryngeal cancer. These were divided randomly and equally into a training set and a study set and Cox's and neural network models applied in turn. Data were then divided into seven sets of binary covariates and the analysis repeated. Overall survival was not significantly different on Kaplan-Meier plot, or with either test model. Although the network produced qualitatively similar results to Cox's model it was significantly more sensitive to differences in survival curves for age and N stage. We propose that neural networks are capable of prediction in systems involving complex interactions between variables and non-linearity.

External-environmental and internal-health early life predictors of adolescent development.

PubMed

Hartman, Sarah; Li, Zhi; Nettle, Daniel; Belsky, Jay

2017-12-01

A wealth of evidence documents associations between various aspects of the rearing environment and later development. Two evolutionary-inspired models advance explanations for why and how such early experiences shape later functioning: (a) the external-prediction model, which highlights the role of the early environment (e.g., parenting) in regulating children's development, and (b) the internal-prediction model, which emphasizes internal state (i.e., health) as the critical regulator. Thus, by using data from the NICHD Study of Early Child Care and Youth Development, the current project draws from both models by investigating whether the effect of the early environment on later adolescent functioning is subject to an indirect effect by internal-health variables. Results showed a significant indirect effect of internal health on the relation between the early environment and adolescent behavior. Specifically, early environmental adversity during the first 5 years of life predicted lower quality health during childhood, which then led to problematic adolescent functioning and earlier age of menarche for girls. In addition, for girls, early adversity predicted lower quality health that forecasted earlier age of menarche leading to increased adolescent risk taking. The discussion highlights the importance of integrating both internal and external models to further understand the developmental processes that effect adolescent behavior.

Cryptosporidiosis susceptibility and risk: a case study.

PubMed

Makri, Anna; Modarres, Reza; Parkin, Rebecca

2004-02-01

Regional estimates of cryptosporidiosis risks from drinking water exposure were developed and validated, accounting for AIDS status and age. We constructed a model with probability distributions and point estimates representing Cryptosporidium in tap water, tap water consumed per day (exposure characterization); dose response, illness given infection, prolonged illness given illness; and three conditional probabilities describing the likelihood of case detection by active surveillance (health effects characterization). The model predictions were combined with population data to derive expected case numbers and incidence rates per 100,000 population, by age and AIDS status, borough specific and for New York City overall in 2000 (risk characterization). They were compared with same-year surveillance data to evaluate predictive ability, assumed to represent true incidence of waterborne cryptosporidiosis. The predicted mean risks, similar to previously published estimates for this region, overpredicted observed incidence-most extensively when accounting for AIDS status. The results suggest that overprediction may be due to conservative parameters applied to both non-AIDS and AIDS populations, and that biological differences for children need to be incorporated. Interpretations are limited by the unknown accuracy of available surveillance data, in addition to variability and uncertainty of model predictions. The model appears sensitive to geographical differences in AIDS prevalence. The use of surveillance data for validation and model parameters pertinent to susceptibility are discussed.

Alcohol-related predictors of adolescent driving: gender differences in crashes and offenses.

PubMed

Shope, J T; Waller, P F; Lang, S W

1996-11-01

Demographic and alcohol-related data collected from eight-grade students (age 13 years) were used in logistic regression to predict subsequent first-year driving crashes and offenses (age 17 years). For young men's crashes and offenses, good-fitting models used living situation (both parents or not), parents' attitude about teen drinking (negative or neutral), and the interaction term. Young men who lived with both parents and reported negative parental attitudes regarding teen drinking were less likely to have crashes and offenses. For young women's crashes, a good-fitting model included friends' involvement with alcohol. Young women who reported that their friends were not involved with alcohol were least likely to have crashes. No model predicting young women's offenses emerged.

Prospective Evaluation of a Model-Based Dosing Regimen for Amikacin in Preterm and Term Neonates in Clinical Practice

PubMed Central

De Cock, R. F. W.; Allegaert, K.; Vanhaesebrouck, S.; Danhof, M.; Knibbe, C. A. J.

2015-01-01

Based on a previously derived population pharmacokinetic model, a novel neonatal amikacin dosing regimen was developed. The aim of the current study was to prospectively evaluate this dosing regimen. First, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/liter) and peak (>24 mg/liter) levels. Second, all observed TDM concentrations were compared with model-predicted concentrations, whereby the results of a normalized prediction distribution error (NPDE) were considered. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (i.e., prescription errors and disease characteristics of outliers) were explored. In 579 neonates (median birth body weight, 2,285 [range, 420 to 4,850] g; postnatal age 2 days [range, 1 to 30 days]; gestational age, 34 weeks [range, 24 to 41 weeks]), 90.5% of the observed early peak levels reached 24 mg/liter, and 60.2% of the trough levels were <3 mg/liter (93.4% ≤5 mg/liter). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations of >24 mg/liter were reached at steady state in almost all patients. Trough values of <3 mg/liter at steady state were documented in 78% to 100% and 45% to 96% of simulated cases with and without ibuprofen coadministration, respectively; suboptimal trough levels were found in patients with postnatal age <14 days and current weight of >2,000 g. Prospective evaluation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Slightly adapted dosing for patient subgroups with suboptimal trough levels was proposed. This model-based approach improves neonatal dosing individualization. PMID:26248375

A comparison of the Full Outline of UnResponsiveness (FOUR) score and Glasgow Coma Score (GCS) in predictive modelling in traumatic brain injury.

PubMed

Kasprowicz, Magdalena; Burzynska, Malgorzata; Melcer, Tomasz; Kübler, Andrzej

2016-01-01

To compare the performance of multivariate predictive models incorporating either the Full Outline of UnResponsiveness (FOUR) score or Glasgow Coma Score (GCS) in order to test whether substituting GCS with the FOUR score in predictive models for outcome in patients after TBI is beneficial. A total of 162 TBI patients were prospectively enrolled in the study. Stepwise logistic regression analysis was conducted to compare the prediction of (1) in-ICU mortality and (2) unfavourable outcome at 3 months post-injury using as predictors either the FOUR score or GCS along with other factors that may affect patient outcome. The areas under the ROC curves (AUCs) were used to compare the discriminant ability and predictive power of the models. The internal validation was performed with bootstrap technique and expressed as accuracy rate (AcR). The FOUR score, age, the CT Rotterdam score, systolic ABP and being placed on ventilator within day one (model 1: AUC: 0.906 ± 0.024; AcR: 80.3 ± 4.8%) performed equally well in predicting in-ICU mortality as the combination of GCS with the same set of predictors plus pupil reactivity (model 2: AUC: 0.913 ± 0.022; AcR: 81.1 ± 4.8%). The CT Rotterdam score, age and either the FOUR score (model 3) or GCS (model 4) equally well predicted unfavourable outcome at 3 months post-injury (AUC: 0.852 ± 0.037 vs. 0.866 ± 0.034; AcR: 72.3 ± 6.6% vs. 71.9%±6.6%, respectively). Adding the FOUR score or GCS at discharge from ICU to predictive models for unfavourable outcome increased significantly their performances (AUC: 0.895 ± 0.029, p = 0.05; AcR: 76.1 ± 6.5%; p < 0.004 when compared with model 3; and AUC: 0.918 ± 0.025, p < 0.05; AcR: 79.6 ± 7.2%, p < 0.009 when compared with model 4), but there was no benefit from substituting GCS with the FOUR score. Results showed that FOUR score and GCS perform equally well in multivariate predictive modelling in TBI.

High-Temperature Cast Aluminum for Efficient Engines

NASA Astrophysics Data System (ADS)

Bobel, Andrew C.

Accurate thermodynamic databases are the foundation of predictive microstructure and property models. An initial assessment of the commercially available Thermo-Calc TCAL2 database and the proprietary aluminum database of QuesTek demonstrated a large degree of deviation with respect to equilibrium precipitate phase prediction in the compositional region of interest when compared to 3-D atom probe tomography (3DAPT) and transmission electron microscopy (TEM) experimental results. New compositional measurements of the Q-phase (Al-Cu-Mg-Si phase) led to a remodeling of the Q-phase thermodynamic description in the CALPHAD databases which has produced significant improvements in the phase prediction capabilities of the thermodynamic model. Due to the unique morphologies of strengthening precipitate phases commonly utilized in high-strength cast aluminum alloys, the development of new microstructural evolution models to describe both rod and plate particle growth was critical for accurate mechanistic strength models which rely heavily on precipitate size and shape. Particle size measurements through both 3DAPT and TEM experiments were used in conjunction with literature results of many alloy compositions to develop a physical growth model for the independent prediction of rod radii and rod length evolution. In addition a machine learning (ML) model was developed for the independent prediction of plate thickness and plate diameter evolution as a function of alloy composition, aging temperature, and aging time. The developed models are then compared with physical growth laws developed for spheres and modified for ellipsoidal morphology effects. Analysis of the effect of particle morphology on strength enhancement has been undertaken by modification of the Orowan-Ashby equation for 〈110〉 alpha-Al oriented finite rods in addition to an appropriate version for similarly oriented plates. A mechanistic strengthening model was developed for cast aluminum alloys containing both rod and plate-like precipitates. The model accurately accounts for the temperature dependence of particle nucleation and growth, solid solution strengthening, Si eutectic strength, and base aluminum yield strength. Strengthening model predictions of tensile yield strength are in excellent agreement with experimental observations over a wide range of aluminum alloy systems, aging temperatures, and test conditions. The developed models enable the prediction of the required particle morphology and volume fraction necessary to achieve target property goals in the design of future aluminum alloys. The effect of partitioning elements to the Q-phase was also considered for the potential to control the nucleation rate, reduce coarsening, and control the evolution of particle morphology. Elements were selected based on density functional theory (DFT) calculations showing the prevalence of certain elements to partition to the Q-phase. 3DAPT experiments were performed on Q-phase containing wrought alloys with these additions and show segregation of certain elements to the Q-phase with relative agreement to DFT predictions.

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

PubMed

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

2015-01-01

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

Predicting the impact of measles vaccination in England and Wales: model validation and analysis of policy options.

PubMed Central

Babad, H. R.; Nokes, D. J.; Gay, N. J.; Miller, E.; Morgan-Capner, P.; Anderson, R. M.

1995-01-01

Measles incidence in England and Wales has fallen to an all-time low. Attention is now focused on preventing local outbreaks, and, in the long run, on the elimination of indigenous measles. A realistic age-structured (RAS) mathematical model of measles transmission is used to reconstruct the impact of measles vaccination in England and Wales from 1968 to the present and to evaluate the merits of future policy options. In general, the predictions of the model show good agreement with long-term age stratified case reports and seroprevalence surveys. The model underestimates the proportion of cases that are notified in 0-2-year-old children. However, recent work suggests a high degree of misdiagnosis in this age group. Projections on the basis of the existing vaccination strategy in the UK suggest that the present level of measles vaccine coverage will be insufficient to eliminate small seasonal outbreaks of measles. This result is, however, sensitive to the assumed level of vaccine efficacy. Explorations of a variety of changes to current vaccination strategy favour a 2-dose schedule with the second dose administered at age 4 years irrespective of vaccination history. A vaccination campaign in school-age children, to reduce deficits in herd immunity, would accelerate progress towards measles elimination. PMID:7705494

Predicting the impact of measles vaccination in England and Wales: model validation and analysis of policy options.

PubMed

Babad, H R; Nokes, D J; Gay, N J; Miller, E; Morgan-Capner, P; Anderson, R M

1995-04-01

Measles incidence in England and Wales has fallen to an all-time low. Attention is now focused on preventing local outbreaks, and, in the long run, on the elimination of indigenous measles. A realistic age-structured (RAS) mathematical model of measles transmission is used to reconstruct the impact of measles vaccination in England and Wales from 1968 to the present and to evaluate the merits of future policy options. In general, the predictions of the model show good agreement with long-term age stratified case reports and seroprevalence surveys. The model underestimates the proportion of cases that are notified in 0-2-year-old children. However, recent work suggests a high degree of misdiagnosis in this age group. Projections on the basis of the existing vaccination strategy in the UK suggest that the present level of measles vaccine coverage will be insufficient to eliminate small seasonal outbreaks of measles. This result is, however, sensitive to the assumed level of vaccine efficacy. Explorations of a variety of changes to current vaccination strategy favour a 2-dose schedule with the second dose administered at age 4 years irrespective of vaccination history. A vaccination campaign in school-age children, to reduce deficits in herd immunity, would accelerate progress towards measles elimination.

Temporal Trends and Future Prediction of Breast Cancer Incidence Across Age Groups in Trivandrum, South India.

PubMed

Mathew, Aleyamma; George, Preethi Sara; Arjunan, Asha; Augustine, Paul; Kalavathy, Mc; Padmakumari, G; Mathew, Beela Sarah

2016-01-01

Increasing breast cancer (BC) incidence rates have been reported from India; causal factors for this increased incidence are not understood and diagnosis is mostly in advanced stages. Trivandrum exhibits the highest BC incidence rates in India. This study aimed to estimate trends in incidence by age from 2005- 2014, to predict rates through 2020 and to assess the stage at diagnosis of BC in Trivandrum. BC cases were obtained from the Population Based Cancer Registry, Trivandrum. Distribution of stage at diagnosis and incidence rates of BC [Age-specific (ASpR), crude (CR) and age-standardized (ASR)] are described and employed with a joinpoint regression model to estimate average annual percent changes (AAPC) and a Bayesian model to estimate predictive rates. BC accounts for 31% (2681/8737) of all female cancers in Trivandrum. Thirty-five percent (944/2681) are <50 years of age and only 9% present with stage I disease. Average age increased from 53 to 56.4 years (p=0.0001), CR (per 105 women) increased from 39 (ASR: 35.2) to 55.4 (ASR: 43.4), AAPC for CR was 5.0 (p=0.001) and ASR was 3.1 (p=0.001). Rates increased from 50 years. Predicted ASpR is 174 in 50-59 years, 231 in > 60 years and overall CR is 80 (ASR: 57) for 2019- 20. BC, mostly diagnosed in advanced stages, is rising rapidly in South India with large increases likely in the future; particularly among post-menopausal women. This increase might be due to aging and/or changes in lifestyle factors. Reasons for the increased incidence and late stage diagnosis need to be studied.

Predicting severe motor impairment in preterm children at age 5 years.

PubMed

Synnes, Anne; Anderson, Peter J; Grunau, Ruth E; Dewey, Deborah; Moddemann, Diane; Tin, Win; Davis, Peter G; Doyle, Lex W; Foster, Gary; Khairy, May; Nwaesei, Chukwuma; Schmidt, Barbara

2015-08-01

To determine whether the ability to predict severe motor impairment at age 5 years improves between birth and 18 months. Ancillary study of the Caffeine for Apnea of Prematurity Trial. International cohort of very low birth weight children who were assessed sequentially from birth to 5 years. Severe motor impairment was defined as a score <5th percentile on the Movement Assessment Battery of Children (MABC), or inability to complete the MABC because of cerebral palsy. Multivariable logistic regression cumulative risk models used four sets of predictor variables: early neonatal risk factors, risk factors at 36 weeks' postmenstrual age, risk factors at a corrected age of 18 months, and sociodemographic variables. A receiver operating characteristic curve (ROC) was generated for each model, and the four ROC curves were compared to determine if the addition of the new set of predictors significantly increased the area under the curve (AUC). Of 1469 children, 291 (19.8%) had a severe motor impairment at 5 years. The AUC increased from 0.650 soon after birth, to 0.718 (p<0.001) at 36 weeks' postmenstrual age, and to 0.797 at 18 months (p<0.001). Sociodemographic variables did not significantly improve the AUC (AUC=0.806; p=0.07). Prediction of severe motor impairment at 5 years of age using a cumulative risk model improves significantly from birth to 18 months of age in children with birth weights between 500 g and 1250 g. ClinicalTrials.gov number NCT00182312. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Age at treatment predicts reason for discontinuation of TNF antagonists: data from the BIOBADASER 2.0 registry.

PubMed

Busquets, Noemí; Tomero, Eva; Descalzo, Miguel Ángel; Ponce, Andrés; Ortiz-Santamaría, Vera; Surís, Xavier; Carmona, Loreto; Gómez-Reino, Juan J

2011-11-01

To assess the retention rate of TNF antagonists in elderly patients suffering from chronic arthropathies and to identify predictive variables of discontinuation by inefficacy or by adverse events (AEs). All patients treated with TNF antagonists in BIOBADASER 2.0, with a diagnosis of either RA or spondyloarthritis (SpA: AS and PsA) were included and classified as <65 (younger) or ≥65 years of age (older) at start of the treatment. Cumulative incidence function for discontinuation (inefficacy or AE) was estimated as being the alternative reason for a competing risk. Competing-risks regression models were used to measure the association between study groups, covariates and reason for discontinuation. A total of 4851 patients were studied; 2957 RA (2291 in the younger group and 666 in the older group) and 1894 SpA (1795 in the younger group and 99 in the older group). Retention curves were statistically differently stratified by age groups, with the SpA younger group having the largest retention rate. Competing-risks regression models showed that in the older group, AEs were the most common reason for discontinuation regardless of the diagnosis of the patient and TNF antagonist molecule, whereas in the younger group, the most common cause of discontinuation was inefficacy. In conclusion, factors predicting discontinuation of TNF antagonists due to AEs are older age and diagnosis of RA. On the other hand, younger age predicts discontinuation due to lack of efficacy.

The utility of childhood and adolescent obesity assessment in relation to adult health

PubMed Central

Goldhaber-Fiebert, Jeremy D.; Rubinfeld, Rachel E.; Bhattacharya, Jay; Robinson, Thomas N.; Wise, Paul H.

2014-01-01

The high prevalence of childhood obesity has raised concerns regarding long-term patterns of adult health and has generated calls for obesity screening of young children. This study examined patterns of obesity and the predictive utility of obesity screening for children of different ages in terms of adult health outcomes. Using the National Longitudinal Survey of Youth, the Population Study of Income Dynamics, and National Health and Nutrition Evaluation Surveys, we estimated the sensitivity, specificity and predictive value of childhood BMI to identify 2, 5, 10, or 15 year-olds who will become obese adults. We constructed models assessing the relationship of childhood BMI to obesity-related diseases through middle age stratified by sex and race/ethnicity. 12% of 18 year-olds were obese. While 50% of these adolescents would not have been identified by screening at age 5, 9% would have been missed at age 15. Approximately 70% of obese children at age 5 became non-obese at age 18. The predictive utility of obesity screening below the age of 10 was low, even when maternal obesity was also included. The elevated risk of diabetes, obesity, and hypertension in middle age predicted by obesity at age 15 was significantly higher than at age 5 (e.g., the RR of diabetes for obese white male 15 year-olds was 4.5; for 5 year-olds, it was 1.6). Early childhood obesity assessment adds limited predictive utility to strategies that also include later childhood assessment. Targeted approaches in later childhood or universal strategies to prevent unhealthy weight gain should be considered. PMID:22647830

The burden of typhoid fever in low- and middle-income countries: A meta-regression approach

PubMed Central

Warren, Joshua L.; Crawford, Forrest W.; Weinberger, Daniel M.; Kürüm, Esra; Pak, Gi Deok; Marks, Florian; Pitzer, Virginia E.

2017-01-01

Background Upcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data. Methods We developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model. Results We estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9–48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2–4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models. Conclusions Recent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases. PMID:28241011

Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study.

PubMed

Byham-Gray, Laura; Parrott, J Scott; Ho, Wai Yin; Sundell, Mary B; Ikizler, T Alp

2014-01-01

The study objectives were to explore the predictors of measured resting energy expenditure (mREE) among a sample of maintenance hemodialysis (MHD) patients, to generate a predictive energy equation (MHDE), and to compare such models to another commonly used predictive energy equation in nutritional care, the Mifflin-St. Jeor equation (MSJE). The study was a retrospective, cross-sectional cohort design conducted at the Vanderbilt University Medical Center. Study subjects were adult MHD patients (N = 67). Data collected from several clinical trials were analyzed using Pearson's correlation and multivariate linear regression procedures. Demographic, anthropometric, clinical, and laboratory data were examined as potential predictors of mREE. Limits of agreement between the MHDE and the MSJE were evaluated using Bland-Altman plots. The a priori α was set at P < .05. The main outcome measure was mREE. The mean age of the sample was 47 ± 13 years. Fifty participants (75.6%) were African American, 7.5% were Hispanic, and 73.1% were males. Fat-free mass (FFM), serum albumin (ALB), age, weight, serum creatinine (CR), height, body mass index, sex, high-sensitivity C-reactive protein (CRP), and fat mass (FM) were all significantly (P < .05) correlated with mREE. After screening for multi-collinearity, the best predictive model (MHDE-lean body mass [LBM]) of mREE included (R(2) = 0.489) FFM, ALB, age, and CRP. Two additional models (MHDE-CRP and MHDE-CR) with acceptable predictability (R(2) = 0.460 and R(2) = 0.451) were derived to improve the clinical utility of the developed energy equation (MHDE-LBM). Using Bland-Altman plots, the MHDE over- and underpredicted mREE less often than the MSJE. Predictive models (MHDE) including selective demographic, clinical, and anthropometric data explained less than 50% variance of mREE but had better precision in determining energy requirements for MHD patients when compared with MSJE. Further research is necessary to improve predictive models of mREE in the MHD population and to test its validity and clinical application. Copyright © 2014 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy.

PubMed

Jochems, Arthur; El-Naqa, Issam; Kessler, Marc; Mayo, Charles S; Jolly, Shruti; Matuszak, Martha; Faivre-Finn, Corinne; Price, Gareth; Holloway, Lois; Vinod, Shalini; Field, Matthew; Barakat, Mohamed Samir; Thwaites, David; de Ruysscher, Dirk; Dekker, Andre; Lambin, Philippe

2018-02-01

Early death after a treatment can be seen as a therapeutic failure. Accurate prediction of patients at risk for early mortality is crucial to avoid unnecessary harm and reducing costs. The goal of our work is two-fold: first, to evaluate the performance of a previously published model for early death in our cohorts. Second, to develop a prognostic model for early death prediction following radiotherapy. Patients with NSCLC treated with chemoradiotherapy or radiotherapy alone were included in this study. Four different cohorts from different countries were available for this work (N = 1540). The previous model used age, gender, performance status, tumor stage, income deprivation, no previous treatment given (yes/no) and body mass index to make predictions. A random forest model was developed by learning on the Maastro cohort (N = 698). The new model used performance status, age, gender, T and N stage, total tumor volume (cc), total tumor dose (Gy) and chemotherapy timing (none, sequential, concurrent) to make predictions. Death within 4 months of receiving the first radiotherapy fraction was used as the outcome. Early death rates ranged from 6 to 11% within the four cohorts. The previous model performed with AUC values ranging from 0.54 to 0.64 on the validation cohorts. Our newly developed model had improved AUC values ranging from 0.62 to 0.71 on the validation cohorts. Using advanced machine learning methods and informative variables, prognostic models for early mortality can be developed. Development of accurate prognostic tools for early mortality is important to inform patients about treatment options and optimize care.

One vs. Two Breast Density Measures to Predict 5- and 10- Year Breast Cancer Risk

PubMed Central

Kerlikowske, Karla; Gard, Charlotte C.; Sprague, Brian L.; Tice, Jeffrey A.; Miglioretti, Diana L.

2015-01-01

Background One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined if two BI-RADS density measures improves the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared to one measure. Methods We included 722,654 women aged 35–74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000–2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. Results The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC=0.640 vs. 0.635). Of 18.6% of women (134,404/722,654) who decreased density categories, 15.4% (20,741/134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. Conclusion The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. Impact A two-density model should be considered for women whose density decreases when calculating breast cancer risk. PMID:25824444

One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

PubMed

Kerlikowske, Karla; Gard, Charlotte C; Sprague, Brian L; Tice, Jeffrey A; Miglioretti, Diana L

2015-06-01

One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure. We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. A two-density model should be considered for women whose density decreases when calculating breast cancer risk. ©2015 American Association for Cancer Research.

The dark side of galaxy colour: evidence from new SDSS measurements of galaxy clustering and lensing

NASA Astrophysics Data System (ADS)

Hearin, Andrew P.; Watson, Douglas F.; Becker, Matthew R.; Reyes, Reinabelle; Berlind, Andreas A.; Zentner, Andrew R.

2014-10-01

The age-matching model has recently been shown to predict correctly the luminosity L and g - r colour of galaxies residing within dark matter haloes. The central tenet of the model is intuitive: older haloes tend to host galaxies with older stellar populations. In this paper, we demonstrate that age matching also correctly predicts the g - r colour trends exhibited in a wide variety of statistics of the galaxy distribution for stellar mass M* threshold samples. In particular, we present new Sloan Digital Sky Survey (SDSS) measurements of galaxy clustering and the galaxy-galaxy lensing signal ΔΣ as a function of M* and g - r colour, and show that age matching exhibits remarkable agreement with these and other statistics of low-redshift galaxies. In so doing, we also demonstrate good agreement between the galaxy-galaxy lensing observed by SDSS and the ΔΣ signal predicted by abundance matching, a new success of this model. We describe how age matching is a specific example of a larger class of conditional abundance matching models (CAM), a theoretical framework we introduce here for the first time. CAM provides a general formalism to study correlations at fixed mass between any galaxy property and any halo property. The striking success of our simple implementation of CAM suggests that this technique has the potential to describe the same set of data as alternative models, but with a dramatic reduction in the required number of parameters. CAM achieves this reduction by exploiting the capability of contemporary N-body simulations to determine dark matter halo properties other than mass alone, which distinguishes our model from conventional approaches to the galaxy-halo connection.

Floodplain dynamics control the age distribution of organic carbon in large rivers

NASA Astrophysics Data System (ADS)

Torres, M. A.; Limaye, A. B. S.; Ganti, V.; West, A. J.; Fischer, W. W.; Lamb, M. P.

2016-12-01

As sediments transit through river systems, they are temporarily stored within floodplains. This storage is important for geochemical cycles because it imparts a certain cadence to weathering processes and organic carbon cycling. However, the time and length scales over which these processes operate are poorly known. To address this, we developed a model for the distribution of storage times in floodplains and used it to make predictions of the age distribution of riverine particulate organic carbon (POC) that can be compared with data from a range of rivers.Using statistics generated from a numerical model of river meandering that accounts for the rates of lateral channel migration and the lengths of channel needed to exchange the sediment flux with the floodplain, we estimated the distribution of sediment storage times. Importantly, this approach consistently yields a heavy-tailed distribution of storage times. This finding, based on comprehensive simulations of a wide range of river conditions, arises because of geometrical constraints that lead to the preferential erosion and reworking of young deposits. To benchmark our model, we compared our results with meteoric 10Be data (a storage time proxy) from Amazonian rivers. Our model correctly predicts observed 10Be concentrations, and consequently appears to capture the correct characteristic timescales associated with floodplain storage. By coupling a simple model of carbon cycling with our floodplain storage model, we are able to make predictions about the radiocarbon content of riverine POC. We observe that floodplains with greater storage times tend to have biospheric POC with a lower radiocarbon content (after correcting bulk ages for contribution from radiocarbon-dead petrogenic carbon). This result confirms that storage plays a key role in setting the age of POC transported by rivers with important implications for the dynamics of the global carbon cycle.

Longitudinal predictive ability of mapping models: examining post-intervention EQ-5D utilities derived from baseline MHAQ data in rheumatoid arthritis patients.

PubMed

Kontodimopoulos, Nick; Bozios, Panagiotis; Yfantopoulos, John; Niakas, Dimitris

2013-04-01

The purpose of this methodological study was to to provide insight into the under-addressed issue of the longitudinal predictive ability of mapping models. Post-intervention predicted and reported utilities were compared, and the effect of disease severity on the observed differences was examined. A cohort of 120 rheumatoid arthritis (RA) patients (60.0% female, mean age 59.0) embarking on therapy with biological agents completed the Modified Health Assessment Questionnaire (MHAQ) and the EQ-5D at baseline, and at 3, 6 and 12 months post-intervention. OLS regression produced a mapping equation to estimate post-intervention EQ-5D utilities from baseline MHAQ data. Predicted and reported utilities were compared with t test, and the prediction error was modeled, using fixed effects, in terms of covariates such as age, gender, time, disease duration, treatment, RF, DAS28 score, predicted and reported EQ-5D. The OLS model (RMSE = 0.207, R(2) = 45.2%) consistently underestimated future utilities, with a mean prediction error of 6.5%. Mean absolute differences between reported and predicted EQ-5D utilities at 3, 6 and 12 months exceeded the typically reported MID of the EQ-5D (0.03). According to the fixed-effects model, time, lower predicted EQ-5D and higher DAS28 scores had a significant impact on prediction errors, which appeared increasingly negative for lower reported EQ-5D scores, i.e., predicted utilities tended to be lower than reported ones in more severe health states. This study builds upon existing research having demonstrated the potential usefulness of mapping disease-specific instruments onto utility measures. The specific issue of longitudinal validity is addressed, as mapping models derived from baseline patients need to be validated on post-therapy samples. The underestimation of post-treatment utilities in the present study, at least in more severe patients, warrants further research before it is prudent to conduct cost-utility analyses in the context of RA by means of the MHAQ alone.

Genetic and clinical predictors of ovarian response in assisted reproductive technology

NASA Astrophysics Data System (ADS)

Wiweko, B.; Damayanti, I.; Suryandari, D.; Natadisastra, M.; Pratama, G.; Sumapraja, K.; Meutia, K.; Iffanolia, P.; Harzief, A. K.; Hestiantoro, A.

2017-08-01

Several factors are known to influence ovarian response to rFSH stimulation such as age, antral follicle count (AFC), and basal FSH level, Mutation of allele Ser680Asn in FSHR gene was responsible to ovarian resistance toward exogenous FSH. The aim of this study is to develop a prediction model of ovarian response to COS in IVF. This study was a prospective cohort study. One hundred and thirteen women undergoing their first cycle of IVF in Yasmin IVF Clinic Jakarta were recruited to this study. Clinical datas included were age, BMI, and AFC. Basal FSH and E2 as well as serum AMH was measured from peripheral blood taken at second day of cycle. Bsr-1 enzyme is used to identify the polymorphism in exon 10 position 680 with RFLP technique. Three genotype polymorphism, Asn/Asn (255 bp ribbon), Asn/Ser (97 bp and 158 bp), and Ser/Ser (97 bp, 158 bp, and 255 bp). AFC has the highest predictor for ovarian response with AUC 0.922 (CI 95% 0.833-1.000). AMH also showed high predicting value (AUC 0.843 CI 95% 0.663-1.000). The multivariate analysis revealed combination of AFC, AMH, age, and basal FSH is a good model for ovarian response prediction (AUC=0.97). No significant relation between Asn/Asn, Asn/Ser, or Ser/Ser genotype FSHR polymorphism with ovarian response (p = 0.866) and total dose of rRSH (p = 0.08). This study showed that model combination of AFC, AMH, patient’s age and basal FSH are very good to predict number of mature oocytes.

Computer model for electrochemical cell performance loss over time in terms of capacity, power, and conductance (CPC)

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

Gering, Kevin L.

2015-09-01

Available capacity, power, and cell conductance figure centrally into performance characterization of electrochemical cells (such as Li-ion cells) over their service life. For example, capacity loss in Li-ion cells is due to a combination of mechanisms, including loss of free available lithium, loss of active host sites, shifts in the potential-capacity curve, etc. Further distinctions can be made regarding irreversible and reversible capacity loss mechanisms. There are tandem needs for accurate interpretation of capacity at characterization conditions (cycling rate, temperature, etc.) and for robust self-consistent modeling techniques that can be used for diagnostic analysis of cell data as well asmore » forecasting of future performance. Analogous issues exist for aging effects on cell conductance and available power. To address these needs, a modeling capability was developed that provides a systematic analysis of the contributing factors to battery performance loss over aging and to act as a regression/prediction platform for cell performance. The modeling basis is a summation of self-consistent chemical kinetics rate expressions, which as individual expressions each covers a distinct mechanism (e.g., loss of active host sites, lithium loss), but collectively account for the net loss of premier metrics (e.g., capacity) over time for a particular characterization condition. Specifically, sigmoid-based rate expressions are utilized to describe each contribution to performance loss. Through additional mathematical development another tier of expressions is derived and used to perform differential analyses and segregate irreversible versus reversible contributions, as well as to determine concentration profiles over cell aging for affected Li+ ion inventory and fraction of active sites that remain at each time step. Reversible fade components are surmised by comparing fade rates at fast versus slow cycling conditions. The model is easily utilized for predictive calculations so that future capacity performance can be estimated. The invention covers mathematical and theoretical frameworks, and demonstrates application to various Li-ion cells covering test periods that vary in duration, and shows model predictions well past the end of test periods. Version 2.0 Enhancements: the code now covers path-dependent aging scenarios, wherein the framework allows for arbitrarily-chosen aging conditions over a timeline to accommodate prediction of battery aging over a multiplicity of changing conditions. The code framework also allows for cell conductance and power loss evaluations over cell aging, analysis of series strings that contain a thermal anomaly (hot spot), and evaluation of battery thermal management parameters that impact battery lifetimes. Lastly, a comprehensive GUI now resides in the Ver. 2.0 code.« less

Availability, use, and cultivation of support networks as predictors of the well-being of middle-aged and older Chinese: a panel study.

PubMed

Chong, Alice Ming Lin; Cheung, Chau-kiu; Woo, Jean; Kwan, Alex Yui-Huen

2012-01-01

To examine the impact of the availability, use, and cultivation of a support network on the well-being of community-dwelling, middle-aged, and older Chinese. A total of 2,970 Hong Kong Chinese aged 40-74 years were interviewed using a structured questionnaire in 2004. Out of the original group of interviewees, 2,120 (71.4%) were interviewed again in 2005. Structural equation modeling revealed a good fit of the model employing Wave 1 support network data and demographic characteristics to predict Wave 2 well-being. As hypothesized, the availability of important social ties and the cultivation of one's support networks were found to predict well-being one year later, but not the use of support networks to meet emotional, financial, or companion needs after controlling for demographic variables and baseline well-being. Cultivating support networks can be interpreted as positive and active coping. Such cultivation is in line with what socioemotional selectivity theory predicts; specifically, when people age, they become more selective and concentrate on strengthening their relationship with those they are emotionally close to. We argue that network cultivation deserves more attention in theory, practice, and research to strengthen the resilience and adaptability of individuals approaching and experiencing old age.

Interest level in 2-year-olds with autism spectrum disorder predicts rate of verbal, nonverbal, and adaptive skill acquisition.

PubMed

Klintwall, Lars; Macari, Suzanne; Eikeseth, Svein; Chawarska, Katarzyna

2015-11-01

Recent studies have suggested that skill acquisition rates for children with autism spectrum disorders receiving early interventions can be predicted by child motivation. We examined whether level of interest during an Autism Diagnostic Observation Schedule assessment at 2 years predicts subsequent rates of verbal, nonverbal, and adaptive skill acquisition to the age of 3 years. A total of 70 toddlers with autism spectrum disorder, mean age of 21.9 months, were scored using Interest Level Scoring for Autism, quantifying toddlers' interest in toys, social routines, and activities that could serve as reinforcers in an intervention. Adaptive level and mental age were measured concurrently (Time 1) and again after a mean of 16.3 months of treatment (Time 2). Interest Level Scoring for Autism score, Autism Diagnostic Observation Schedule score, adaptive age equivalent, verbal and nonverbal mental age, and intensity of intervention were entered into regression models to predict rates of skill acquisition. Interest level at Time 1 predicted subsequent acquisition rate of adaptive skills (R(2) = 0.36) and verbal mental age (R(2) = 0.30), above and beyond the effects of Time 1 verbal and nonverbal mental ages and Autism Diagnostic Observation Schedule scores. Interest level at Time 1 also contributed (R(2) = 0.30), with treatment intensity, to variance in development of nonverbal mental age. © The Author(s) 2014.

Personalized weight change prediction in the first week of life.

PubMed

Wilbaux, Mélanie; Kasser, Severin; Gromann, Julia; Mancino, Isabella; Coscia, Tania; Lapaire, Olav; van den Anker, Johannes N; Pfister, Marc; Wellmann, Sven

2018-04-11

Almost all neonates show physiological weight loss and consecutive weight gain after birth. The resulting weight change profiles are highly variable as they depend on multiple neonatal and maternal factors. This limits the value of weight nomograms for the early identification of neonates at risk for excessive weight loss and related morbidities. The objective of this study was to characterize weight changes and the effect of supplemental feeding in late preterm and term neonates during the first week of life, to identify and quantify neonatal and maternal influencing factors, and to provide an educational online prediction tool. Longitudinal weight data from 3638 healthy term and late preterm neonates were prospectively recorded up to 7 days of life. Two-thirds (n = 2425) were randomized to develop a semi-mechanistic model characterizing weight change as a balance between time-dependent rates of weight gain and weight loss. The dose-dependent effect of supplemental feeding on weight gain was characterized. A population analysis applying nonlinear mixed-effects modeling was performed using NONMEM 7.3. The model was evaluated on the remaining third of neonates (n = 1213). Key population characteristics (median [range]) of the whole sample were gestational age 39.9 [34.4-42.4] weeks, birth weight 3400 [1980-5580] g, maternal age 32 [15-51] years, cesarean section 26%, and girls 50%. The model demonstrated good predictive performance (bias 0.01%, precision 0.56%), and is able to accurately predict individual weight change (bias 0.15%, precision 1.43%) and the dose-dependent effects of supplemental feeding up to 1 week after birth based on weight measurements during the first 3 days of life, including birth weight, and the following characteristics: gestational age, gender, delivery mode, type of feeding, maternal age, and parity. We present the first mathematical model not only to describe weight change in term and late preterm neonates but also to provide an educational online tool for personalized weight prediction in the first week of life. Copyright © 2018 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

The External Validity of Prediction Models for the Diagnosis of Obstructive Coronary Artery Disease in Patients With Stable Chest Pain: Insights From the PROMISE Trial.

PubMed

Genders, Tessa S S; Coles, Adrian; Hoffmann, Udo; Patel, Manesh R; Mark, Daniel B; Lee, Kerry L; Steyerberg, Ewout W; Hunink, M G Myriam; Douglas, Pamela S

2018-03-01

This study sought to externally validate prediction models for the presence of obstructive coronary artery disease (CAD). A better assessment of the probability of CAD may improve the identification of patients who benefit from noninvasive testing. Stable chest pain patients from the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial with computed tomography angiography (CTA) or invasive coronary angiography (ICA) were included. The authors assumed that patients with CTA showing 0% stenosis and a coronary artery calcium (CAC) score of 0 were free of obstructive CAD (≥50% stenosis) on ICA, and they multiply imputed missing ICA results based on clinical variables and CTA results. Predicted CAD probabilities were calculated using published coefficients for 3 models: basic model (age, sex, chest pain type), clinical model (basic model + diabetes, hypertension, dyslipidemia, and smoking), and clinical + CAC score model. The authors assessed discrimination and calibration, and compared published effects with observed predictor effects. In 3,468 patients (1,805 women; mean 60 years of age; 779 [23%] with obstructive CAD on CTA), the models demonstrated moderate-good discrimination, with C-statistics of 0.69 (95% confidence interval [CI]: 0.67 to 0.72), 0.72 (95% CI: 0.69 to 0.74), and 0.86 (95% CI: 0.85 to 0.88) for the basic, clinical, and clinical + CAC score models, respectively. Calibration was satisfactory although typical chest pain and diabetes were less predictive and CAC score was more predictive than was suggested by the models. Among the 31% of patients for whom the clinical model predicted a low (≤10%) probability of CAD, actual prevalence was 7%; among the 48% for whom the clinical + CAC score model predicted a low probability the observed prevalence was 2%. In 2 sensitivity analyses excluding imputed data, similar results were obtained using CTA as the outcome, whereas in those who underwent ICA the models significantly underestimated CAD probability. Existing clinical prediction models can identify patients with a low probability of obstructive CAD. Obstructive CAD on ICA was imputed for 61% of patients; hence, further validation is necessary. Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Predictors of treatment failure in young patients undergoing in vitro fertilization.

PubMed

Jacobs, Marni B; Klonoff-Cohen, Hillary; Agarwal, Sanjay; Kritz-Silverstein, Donna; Lindsay, Suzanne; Garzo, V Gabriel

2016-08-01

The purpose of the study was to evaluate whether routinely collected clinical factors can predict in vitro fertilization (IVF) failure among young, "good prognosis" patients predominantly with secondary infertility who are less than 35 years of age. Using de-identified clinic records, 414 women <35 years undergoing their first autologous IVF cycle were identified. Logistic regression was used to identify patient-driven clinical factors routinely collected during fertility treatment that could be used to model predicted probability of cycle failure. One hundred ninety-seven patients with both primary and secondary infertility had a failed IVF cycle, and 217 with secondary infertility had a successful live birth. None of the women with primary infertility had a successful live birth. The significant predictors for IVF cycle failure among young patients were fewer previous live births, history of biochemical pregnancies or spontaneous abortions, lower baseline antral follicle count, higher total gonadotropin dose, unknown infertility diagnosis, and lack of at least one fair to good quality embryo. The full model showed good predictive value (c = 0.885) for estimating risk of cycle failure; at ≥80 % predicted probability of failure, sensitivity = 55.4 %, specificity = 97.5 %, positive predictive value = 95.4 %, and negative predictive value = 69.8 %. If this predictive model is validated in future studies, it could be beneficial for predicting IVF failure in good prognosis women under the age of 35 years.

Determinants of tree quality and lumber value in natural uneven-aged southern pine stands

Treesearch

Jeffrey P. Prestemon; Joseph Buongiorno

2000-01-01

An ordered-probit model was developed to predict tree grade from tree- and stand-level variables, some of which could be changed by management. Applied to uneven-aged mixed loblolly (Pinus taeda L.) - shortleaf pine (Pinus echinata Mill.) stands, the model showed that the grade of pine trees was highly correlated with tree diameter...

Adult Body Height Is a Good Predictor of Different Dimensions of Cognitive Function in Aged Individuals: A Cross-Sectional Study.

PubMed

Pereira, Vitor H; Costa, Patrício S; Santos, Nadine C; Cunha, Pedro G; Correia-Neves, Margarida; Palha, Joana A; Sousa, Nuno

2016-01-01

Background: Adult height, weight, and adiposity measures have been suggested by some studies to be predictors of depression, cognitive impairment, and dementia. However, the presence of confounding factors and the lack of a thorough neuropsychological evaluation in many of these studies have precluded a definitive conclusion about the influence of anthropometric measures in cognition and depression. In this study we aimed to assess the value of height, weight, and abdominal perimeter to predict cognitive impairment and depressive symptoms in aged individuals. Methods and Findings: Cross-sectional study performed between 2010 and 2012 in the Portuguese general community. A total of 1050 participants were included in the study and randomly selected from local area health authority registries. The cohort was representative of the general Portuguese population with respect to age (above 50 years of age) and gender. Cognitive function was assessed using a battery of tests grouped in two dimensions: general executive function and memory. Two-step hierarchical multiple linear regression models were conducted to determine the predictive value of anthropometric measures in cognitive performance and mood before and after correction for possible confounding factors (gender, age, school years, physical activity, alcohol consumption, and smoking habits). We found single associations of weight, height, body mass index, abdominal perimeter, and age with executive function, memory and depressive symptoms. However, when included in a predictive model adjusted for gender, age, school years, and lifestyle factors only height prevailed as a significant predictor of general executive function (β = 0.139; p < 0.001) and memory (β = 0.099; p < 0.05). No relation was found between mood and any of the anthropometric measures studied. Conclusions and Relevance: Height is an independent predictor of cognitive function in late-life and its effects on the general and executive function and memory are independent of age, weight, education level, gender, and lifestyle factors. Altogether, our data suggests that modulators of adult height during childhood may irreversibly contribute to cognitive function in adult life and that height should be used in models to predict cognitive performance.

Assessment of resource selection models to predict occurrence of five juvenile flatfish species (Pleuronectidae) over the continental shelf in the western Gulf of Alaska

NASA Astrophysics Data System (ADS)

Wilson, Matthew T.; Mier, Kathryn L.; Cooper, Dan W.

2016-05-01

According to the nursery size hypothesis, flatfish recruitment is constrained by nursery area. Thus, if resource selection models can be shown to accurately predict the location and geographic extent of flatfish nursery areas, they will become important tools in the management and study of flatfish population dynamics. We demonstrate that some resource selection models derived previously to predict the presence and absence of juvenile flatfishes near shore were applicable to the broader continental shelf. For other age-species groups, derivation of new models for the continental shelf was necessary. Our study was conducted in the western Gulf of Alaska (GoA) during October 2011 on four groups of age-0 juvenile flatfishes: Pacific halibut (Hippoglossus stenolepis), arrowtooth flounder (Atheresthes stomias), northern rock sole (Lepidopsetta polyxystra), and flathead sole (Hippoglossoides elassodon); and three groups of age-1 juvenile flatfishes: northern rock sole, flathead sole, and yellowfin sole (Limanda aspera). Sampling occurred at 33 sites across the continental shelf. Fish were collected using a 3-m beam trawl, and a midwater trawl. Environmental data were collected on sediment composition and water temperature and depth. Many of the age-species groups co-occurred in the Shumagin and Barnabus sea valleys; however, age-0 arrowtooth flounder occurred at more locations than other juveniles, perhaps due to a relatively broad tolerance of environmental conditions and to the utilization of midwater habitat. Thus, the large nursery area of arrowtooth flounder may be one reason why they are currently the most abundant GoA flatfish. In fact, among all species, mean recruitment at age 3 increased with the percent occurrence of age-0 juveniles at the 33 sites, a proxy for relative nursery area, in accordance with the nursery size hypothesis, suggesting that mean recruitment among GoA flatfishes is structured by nursery size.

Size at emergence improves accuracy of age estimates in forensically-useful beetle Creophilus maxillosus L. (Staphylinidae).

PubMed

Matuszewski, Szymon; Frątczak-Łagiewska, Katarzyna

2018-02-05

Insects colonizing human or animal cadavers may be used to estimate post-mortem interval (PMI) usually by aging larvae or pupae sampled on a crime scene. The accuracy of insect age estimates in a forensic context is reduced by large intraspecific variation in insect development time. Here we test the concept that insect size at emergence may be used to predict insect physiological age and accordingly to improve the accuracy of age estimates in forensic entomology. Using results of laboratory study on development of forensically-useful beetle Creophilus maxillosus (Linnaeus, 1758) (Staphylinidae) we demonstrate that its physiological age at emergence [i.e. thermal summation value (K) needed for emergence] fall with an increase of beetle size. In the validation study it was found that K estimated based on the adult insect size was significantly closer to the true K as compared to K from the general thermal summation model. Using beetle length at emergence as a predictor variable and male or female specific model regressing K against beetle length gave the most accurate predictions of age. These results demonstrate that size of C. maxillosus at emergence improves accuracy of age estimates in a forensic context.

A Predictive Mathematical Model of Muscle Forces for Children with Cerebral Palsy

ERIC Educational Resources Information Center

Lee, Samuel C. K.; Ding, Jun; Prosser, Laura A.; Wexler, Anthony S.; Binder-Macleod, Stuart A.

2009-01-01

Aim: The purpose of this study was to determine if our previously developed muscle model could be used to predict forces of the quadriceps femoris and triceps surae muscles of children with spastic diplegic cerebral palsy (CP). Method: Twenty-two children with CP (12 males, 10 females; mean age 10y, SD 2y, range 7-13y; Gross Motor Function…

Monitoring of chicken meat freshness by means of a colorimetric sensor array.

PubMed

Salinas, Yolanda; Ros-Lis, José V; Vivancos, José-L; Martínez-Máñez, Ramón; Marcos, M Dolores; Aucejo, Susana; Herranz, Nuria; Lorente, Inmaculada

2012-08-21

A new optoelectronic nose to monitor chicken meat ageing has been developed. It is based on 16 pigments prepared by the incorporation of different dyes (pH indicators, Lewis acids, hydrogen-bonding derivatives, selective probes and natural dyes) into inorganic materials (UVM-7, silica and alumina). The colour changes of the sensor array were characteristic of chicken ageing in a modified packaging atmosphere (30% CO(2)-70% N(2)). The chromogenic array data were processed with qualitative (PCA) and quantitative (PLS) tools. The PCA statistical analysis showed a high degree of dispersion, with nine dimensions required to explain 95% of variance. Despite this high dimensionality, a tridimensional representation of the three principal components was able to differentiate ageing with 2-day intervals. Moreover, the PLS statistical analysis allows the creation of a model to correlate the chromogenic data with chicken meat ageing. The model offers a PLS prediction model for ageing with values of 0.9937, 0.0389 and 0.994 for the slope, the intercept and the regression coefficient, respectively, and is in agreement with the perfect fit between the predicted and measured values observed. The results suggest the feasibility of this system to help develop optoelectronic noses that monitor food freshness.

Predicting the Impact of Vaccination on the Transmission Dynamics of Typhoid in South Asia: A Mathematical Modeling Study

PubMed Central

Pitzer, Virginia E.; Bowles, Cayley C.; Baker, Stephen; Kang, Gagandeep; Balaji, Veeraraghavan; Farrar, Jeremy J.; Grenfell, Bryan T.

2014-01-01

Background Modeling of the transmission dynamics of typhoid allows for an evaluation of the potential direct and indirect effects of vaccination; however, relevant typhoid models rooted in data have rarely been deployed. Methodology/Principal Findings We developed a parsimonious age-structured model describing the natural history and immunity to typhoid infection. The model was fit to data on culture-confirmed cases of typhoid fever presenting to Christian Medical College hospital in Vellore, India from 2000–2012. The model was then used to evaluate the potential impact of school-based vaccination strategies using live oral, Vi-polysaccharide, and Vi-conjugate vaccines. The model was able to reproduce the incidence and age distribution of typhoid cases in Vellore. The basic reproductive number (R 0) of typhoid was estimated to be 2.8 in this setting. Vaccination was predicted to confer substantial indirect protection leading to a decrease in the incidence of typhoid in the short term, but (intuitively) typhoid incidence was predicted to rebound 5–15 years following a one-time campaign. Conclusions/Significance We found that model predictions for the overall and indirect effects of vaccination depend strongly on the role of chronic carriers in transmission. Carrier transmissibility was tentatively estimated to be low, consistent with recent studies, but was identified as a pivotal area for future research. It is unlikely that typhoid can be eliminated from endemic settings through vaccination alone. PMID:24416466

Exploring the social dimension of sandy beaches through predictive modelling.

PubMed

Domínguez-Tejo, Elianny; Metternicht, Graciela; Johnston, Emma L; Hedge, Luke

2018-05-15

Sandy beaches are unique ecosystems increasingly exposed to human-induced pressures. Consistent with emerging frameworks promoting this holistic approach towards beach management, is the need to improve the integration of social data into management practices. This paper aims to increase understanding of links between demographics and community values and preferred beach activities, as key components of the social dimension of the beach environment. A mixed method approach was adopted to elucidate users' opinions on beach preferences and community values through a survey carried out in Manly Local Government Area in Sydney Harbour, Australia. A proposed conceptual model was used to frame demographic models (using age, education, employment, household income and residence status) as predictors of these two community responses. All possible regression-model combinations were compared using Akaike's information criterion. Best models were then used to calculate quantitative likelihoods of the responses, presented as heat maps. Findings concur with international research indicating the relevance of social and restful activities as important social links between the community and the beach environment. Participant's age was a significant variable in the four predictive models. The use of predictive models informed by demographics could potentially increase our understanding of interactions between the social and ecological systems of the beach environment, as a prelude to integrated beach management approaches. The research represents a practical demonstration of how demographic predictive models could support proactive approaches to beach management. Copyright © 2018 Elsevier Ltd. All rights reserved.

"Despair" induced by extinction trials in the water maze: relationship with measures of anxiety in aged and adult rats.

PubMed

Schulz, Daniela; Huston, Joseph P; Buddenberg, Tim; Topic, Bianca

2007-03-01

We have previously reported that extinction of escape behavior in the water maze due to the removal of the platform coincided with the development of behavioral "despair" in aged and adult rats, as assessed by immobility. The present study examines further predictions derived from the hypothesis that the withholding of reinforcement induces behaviors akin to depression. We tested for correlations between extinction performance and immobility, as well as between immobility and measures of anxiety in aged and adult rats. Age comparisons were also performed on these variables. Forty aged and 29 adult male Wistar rats (24 and 3 months old, respectively) were examined in the open field, black/white box and elevated-plus maze followed by 6 days of training in the water maze hidden platform task and 8 days of extinction without the platform. Indices of immobility increased over trials of extinction, with the aged showing higher levels, earlier onsets and larger slope increases of immobility than the adults. A lower resistance-to-extinction was predictive of more "despair" in both age groups. Between-group differences in the open field, black/white box and elevated-plus maze indicated that the aged showed more anxiety-like behavior than the adults and/or explored these environments less. Within the aged group, indicators of fearfulness in the three tests were predictive of higher levels of "despair". The extinction-despair model is held to provide the promise of a conceptual and empirical model of human depression that is the consequence of withdrawal of reinforcement.

Estimating Stage Specific Vital Rate Responses to Stress Within Mixed Age Populations of the Opossum Shrimp Americamysis bahia Using Digital Imaging

EPA Science Inventory

Most observations of stressor effects on marine crustaceans are made on individuals or even-aged cohorts. Results of these studies are difficult to translate into ecological predictions, either because life cycle models are incomplete, or because stressor effects on mixed age po...

Simulation of κ-Carbide Precipitation Kinetics in Aged Low-Density Fe-Mn-Al-C Steels and Its Effects on Strengthening

NASA Astrophysics Data System (ADS)

Lee, Jaeeun; Park, Siwook; Kim, Hwangsun; Park, Seong-Jun; Lee, Keunho; Kim, Mi-Young; Madakashira, Phaniraj P.; Han, Heung Nam

2018-03-01

Fe-Al-Mn-C alloy systems are low-density austenite-based steels that show excellent mechanical properties. After aging such steels at adequate temperatures for adequate time, nano-scale precipitates such as κ-carbide form, which have profound effects on the mechanical properties. Therefore, it is important to predict the amount and size of the generated κ-carbide precipitates in order to control the mechanical properties of low-density steels. In this study, the microstructure and mechanical properties of aged low-density austenitic steel were characterized. Thermo-kinetic simulations of the aging process were used to predict the size and phase fraction of κ-carbide after different aging periods, and these results were validated by comparison with experimental data derived from dark-field transmission electron microscopy images. Based on these results, models for precipitation strengthening based on different mechanisms were assessed. The measured increase in the strength of aged specimens was compared with that calculated from the models to determine the exact precipitation strengthening mechanism.

Mammographic density, breast cancer risk and risk prediction

PubMed Central

Vachon, Celine M; van Gils, Carla H; Sellers, Thomas A; Ghosh, Karthik; Pruthi, Sandhya; Brandt, Kathleen R; Pankratz, V Shane

2007-01-01

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models. PMID:18190724

Health-Related Quality of Life in a Predictive Model for Mortality in Older Breast Cancer Survivors.

PubMed

DuMontier, Clark; Clough-Gorr, Kerri M; Silliman, Rebecca A; Stuck, Andreas E; Moser, André

2018-03-13

To develop a predictive model and risk score for 10-year mortality using health-related quality of life (HRQOL) in a cohort of older women with early-stage breast cancer. Prospective cohort. Community. U.S. women aged 65 and older diagnosed with Stage I to IIIA primary breast cancer (N=660). We used medical variables (age, comorbidity), HRQOL measures (10-item Physical Function Index and 5-item Mental Health Index from the Medical Outcomes Study (MOS) 36-item Short-Form Survey; 8-item Modified MOS Social Support Survey), and breast cancer variables (stage, surgery, chemotherapy, endocrine therapy) to develop a 10-year mortality risk score using penalized logistic regression models. We assessed model discriminative performance using the area under the receiver operating characteristic curve (AUC), calibration performance using the Hosmer-Lemeshow test, and overall model performance using Nagelkerke R 2 (NR). Compared to a model including only age, comorbidity, and cancer stage and treatment variables, adding HRQOL variables improved discrimination (AUC 0.742 from 0.715) and overall performance (NR 0.221 from 0.190) with good calibration (p=0.96 from HL test). In a cohort of older women with early-stage breast cancer, HRQOL measures predict 10-year mortality independently of traditional breast cancer prognostic variables. These findings suggest that interventions aimed at improving physical function, mental health, and social support might improve both HRQOL and survival. © 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.

Predicting Madura cattle growth curve using non-linear model

NASA Astrophysics Data System (ADS)

Widyas, N.; Prastowo, S.; Widi, T. S. M.; Baliarti, E.

2018-03-01

Madura cattle is Indonesian native. It is a composite breed that has undergone hundreds of years of selection and domestication to reach nowadays remarkable uniformity. Crossbreeding has reached the isle of Madura and the Madrasin, a cross between Madura cows and Limousine semen emerged. This paper aimed to compare the growth curve between Madrasin and one type of pure Madura cows, the common Madura cattle (Madura) using non-linear models. Madura cattles are kept traditionally thus reliable records are hardly available. Data were collected from small holder farmers in Madura. Cows from different age classes (<6 months, 6-12 months, 1-2years, 2-3years, 3-5years and >5years) were observed, and body measurements (chest girth, body length and wither height) were taken. In total 63 Madura and 120 Madrasin records obtained. Linear model was built with cattle sub-populations and age as explanatory variables. Body weights were estimated based on the chest girth. Growth curves were built using logistic regression. Results showed that within the same age, Madrasin has significantly larger body compared to Madura (p<0.05). The logistic models fit better for Madura and Madrasin cattle data; with the estimated MSE for these models were 39.09 and 759.28 with prediction accuracy of 99 and 92% for Madura and Madrasin, respectively. Prediction of growth curve using logistic regression model performed well in both types of Madura cattle. However, attempts to administer accurate data on Madura cattle are necessary to better characterize and study these cattle.

Forensic individual age estimation with DNA: From initial approaches to methylation tests.

PubMed

Freire-Aradas, A; Phillips, C; Lareu, M V

2017-07-01

Individual age estimation is a key factor in forensic science analysis that can provide very useful information applicable to criminal, legal, and anthropological investigations. Forensic age inference was initially based on morphological inspection or radiography and only later began to adopt molecular approaches. However, a lack of accuracy or technical problems hampered the introduction of these DNA-based methodologies in casework analysis. A turning point occurred when the epigenetic signature of DNA methylation was observed to gradually change during an individual´s lifespan. In the last four years, the number of publications reporting DNA methylation age-correlated changes has gradually risen and the forensic community now has a range of age methylation tests applicable to forensic casework. Most forensic age predictor models have been developed based on blood DNA samples, but additional tissues are now also being explored. This review assesses the most widely adopted genes harboring methylation sites, detection technologies, statistical age-predictive analyses, and potential causes of variation in age estimates. Despite the need for further work to improve predictive accuracy and establishing a broader range of tissues for which tests can analyze the most appropriate methylation sites, several forensic age predictors have now been reported that provide consistency in their prediction accuracies (predictive error of ±4 years); this makes them compelling tools with the potential to contribute key information to help guide criminal investigations. Copyright © 2017 Central Police University.

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 lifestyle activities, predicted variability across both verbal and visuospatial domains in aging. These findings are suggestive of different use-dependent adaptation periods depending on cognitive domain. Furthermore, they imply that early age of musical acquisition, sustained and maintained during advanced age, may enhance cognitive functions and buffer age and education influences.

Clinical Prediction Rule for Declines in Activities of Daily Living at 6 Months After Surgery for Hip Fracture Repair.

PubMed

Tanaka, Ryo; Umehara, Takuya; Fujimura, Takafumi; Ozawa, Junya

2016-12-01

To develop and assess a clinical prediction rule (CPR) to predict declines in activities of daily living (ADL) at 6 months after surgery for hip fracture repair. Prospective, cohort study. From hospital to home. Patients (N=104) with hip fractures after surgery. Not applicable. ADL were assessed using the Barthel Index at 6 months after surgery. At 6 months after surgery, 86 patients (82.6%) were known to be alive, 1 patient (1.0%) had died, and 17 (16.3%) were lost to follow-up. Thirty-two patients (37.2%) did not recover their ADL at 6 months after surgery to levels before fracture. The classification and regression trees methodology was used to develop 2 models to predict a decline in ADL: (1) model 1 included age, type of fracture, and care level before fracture (sensitivity=75.0%, specificity=81.5%, positive predictive value=70.6%, positive likelihood ratio=4.050); and (2) model 2 included the degree of independence 2 weeks postsurgery for ADL chair transfer, ADL ambulation, and age (sensitivity=65.6%, specificity=87.0%, positive predictive value=75.0%, positive likelihood ratio=5.063). The areas under the receiver operating characteristic curves of both CPR models were .825 (95% confidential interval, .728-.923) and .790 (95% confidence interval, .683-.897), respectively. CPRs with moderate accuracy were developed to predict declines in ADL at 6 months after surgery for hip fracture repair. Copyright © 2016 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Only as Happy as the Least Happy Child: Multiple Grown Children's Problems and Successes and Middle-aged Parents’ Well-being

PubMed Central

Cheng, Yen-Pi; Birditt, Kira; Zarit, Steven

2012-01-01

Objectives. Middle-aged parents’ well-being may be tied to successes and failures of grown children. Moreover, most parents have more than one child, but studies have not considered how different children's successes and failures may be associated with parental well-being. Methods. Middle-aged adults (aged 40–60; N = 633) reported on each of their grown children (n = 1,384) and rated their own well-being. Participants indicated problems each child had experienced in the past two years, rated their children's successes, as well as positive and negative relationship qualities. Results. Analyses compared an exposure model (i.e., having one grown child with a problem or deemed successful) and a cumulative model (i.e., total problems or successes in the family). Consistent with the exposure and cumulative models, having one child with problems predicted poorer parental well-being and the more problems in the family, the worse parental well-being. Having one successful child did not predict well-being, but multiple grown children with higher total success in the family predicted enhanced parental well-being. Relationship qualities partially explained associations between children's successes and parental well-being. Discussion. Discussion focuses on benefits and detriments parents derive from how grown progeny turn out and particularly the implications of grown children's problems. PMID:21856677

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

PubMed

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

2017-07-01

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

The psychometrics and validity of the Junior Temperament and Character Inventory in Portuguese adolescents.

PubMed

Moreira, Paulo A; Oliveira, João Tiago; Cloninger, Kevin M; Azevedo, Carla; Sousa, Alexandra; Castro, Jorge; Cloninger, C Robert

2012-11-01

Personality traits related to persistence and self-regulation of long-term goals can predict academic performance as well or better than measures of intelligence. The 5-factor model has been suggested to outperform some other personality tests in predicting academic performance, but it has not been compared to Cloninger's psychobiological model for this purpose. The aims of this study were, first, to evaluate the psychometric properties of the Junior Temperament and Character Inventory (JTCI) in adolescents in Portugal, and second, to evaluate the comparative validity of age-appropriate versions of Cloninger's 7-factor psychobiological model, Costa and McCrae's five-factor NEO-Personality Inventory-Revised, and Cattell's 16-personality-factor inventory in predicting academic achievement. All dimensions of the Portuguese JTCI had moderate to strong internal consistency. The Cattell's sixteen-personality-factor and NEO inventories provided strong construct validity for the JTCI in students younger than 17 years and for the revised adult version (TCI-Revised) in those 17 years and older. High TCI Persistence predicted school grades regardless of age as much or more than intelligence. High TCI Harm Avoidance, high Self-Transcendence, and low TCI Novelty Seeking were additional predictors in students older than 17. The psychobiological model, as measured by the JTCI and TCI-Revised, performed as well or better than other measures of personality or intelligence in predicting academic achievement. Copyright © 2012 Elsevier Inc. All rights reserved.

Central adiposity is negatively associated with hippocampal-dependent relational memory among overweight and obese children.

PubMed

Khan, Naiman A; Baym, Carol L; Monti, Jim M; Raine, Lauren B; Drollette, Eric S; Scudder, Mark R; Moore, R Davis; Kramer, Arthur F; Hillman, Charles H; Cohen, Neal J

2015-02-01

To assess associations between adiposity and hippocampal-dependent and hippocampal-independent memory forms among prepubertal children. Prepubertal children (age 7-9 years; n = 126), classified as non-overweight (<85th percentile body mass index [BMI]-for-age [n = 73]) or overweight/obese (≥85th percentile BMI-for-age [n = 53]), completed relational (hippocampal-dependent) and item (hippocampal-independent) memory tasks. Performance was assessed with both direct (behavioral accuracy) and indirect (preferential disproportionate viewing [PDV]) measures. Adiposity (ie, percent whole-body fat mass, subcutaneous abdominal adipose tissue, visceral adipose tissue, and total abdominal adipose tissue) was assessed by dual-energy X-ray absorptiometry. Backward regression identified significant (P < .05) predictive models of memory performance. Covariates included age, sex, pubertal timing, socioeconomic status (SES), IQ, oxygen consumption, and BMI z-score. Among overweight/obese children, total abdominal adipose tissue was a significant negative predictor of relational memory behavioral accuracy, and pubertal timing together with SES jointly predicted the PDV measure of relational memory. In contrast, among non-overweight children, male sex predicted item memory behavioral accuracy, and a model consisting of SES and BMI z-score jointly predicted the PDV measure of relational memory. Regional, but not whole-body, fat deposition was selectively and negatively associated with hippocampal-dependent relational memory among overweight/obese prepubertal children. Copyright © 2015 Elsevier Inc. All rights reserved.

Pediatric ocular trauma score as a prognostic tool in the management of pediatric traumatic cataracts.

PubMed

Shah, Mehul A; Agrawal, Rupesh; Teoh, Ryan; Shah, Shreya M; Patel, Kashyap; Gupta, Satyam; Gosai, Siddharth

2017-05-01

To introduce and validate the pediatric ocular trauma score (POTS) - a mathematical model to predict visual outcome trauma in children with traumatic cataract METHODS: In this retrospective cohort study, medical records of consecutive children with traumatic cataracts aged 18 and below were retrieved and analysed. Data collected included age, gender, visual acuity, anterior segment and posterior segment findings, nature of surgery, treatment for amblyopia, follow-up, and final outcome was recorded on a precoded data information sheet. POTS was derived based on the ocular trauma score (OTS), adjusting for age of patient and location of the injury. Visual outcome was predicted using the OTS and the POTS and using receiver operating characteristic (ROC) curves. POTS predicted outcomes were more accurate compared to that of OTS (p = 0.014). POTS is a more sensitive and specific score with more accurate predicted outcomes compared to OTS, and is a viable tool to predict visual outcomes of pediatric ocular trauma with traumatic cataract.

Prediction of BP reactivity to talking using hybrid soft computing approaches.

PubMed

Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar

2014-01-01

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R (2)), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Utility of existing diabetes risk prediction tools for young black and white adults: Evidence from the Bogalusa Heart Study.

PubMed

Pollock, Benjamin D; Hu, Tian; Chen, Wei; Harville, Emily W; Li, Shengxu; Webber, Larry S; Fonseca, Vivian; Bazzano, Lydia A

2017-01-01

To evaluate several adult diabetes risk calculation tools for predicting the development of incident diabetes and pre-diabetes in a bi-racial, young adult population. Surveys beginning in young adulthood (baseline age ≥18) and continuing across multiple decades for 2122 participants of the Bogalusa Heart Study were used to test the associations of five well-known adult diabetes risk scores with incident diabetes and pre-diabetes using separate Cox models for each risk score. Racial differences were tested within each model. Predictive utility and discrimination were determined for each risk score using the Net Reclassification Index (NRI) and Harrell's c-statistic. All risk scores were strongly associated (p<.0001) with incident diabetes and pre-diabetes. The Wilson model indicated greater risk of diabetes for blacks versus whites with equivalent risk scores (HR=1.59; 95% CI 1.11-2.28; p=.01). C-statistics for the diabetes risk models ranged from 0.79 to 0.83. Non-event NRIs indicated high specificity (non-event NRIs: 76%-88%), but poor sensitivity (event NRIs: -23% to -3%). Five diabetes risk scores established in middle-aged, racially homogenous adult populations are generally applicable to younger adults with good specificity but poor sensitivity. The addition of race to these models did not result in greater predictive capabilities. A more sensitive risk score to predict diabetes in younger adults is needed. Copyright © 2017 Elsevier Inc. All rights reserved.

Age and total and free prostate-specific antigen levels for predicting prostate volume in patients with benign prostatic hyperplasia.

PubMed

Coban, Soner; Doluoglu, Omer Gokhan; Keles, Ibrahim; Demirci, Hakan; Turkoglu, Ali Riza; Guzelsoy, Muhammet; Karalar, Mustafa; Demirbas, Murat

2016-06-01

To investigate the predictive values of free prostate-specific antigen (fPSA), total PSA (tPSA) and age on the prostate volume. The data of 2148 patients with lower urinary tract symptoms were analyzed retrospectively. The patients who had transrectal ultrasonography guided 10 core biopsies owing to the findings obtained on digital rectal examination and presence of high PSA levels (PSA = 2.5-10 ng/dl), and proven to have BPH histopathologically were included in the study. Age, tPSA, fPSA and the prostate volumes (PV) of the patients were noted. One thousand patients that fulfilled the inclusion criteria were included in the study. The PV of the patients were significantly correlated with age, tPSA and fPSA (p < 0.001 and r = 0.307, p < 0.001 and r = 0.382, p < 0.001 and r = 0.296, respectively). On linear regression model, fPSA was found as a stronger predictive for PV (AUC = 0.75, p < 0.001) when compared to age (AUC = 0.64, p < 0.001), and tPSA (AUC = 0.69, p = 0.013). Although tPSA is an important prognostic factor for predicting PV, the predictive value of fPSA is higher. PV can easily be predicted by using age, and serum tPSA and fPSA levels.

[How exactly can we predict the prognosis of COPD].

PubMed

Atiş, Sibel; Kanik, Arzu; Ozgür, Eylem Sercan; Eker, Suzan; Tümkaya, Münir; Ozge, Cengiz

2009-01-01

Predictive models play a pivotal role in the provision of accurate and useful probabilistic assessments of clinical outcomes in chronic diseases. This study was aimed to develop a dedicated prognostic index for quantifying progression risk in chronic obstructive pulmonary disease (COPD). Data were collected prospectively from 75 COPD patients during a three years period. A predictive model of progression risk of COPD was developed using Bayesian logistic regression analysis by Markov chain Monte Carlo method. One-year cycles were used for the disease progression in this model. Primary end points for progression were impairment in basal dyspne index (BDI) score, FEV(1) decline, and exacerbation frequency in last three years. Time-varying covariates age, smoking, body mass index (BMI), severity of disease according to GOLD, PaO2, PaCO(2), IC, RV/TLC, DLCO were used under the study. The mean age was 57.1 + or - 8.1. BDI were strongly correlated with exacerbation frequency (p= 0.001) but not with FEV(1) decline. BMI was found to be a predictor factor for impairment in BDI (p= 0.03). The following independent risk factors were significant to predict exacerbation frequency: GOLD staging (OR for GOLD I vs. II and III = 2.3 and 4.0), hypoxemia (OR for mild vs moderate and severe = 2.1 and 5.1) and hyperinflation (OR= 1.6). PaO2 (p= 0.026), IC (p= 0.02) and RV/TLC (p= 0.03) were found to be predictive factors for FEV(1) decline. The model estimated BDI, lung function and exacerbation frequency at the last time point by testing initial data of three years with 95% reliability (p< 0.001). Accordingly, this model was evaluated as confident of 95% for assessing the future status of COPD patients. Using Bayesian predictive models, it was possible to develop a risk-stratification index that accurately predicted progression of COPD. This model can provide decision-making about future in COPD patients with high reliability looking clinical data of beginning.

Ages of LMC star clusters using ASAD2

NASA Astrophysics Data System (ADS)

Asa'd, Randa S.; Vazdekis, Alexandre; Zeinelabdin, Sami

2016-04-01

We use ASAD2, the new version of ASAD (Analyzer of Spectra for Age Determination), to obtain the age and reddening of 27 Large Magellanic Cloud (LMC) clusters from full fitting of integrated spectra using different statistical methods [χ2 and Kolmogorov-Smirnov (KS) test] and a set of stellar population models including GALAXEV and MILES. We show that our results are in good agreement with the colour-magnitude diagram (CMD) ages for both models, and that metallicity does not affect the age determination for the full spectrum fitting method regardless of the model used for ages with log (age/year) < 9. We discuss the results obtained by the two statistical results for both GALAXEV and MILES versus three factors: age, signal-to-noise ratio and resolution (full width at half maximum). The predicted reddening values when using the χ2 minimization method are within the range found in the literature for resolved clusters (I.e. <0.35); however the KS test can predict E(B - V) higher values. The sharp spectrum transition originated at ages around the supergiants contribution, at either side of the AGB peak around log (age/year) 9.0 and log (age/year) 7.8 are limiting our ability to provide values in agreement with the CMD estimates and as a result the reddening determination is not accurate. We provide the detailed results of four clusters spanning a wide range of ages. ASAD2 is a user-friendly program available for download on the Web and can be immediately used at http://randaasad.wordpress.com/asad-package/.

Predicting symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage with an artificial neural network in a pediatric population.

PubMed

Skoch, Jesse; Tahir, Rizwan; Abruzzo, Todd; Taylor, John M; Zuccarello, Mario; Vadivelu, Sudhakar

2017-12-01

Artificial neural networks (ANN) are increasingly applied to complex medical problem solving algorithms because their outcome prediction performance is superior to existing multiple regression models. ANN can successfully identify symptomatic cerebral vasospasm (SCV) in adults presenting after aneurysmal subarachnoid hemorrhage (aSAH). Although SCV is unusual in children with aSAH, the clinical consequences are severe. Consequently, reliable tools to predict patients at greatest risk for SCV may have significant value. We applied ANN modeling to a consecutive cohort of pediatric aSAH cases to assess its ability to predict SCV. A retrospective chart review was conducted to identify patients < 21 years of age who presented with spontaneously ruptured, non-traumatic, non-mycotic, non-flow-related intracranial arterial aneurysms to our institution between January 2002 and January 2015. Demographics, clinical, radiographic, and outcome data were analyzed using an adapted ANN model using learned value nodes from the adult aneurysmal SAH dataset previously reported. The strength of the ANN prediction was measured between - 1 and 1 with - 1 representing no likelihood of SCV and 1 representing high likelihood of SCV. Sixteen patients met study inclusion criteria. The median age for aSAH patients was 15 years. Ten underwent surgical clipping and 6 underwent endovascular coiling for definitive treatment. One patient experienced SCV and 15 did not. The ANN applied here was able to accurately predict all 16 outcomes. The mean strength of prediction for those who did not exhibit SCV was - 0.86. The strength for the one patient who did exhibit SCV was 0.93. Adult-derived aneurysmal SAH value nodes can be applied to a simple AAN model to accurately predict SCV in children presenting with aSAH. Further work is needed to determine if ANN models can prospectively predict SCV in the pediatric aSAH population in toto; adapted to include mycotic, traumatic, and flow-related origins as well.

Whole-brain grey matter density predicts balance stability irrespective of age and protects older adults from falling.

PubMed

Boisgontier, Matthieu P; Cheval, Boris; van Ruitenbeek, Peter; Levin, Oron; Renaud, Olivier; Chanal, Julien; Swinnen, Stephan P

2016-03-01

Functional and structural imaging studies have demonstrated the involvement of the brain in balance control. Nevertheless, how decisive grey matter density and white matter microstructural organisation are in predicting balance stability, and especially when linked to the effects of ageing, remains unclear. Standing balance was tested on a platform moving at different frequencies and amplitudes in 30 young and 30 older adults, with eyes open and with eyes closed. Centre of pressure variance was used as an indicator of balance instability. The mean density of grey matter and mean white matter microstructural organisation were measured using voxel-based morphometry and diffusion tensor imaging, respectively. Mixed-effects models were built to analyse the extent to which age, grey matter density, and white matter microstructural organisation predicted balance instability. Results showed that both grey matter density and age independently predicted balance instability. These predictions were reinforced when the level of difficulty of the conditions increased. Furthermore, grey matter predicted balance instability beyond age and at least as consistently as age across conditions. In other words, for balance stability, the level of whole-brain grey matter density is at least as decisive as being young or old. Finally, brain grey matter appeared to be protective against falls in older adults as age increased the probability of losing balance in older adults with low, but not moderate or high grey matter density. No such results were observed for white matter microstructural organisation, thereby reinforcing the specificity of our grey matter findings. Copyright © 2016 Elsevier B.V. All rights reserved.

Improving sexual health communication between older women and their providers: how the integrative model of behavioral prediction can help.

PubMed

Hughes, Anne K; Rostant, Ola S; Curran, Paul G

2014-07-01

Talking about sexual health can be a challenge for some older women. This project was initiated to identify key factors that improve communication between aging women and their primary care providers. A sample of women (aged 60+) completed an online survey regarding their intent to communicate with a provider about sexual health. Using the integrative model of behavioral prediction as a guide, the survey instrument captured data on attitudes, perceived norms, self-efficacy, and intent to communicate with a provider about sexual health. Data were analyzed using structural equation modeling. Self-efficacy and perceived norms were the most important factors predicting intent to communicate for this sample of women. Intent did not vary with race, but mean scores of the predictors of intent varied for African American and White women. Results can guide practice and intervention with ethnically diverse older women who may be struggling to communicate about their sexual health concerns. © The Author(s) 2013.

Towards lidar-based mapping of tree age at the Arctic forest tundra ecotone.

NASA Astrophysics Data System (ADS)

Jensen, J.; Maguire, A.; Oelkers, R.; Andreu-Hayles, L.; Boelman, N.; D'Arrigo, R.; Griffin, K. L.; Jennewein, J. S.; Hiers, E.; Meddens, A. J.; Russell, M.; Vierling, L. A.; Eitel, J.

2017-12-01

Climate change may cause spatial shifts in the forest-tundra ecotone (FTE). To improve our ability to study these spatial shifts, information on tree demography along the FTE is needed. The objective of this study was to assess the suitability of lidar derived tree heights as a surrogate for tree age. We calculated individual tree age from 48 tree cores collected at basal height from white spruce (Picea glauca) within the FTE in northern Alaska. Tree height was obtained from terrestrial lidar scans (<1cm spatial resolution). The relationship between age and height was examined using a linear regression model forced through the origin. We found a very strong predictive relationship between tree height and age (R2 = 0.90, RMSE = 19.34 years) for trees that ranged between 14 to 230 years. Separate regression models were also developed for small (height < 3 m) and large trees (height >= 3 m), yielding strong predictive relationships between height and age (R2 = 0.86, RMSE 12.21 years, and R2 = 0.93, RMSE = 25.16 years, respectively). The slope coefficient for small and large tree models (16.83 and 12.98 years/m, respectively) indicate that small trees grow 1.3 times faster than large trees at these FTE study sites. Although a strong, predictive relationship between age and height is uncommon in light-limited forest environments, our findings suggest that the sparseness of trees within the FTE may explain the strong tree height-age relationships found herein. Further analysis of 36 additional tree cores recently collected within the FTE near Inuvik, Canada will be performed. Our preliminary analysis suggests that lidar derived tree height could be a reliable proxy for tree age at the FTE, thereby establishing a new technique for scaling tree structure and demographics across larger portions of this sensitive ecotone.

A Risk Prediction Model for Sporadic CRC Based on Routine Lab Results.

PubMed

Boursi, Ben; Mamtani, Ronac; Hwang, Wei-Ting; Haynes, Kevin; Yang, Yu-Xiao

2016-07-01

Current risk scores for colorectal cancer (CRC) are based on demographic and behavioral factors and have limited predictive values. To develop a novel risk prediction model for sporadic CRC using clinical and laboratory data in electronic medical records. We conducted a nested case-control study in a UK primary care database. Cases included those with a diagnostic code of CRC, aged 50-85. Each case was matched with four controls using incidence density sampling. CRC predictors were examined using univariate conditional logistic regression. Variables with p value <0.25 in the univariate analysis were further evaluated in multivariate models using backward elimination. Discrimination was assessed using receiver operating curve. Calibration was evaluated using the McFadden's R2. Net reclassification index (NRI) associated with incorporation of laboratory results was calculated. Results were internally validated. A model similar to existing CRC prediction models which included age, sex, height, obesity, ever smoking, alcohol dependence, and previous screening colonoscopy had an AUC of 0.58 (0.57-0.59) with poor goodness of fit. A laboratory-based model including hematocrit, MCV, lymphocytes, and neutrophil-lymphocyte ratio (NLR) had an AUC of 0.76 (0.76-0.77) and a McFadden's R2 of 0.21 with a NRI of 47.6 %. A combined model including sex, hemoglobin, MCV, white blood cells, platelets, NLR, and oral hypoglycemic use had an AUC of 0.80 (0.79-0.81) with a McFadden's R2 of 0.27 and a NRI of 60.7 %. Similar results were shown in an internal validation set. A laboratory-based risk model had good predictive power for sporadic CRC risk.

Exploring Human Diseases and Biological Mechanisms by Protein Structure Prediction and Modeling.

PubMed

Wang, Juexin; Luttrell, Joseph; Zhang, Ning; Khan, Saad; Shi, NianQing; Wang, Michael X; Kang, Jing-Qiong; Wang, Zheng; Xu, Dong

2016-01-01

Protein structure prediction and modeling provide a tool for understanding protein functions by computationally constructing protein structures from amino acid sequences and analyzing them. With help from protein prediction tools and web servers, users can obtain the three-dimensional protein structure models and gain knowledge of functions from the proteins. In this chapter, we will provide several examples of such studies. As an example, structure modeling methods were used to investigate the relation between mutation-caused misfolding of protein and human diseases including epilepsy and leukemia. Protein structure prediction and modeling were also applied in nucleotide-gated channels and their interaction interfaces to investigate their roles in brain and heart cells. In molecular mechanism studies of plants, rice salinity tolerance mechanism was studied via structure modeling on crucial proteins identified by systems biology analysis; trait-associated protein-protein interactions were modeled, which sheds some light on the roles of mutations in soybean oil/protein content. In the age of precision medicine, we believe protein structure prediction and modeling will play more and more important roles in investigating biomedical mechanism of diseases and drug design.