Strategies for Selecting Crosses Using Genomic Prediction in Two Wheat Breeding Programs.

PubMed

Lado, Bettina; Battenfield, Sarah; Guzmán, Carlos; Quincke, Martín; Singh, Ravi P; Dreisigacker, Susanne; Peña, R Javier; Fritz, Allan; Silva, Paula; Poland, Jesse; Gutiérrez, Lucía

2017-07-01

The single most important decision in plant breeding programs is the selection of appropriate crosses. The ideal cross would provide superior predicted progeny performance and enough diversity to maintain genetic gain. The aim of this study was to compare the best crosses predicted using combinations of mid-parent value and variance prediction accounting for linkage disequilibrium (V) or assuming linkage equilibrium (V). After predicting the mean and the variance of each cross, we selected crosses based on mid-parent value, the top 10% of the progeny, and weighted mean and variance within progenies for grain yield, grain protein content, mixing time, and loaf volume in two applied wheat ( L.) breeding programs: Instituto Nacional de Investigación Agropecuaria (INIA) Uruguay and CIMMYT Mexico. Although the variance of the progeny is important to increase the chances of finding superior individuals from transgressive segregation, we observed that the mid-parent values of the crosses drove the genetic gain but the variance of the progeny had a small impact on genetic gain for grain yield. However, the relative importance of the variance of the progeny was larger for quality traits. Overall, the genomic resources and the statistical models are now available to plant breeders to predict both the performance of breeding lines per se as well as the value of progeny from any potential crosses. Copyright © 2017 Crop Science Society of America.

Does Human Capital Matter? A Meta-Analysis of the Relationship between Human Capital and Firm Performance

ERIC Educational Resources Information Center

Crook, T. Russell; Todd, Samuel Y.; Combs, James G.; Woehr, David J.; Ketchen, David J., Jr.

2011-01-01

Theory at both the micro and macro level predicts that investments in superior human capital generate better firm-level performance. However, human capital takes time and money to develop or acquire, which potentially offsets its positive benefits. Indeed, extant tests appear equivocal regarding its impact. To clarify what is known, we…

[Asymmetric confusability effect in recognition memory of cats pictures].

PubMed

Ando, M; Hakoda, Y

1999-06-01

Performance superiority of the addition of features in the stimuli over the deletion on recognition (asymmetric confusability effect) has been shown in previous studies (Pezdek, Maki, Valencia-Laver, Whetstone, Stoeckert, & Dougherty, 1988; Ando & Hakoda, 1998). We investigated the same effect by using a familiar living thing (cat) as a stimulus. Ten subjects were given a recognition task using pictures of cats with feature changes (additions, deletions, or no change). Results showed that the picture with deletions were easier to recognize than those with additions, which was opposite to the previous studies. Then, we examined the possibility that performance superiority of the deletions over the additions was mediated by the factor of impression. Another group of 18 subjects was asked to rate the impression scales consisting of a "typicality-reality factor", a "stability-balance factor", and a "grotesque-disgust factor". Results showed that there was a significant difference in impression ratings for each factor between the additions and the deletions, and that impression ratings predicted recognition performance well. It was concluded that performance superiority of the deletions over the additions was mediated by the factor of impression.

The right hemisphere supports but does not replace left hemisphere auditory function in patients with persisting aphasia.

PubMed

Teki, Sundeep; Barnes, Gareth R; Penny, William D; Iverson, Paul; Woodhead, Zoe V J; Griffiths, Timothy D; Leff, Alexander P

2013-06-01

In this study, we used magnetoencephalography and a mismatch paradigm to investigate speech processing in stroke patients with auditory comprehension deficits and age-matched control subjects. We probed connectivity within and between the two temporal lobes in response to phonemic (different word) and acoustic (same word) oddballs using dynamic causal modelling. We found stronger modulation of self-connections as a function of phonemic differences for control subjects versus aphasics in left primary auditory cortex and bilateral superior temporal gyrus. The patients showed stronger modulation of connections from right primary auditory cortex to right superior temporal gyrus (feed-forward) and from left primary auditory cortex to right primary auditory cortex (interhemispheric). This differential connectivity can be explained on the basis of a predictive coding theory which suggests increased prediction error and decreased sensitivity to phonemic boundaries in the aphasics' speech network in both hemispheres. Within the aphasics, we also found behavioural correlates with connection strengths: a negative correlation between phonemic perception and an inter-hemispheric connection (left superior temporal gyrus to right superior temporal gyrus), and positive correlation between semantic performance and a feedback connection (right superior temporal gyrus to right primary auditory cortex). Our results suggest that aphasics with impaired speech comprehension have less veridical speech representations in both temporal lobes, and rely more on the right hemisphere auditory regions, particularly right superior temporal gyrus, for processing speech. Despite this presumed compensatory shift in network connectivity, the patients remain significantly impaired.

The right hemisphere supports but does not replace left hemisphere auditory function in patients with persisting aphasia

PubMed Central

Barnes, Gareth R.; Penny, William D.; Iverson, Paul; Woodhead, Zoe V. J.; Griffiths, Timothy D.; Leff, Alexander P.

2013-01-01

In this study, we used magnetoencephalography and a mismatch paradigm to investigate speech processing in stroke patients with auditory comprehension deficits and age-matched control subjects. We probed connectivity within and between the two temporal lobes in response to phonemic (different word) and acoustic (same word) oddballs using dynamic causal modelling. We found stronger modulation of self-connections as a function of phonemic differences for control subjects versus aphasics in left primary auditory cortex and bilateral superior temporal gyrus. The patients showed stronger modulation of connections from right primary auditory cortex to right superior temporal gyrus (feed-forward) and from left primary auditory cortex to right primary auditory cortex (interhemispheric). This differential connectivity can be explained on the basis of a predictive coding theory which suggests increased prediction error and decreased sensitivity to phonemic boundaries in the aphasics’ speech network in both hemispheres. Within the aphasics, we also found behavioural correlates with connection strengths: a negative correlation between phonemic perception and an inter-hemispheric connection (left superior temporal gyrus to right superior temporal gyrus), and positive correlation between semantic performance and a feedback connection (right superior temporal gyrus to right primary auditory cortex). Our results suggest that aphasics with impaired speech comprehension have less veridical speech representations in both temporal lobes, and rely more on the right hemisphere auditory regions, particularly right superior temporal gyrus, for processing speech. Despite this presumed compensatory shift in network connectivity, the patients remain significantly impaired. PMID:23715097

Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling.

PubMed

Størset, Elisabet; Holford, Nick; Hennig, Stefanie; Bergmann, Troels K; Bergan, Stein; Bremer, Sara; Åsberg, Anders; Midtvedt, Karsten; Staatz, Christine E

2014-09-01

The aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models. Data were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting. Pharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h−1 [95% CI 12.6, 18.0 l h−1]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An Emax model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range. A theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.

Comparison of Adjacency and Distance-Based Approaches for Spatial Analysis of Multimodal Traffic Crash Data

NASA Astrophysics Data System (ADS)

Gill, G.; Sakrani, T.; Cheng, W.; Zhou, J.

2017-09-01

Many studies have utilized the spatial correlations among traffic crash data to develop crash prediction models with the aim to investigate the influential factors or predict crash counts at different sites. The spatial correlation have been observed to account for heterogeneity in different forms of weight matrices which improves the estimation performance of models. But very rarely have the weight matrices been compared for the prediction accuracy for estimation of crash counts. This study was targeted at the comparison of two different approaches for modelling the spatial correlations among crash data at macro-level (County). Multivariate Full Bayesian crash prediction models were developed using Decay-50 (distance-based) and Queen-1 (adjacency-based) weight matrices for simultaneous estimation crash counts of four different modes: vehicle, motorcycle, bike, and pedestrian. The goodness-of-fit and different criteria for accuracy at prediction of crash count reveled the superiority of Decay-50 over Queen-1. Decay-50 was essentially different from Queen-1 with the selection of neighbors and more robust spatial weight structure which rendered the flexibility to accommodate the spatially correlated crash data. The consistently better performance of Decay-50 at prediction accuracy further bolstered its superiority. Although the data collection efforts to gather centroid distance among counties for Decay-50 may appear to be a downside, but the model has a significant edge to fit the crash data without losing the simplicity of computation of estimated crash count.

Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types

PubMed Central

Austin, Peter C.; Tu, Jack V.; Ho, Jennifer E.; Levy, Daniel; Lee, Douglas S.

2014-01-01

Objective Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. Study design and Setting We compared the performance of these classification methods with those of conventional classification trees to classify patients with heart failure according to the following sub-types: heart failure with preserved ejection fraction (HFPEF) vs. heart failure with reduced ejection fraction (HFREF). We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. Results We found that modern, flexible tree-based methods from the data mining literature offer substantial improvement in prediction and classification of heart failure sub-type compared to conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared to the methods proposed in the data mining literature. Conclusion The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying heart failure subtypes in a population-based sample of patients from Ontario. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF. PMID:23384592

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

A face only an investor could love: CEOs' facial structure predicts their firms' financial performance.

PubMed

Wong, Elaine M; Ormiston, Margaret E; Haselhuhn, Michael P

2011-12-01

Researchers have theorized that innate personal traits are related to leadership success. Although links between psychological characteristics and leadership success have been well established, research has yet to identify any objective physical traits of leaders that predict organizational performance. In the research reported here, we identified leaders' facial structure as a specific physical trait that correlates with organizational performance. Specifically, we found that firms whose male CEOs have wider faces (relative to facial height) achieve superior financial performance. Decision-making dynamics within a firm's leadership team moderate this effect, such that the relationship between a given CEO's facial measurements and his firm's financial performance is stronger in firms with cognitively simple leadership teams.

Sensorimotor abilities predict on-field performance in professional baseball.

PubMed

Burris, Kyle; Vittetoe, Kelly; Ramger, Benjamin; Suresh, Sunith; Tokdar, Surya T; Reiter, Jerome P; Appelbaum, L Gregory

2018-01-08

Baseball players must be able to see and react in an instant, yet it is hotly debated whether superior performance is associated with superior sensorimotor abilities. In this study, we compare sensorimotor abilities, measured through 8 psychomotor tasks comprising the Nike Sensory Station assessment battery, and game statistics in a sample of 252 professional baseball players to evaluate the links between sensorimotor skills and on-field performance. For this purpose, we develop a series of Bayesian hierarchical latent variable models enabling us to compare statistics across professional baseball leagues. Within this framework, we find that sensorimotor abilities are significant predictors of on-base percentage, walk rate and strikeout rate, accounting for age, position, and league. We find no such relationship for either slugging percentage or fielder-independent pitching. The pattern of results suggests performance contributions from both visual-sensory and visual-motor abilities and indicates that sensorimotor screenings may be useful for player scouting.

Neural network-based run-to-run controller using exposure and resist thickness adjustment

NASA Astrophysics Data System (ADS)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

Mnemonic training reshapes brain networks to support superior memory

PubMed Central

Dresler, Martin; Shirer, William R.; Konrad, Boris N.; Müller, Nils C.J.; Wagner, Isabella C.; Fernández, Guillén; Czisch, Michael; Greicius, Michael D.

2017-01-01

Summary Memory skills strongly differ across the general population, however little is known about the brain characteristics supporting superior memory performance. Here, we assess functional brain network organization of 23 of the world’s most successful memory athletes and matched controls by fMRI during both task-free resting state baseline and active memory encoding. We demonstrate that in a group of naïve controls, functional connectivity changes induced by six weeks of mnemonic training were correlated with the network organization that distinguishes athletes from controls. During rest, this effect was mainly driven by connections between rather than within the visual, medial temporal lobe and default mode networks, whereas during task it was driven by connectivity within these networks. Similarity with memory athlete connectivity patterns predicted memory improvements up to 4 months after training. In conclusion, mnemonic training drives distributed rather than regional changes, reorganizing the brain’s functional network organization to enable superior memory performance. PMID:28279356

A mechano-acoustic model of the effect of superior canal dehiscence on hearing in chinchilla

PubMed Central

Songer, Jocelyn E.; Rosowski, John J.

2008-01-01

Superior canal dehiscence (SCD) is a pathological condition of the ear that can cause a conductive hearing loss. The effect of SCD (a hole in the bony wall of the superior semicircular canal) on chinchilla middle- and inner-ear mechanics is analyzed with a circuit model of the dehiscence. The model is used to predict the effect of dehiscence on auditory sensitivity and mechanics. These predictions are compared to previously published measurements of dehiscence related changes in chinchilla cochlear potential, middle-ear input admittance and stapes velocity. The comparisons show that the model predictions are both qualitatively and quantitatively similar to the physiological results for frequencies where physiologic data are available. The similarity supports the third-window hypothesis of the effect of superior canal dehiscence on auditory sensitivity and mechanics and provides the groundwork for the development of a model that predicts the effect of superior canal dehiscence syndrome on auditory sensitivity and mechanics in humans. PMID:17672643

Predicting outcome of status epilepticus.

PubMed

Leitinger, M; Kalss, G; Rohracher, A; Pilz, G; Novak, H; Höfler, J; Deak, I; Kuchukhidze, G; Dobesberger, J; Wakonig, A; Trinka, E

2015-08-01

Status epilepticus (SE) is a frequent neurological emergency complicated by high mortality and often poor functional outcome in survivors. The aim of this study was to review available clinical scores to predict outcome. Literature review. PubMed Search terms were "score", "outcome", and "status epilepticus" (April 9th 2015). Publications with abstracts available in English, no other language restrictions, or any restrictions concerning investigated patients were included. Two scores were identified: "Status Epilepticus Severity Score--STESS" and "Epidemiology based Mortality score in SE--EMSE". A comprehensive comparison of test parameters concerning performance, options, and limitations was performed. Epidemiology based Mortality score in SE allows detailed individualization of risk factors and is significantly superior to STESS in a retrospective explorative study. In particular, EMSE is very good at detection of good and bad outcome, whereas STESS detecting bad outcome is limited by a ceiling effect and uncertainty of correct cutoff value. Epidemiology based Mortality score in SE can be adapted to different regions in the world and to advances in medicine, as new data emerge. In addition, we designed a reporting standard for status epilepticus to enhance acquisition and communication of outcome relevant data. A data acquisition sheet used from patient admission in emergency room, from the EEG lab to intensive care unit, is provided for optimized data collection. Status Epilepticus Severity Score is easy to perform and predicts bad outcome, but has a low predictive value for good outcomes. Epidemiology based Mortality score in SE is superior to STESS in predicting good or bad outcome but needs marginally more time to perform. Epidemiology based Mortality score in SE may prove very useful for risk stratification in interventional studies and is recommended for individual outcome prediction. Prospective validation in different cohorts is needed for EMSE, whereas STESS needs further validation in cohorts with a wider range of etiologies. This article is part of a Special Issue entitled "Status Epilepticus". Copyright © 2015. Published by Elsevier Inc.

A Model of the Superior Colliculus Predicts Fixation Locations during Scene Viewing and Visual Search.

PubMed

Adeli, Hossein; Vitu, Françoise; Zelinsky, Gregory J

2017-02-08

Modern computational models of attention predict fixations using saliency maps and target maps, which prioritize locations for fixation based on feature contrast and target goals, respectively. But whereas many such models are biologically plausible, none have looked to the oculomotor system for design constraints or parameter specification. Conversely, although most models of saccade programming are tightly coupled to underlying neurophysiology, none have been tested using real-world stimuli and tasks. We combined the strengths of these two approaches in MASC, a model of attention in the superior colliculus (SC) that captures known neurophysiological constraints on saccade programming. We show that MASC predicted the fixation locations of humans freely viewing naturalistic scenes and performing exemplar and categorical search tasks, a breadth achieved by no other existing model. Moreover, it did this as well or better than its more specialized state-of-the-art competitors. MASC's predictive success stems from its inclusion of high-level but core principles of SC organization: an over-representation of foveal information, size-invariant population codes, cascaded population averaging over distorted visual and motor maps, and competition between motor point images for saccade programming, all of which cause further modulation of priority (attention) after projection of saliency and target maps to the SC. Only by incorporating these organizing brain principles into our models can we fully understand the transformation of complex visual information into the saccade programs underlying movements of overt attention. With MASC, a theoretical footing now exists to generate and test computationally explicit predictions of behavioral and neural responses in visually complex real-world contexts. SIGNIFICANCE STATEMENT The superior colliculus (SC) performs a visual-to-motor transformation vital to overt attention, but existing SC models cannot predict saccades to visually complex real-world stimuli. We introduce a brain-inspired SC model that outperforms state-of-the-art image-based competitors in predicting the sequences of fixations made by humans performing a range of everyday tasks (scene viewing and exemplar and categorical search), making clear the value of looking to the brain for model design. This work is significant in that it will drive new research by making computationally explicit predictions of SC neural population activity in response to naturalistic stimuli and tasks. It will also serve as a blueprint for the construction of other brain-inspired models, helping to usher in the next generation of truly intelligent autonomous systems. Copyright © 2017 the authors 0270-6474/17/371453-15$15.00/0.

The role of anthropometric, performance and psychological attributes in predicting selection into an elite development programme in older adolescent rugby league players.

PubMed

Tredrea, Matthew; Dascombe, Ben; Sanctuary, Colin E; Scanlan, Aaron Terrence

2017-10-01

This study aimed to identify attributes that discriminate selected from non-selected players and predict selection into a rugby league development programme in older adolescent players. Anthropometric, performance and psychological attributes were measured in under-16 (N = 100) and under-18 (N = 60) rugby league players trialling for selection into a development programme with a professional Australian club. Sprint times (P < 0.001), predicted VO 2max (P = 0.002) and push-ups 1   min (P = 0.004) were superior in selected under-16 players, and sprint times (P ≤ 0.045), push-ups 1   min (P < 0.001) and chin-ups 1   min (P = 0.013) were superior in selected under-18 players. Further, 10-m sprint (β = -7.706, standard error [SE] = 2.412), VO 2max (β = 0.168, SE = 0.052) and body mass (β = 0.071, SE = 0.023) significantly predicted selection (R 2  = 0.339) in under-16 players, while push-ups 1   min (β = 0.564, SE = 0.250), 10-m sprint (β = -68.477, SE = 28.107), body mass (β = 0.360, SE = 0.155) and chronological age (β = -3.577, SE = 1.720) significantly predicted selection (R 2  = 0.894) in under-18 players. These findings emphasise the importance of performance attributes in junior rugby league and indicate talent identification test batteries should be age-specific in older adolescent players.

Integrated species distribution models: combining presence-background data and site-occupancy data with imperfect detection

USGS Publications Warehouse

Koshkina, Vira; Wang, Yang; Gordon, Ascelin; Dorazio, Robert; White, Matthew; Stone, Lewi

2017-01-01

Two main sources of data for species distribution models (SDMs) are site-occupancy (SO) data from planned surveys, and presence-background (PB) data from opportunistic surveys and other sources. SO surveys give high quality data about presences and absences of the species in a particular area. However, due to their high cost, they often cover a smaller area relative to PB data, and are usually not representative of the geographic range of a species. In contrast, PB data is plentiful, covers a larger area, but is less reliable due to the lack of information on species absences, and is usually characterised by biased sampling. Here we present a new approach for species distribution modelling that integrates these two data types.We have used an inhomogeneous Poisson point process as the basis for constructing an integrated SDM that fits both PB and SO data simultaneously. It is the first implementation of an Integrated SO–PB Model which uses repeated survey occupancy data and also incorporates detection probability.The Integrated Model's performance was evaluated, using simulated data and compared to approaches using PB or SO data alone. It was found to be superior, improving the predictions of species spatial distributions, even when SO data is sparse and collected in a limited area. The Integrated Model was also found effective when environmental covariates were significantly correlated. Our method was demonstrated with real SO and PB data for the Yellow-bellied glider (Petaurus australis) in south-eastern Australia, with the predictive performance of the Integrated Model again found to be superior.PB models are known to produce biased estimates of species occupancy or abundance. The small sample size of SO datasets often results in poor out-of-sample predictions. Integrated models combine data from these two sources, providing superior predictions of species abundance compared to using either data source alone. Unlike conventional SDMs which have restrictive scale-dependence in their predictions, our Integrated Model is based on a point process model and has no such scale-dependency. It may be used for predictions of abundance at any spatial-scale while still maintaining the underlying relationship between abundance and area.

Cryogenic liquid-level detector

NASA Technical Reports Server (NTRS)

Hamlet, J.

1978-01-01

Detector is designed for quick assembly, fast response, and good performance under vibratory stress. Its basic parallel-plate open configuration can be adapted to any length and allows its calibration scale factor to be predicted accurately. When compared with discrete level sensors, continuous reading sensor was found to be superior if there is sloshing, boiling, or other disturbance.

Right anterior superior temporal activation predicts auditory sentence comprehension following aphasic stroke.

PubMed

Crinion, Jenny; Price, Cathy J

2005-12-01

Previous studies have suggested that recovery of speech comprehension after left hemisphere infarction may depend on a mechanism in the right hemisphere. However, the role that distinct right hemisphere regions play in speech comprehension following left hemisphere stroke has not been established. Here, we used functional magnetic resonance imaging (fMRI) to investigate narrative speech activation in 18 neurologically normal subjects and 17 patients with left hemisphere stroke and a history of aphasia. Activation for listening to meaningful stories relative to meaningless reversed speech was identified in the normal subjects and in each patient. Second level analyses were then used to investigate how story activation changed with the patients' auditory sentence comprehension skills and surprise story recognition memory tests post-scanning. Irrespective of lesion site, performance on tests of auditory sentence comprehension was positively correlated with activation in the right lateral superior temporal region, anterior to primary auditory cortex. In addition, when the stroke spared the left temporal cortex, good performance on tests of auditory sentence comprehension was also correlated with the left posterior superior temporal cortex (Wernicke's area). In distinct contrast to this, good story recognition memory predicted left inferior frontal and right cerebellar activation. The implication of this double dissociation in the effects of auditory sentence comprehension and story recognition memory is that left frontal and left temporal activations are dissociable. Our findings strongly support the role of the right temporal lobe in processing narrative speech and, in particular, auditory sentence comprehension following left hemisphere aphasic stroke. In addition, they highlight the importance of the right anterior superior temporal cortex where the response was dissociated from that in the left posterior temporal lobe.

The Motive for Support and the Identification of Responsive Partners

PubMed Central

Turan, Bulent; Horowitz, Leonard M.

2010-01-01

To obtain support from others, a person must first identify responsive partners. One strategy for doing so is to use indicators of responsive partners. We argue that a person with a strong motive for support should rate all indicators highly useful—the “Elevated Motives Effect.” Study 1 confirmed this hypothesis by correlating participants’ total ratings with existing measures of motive-strength. Study 2 applied the Elevated Motives Effect to demonstrate that motive-strength (in interaction with knowledge of indicators) predicts performance on a laboratory task in which participants evaluated a person: Superior knowledge led to superior performance only when motive-strength was high. Study 3, an experience-sampling study, showed that in everyday life, motivated people more often seek support from others when distressed. PMID:20544011

Empirically Optimized Flow Cytometric Immunoassay Validates Ambient Analyte Theory

PubMed Central

Parpia, Zaheer A.; Kelso, David M.

2010-01-01

Ekins’ ambient analyte theory predicts, counter intuitively, that an immunoassay’s limit of detection can be improved by reducing the amount of capture antibody. In addition, it also anticipates that results should be insensitive to the volume of sample as well as the amount of capture antibody added. The objective of this study is to empirically validate all of the performance characteristics predicted by Ekins’ theory. Flow cytometric analysis was used to detect binding between a fluorescent ligand and capture microparticles since it can directly measure fractional occupancy, the primary response variable in ambient analyte theory. After experimentally determining ambient analyte conditions, comparisons were carried out between ambient and non-ambient assays in terms of their signal strengths, limits of detection, and their sensitivity to variations in reaction volume and number of particles. The critical number of binding sites required for an assay to be in the ambient analyte region was estimated to be 0.1VKd. As predicted, such assays exhibited superior signal/noise levels and limits of detection; and were not affected by variations in sample volume and number of binding sites. When the signal detected measures fractional occupancy, ambient analyte theory is an excellent guide to developing assays with superior performance characteristics. PMID:20152793

Deep learning architecture for air quality predictions.

PubMed

Li, Xiang; Peng, Ling; Hu, Yuan; Shao, Jing; Chi, Tianhe

2016-11-01

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

Striatal volume predicts level of video game skill acquisition.

PubMed

Erickson, Kirk I; Boot, Walter R; Basak, Chandramallika; Neider, Mark B; Prakash, Ruchika S; Voss, Michelle W; Graybiel, Ann M; Simons, Daniel J; Fabiani, Monica; Gratton, Gabriele; Kramer, Arthur F

2010-11-01

Video game skills transfer to other tasks, but individual differences in performance and in learning and transfer rates make it difficult to identify the source of transfer benefits. We asked whether variability in initial acquisition and of improvement in performance on a demanding video game, the Space Fortress game, could be predicted by variations in the pretraining volume of either of 2 key brain regions implicated in learning and memory: the striatum, implicated in procedural learning and cognitive flexibility, and the hippocampus, implicated in declarative memory. We found that hippocampal volumes did not predict learning improvement but that striatal volumes did. Moreover, for the striatum, the volumes of the dorsal striatum predicted improvement in performance but the volumes of the ventral striatum did not. Both ventral and dorsal striatal volumes predicted early acquisition rates. Furthermore, this early-stage correlation between striatal volumes and learning held regardless of the cognitive flexibility demands of the game versions, whereas the predictive power of the dorsal striatal volumes held selectively for performance improvements in a game version emphasizing cognitive flexibility. These findings suggest a neuroanatomical basis for the superiority of training strategies that promote cognitive flexibility and transfer to untrained tasks.

Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions.

PubMed

Cheng, Wen; Gill, Gurdiljot Singh; Zhang, Yongping; Cao, Zhong

2018-03-01

The traffic safety research has developed spatiotemporal models to explore the variations in the spatial pattern of crash risk over time. Many studies observed notable benefits associated with the inclusion of spatial and temporal correlation and their interactions. However, the safety literature lacks sufficient research for the comparison of different temporal treatments and their interaction with spatial component. This study developed four spatiotemporal models with varying complexity due to the different temporal treatments such as (I) linear time trend; (II) quadratic time trend; (III) Autoregressive-1 (AR-1); and (IV) time adjacency. Moreover, the study introduced a flexible two-component mixture for the space-time interaction which allows greater flexibility compared to the traditional linear space-time interaction. The mixture component allows the accommodation of global space-time interaction as well as the departures from the overall spatial and temporal risk patterns. This study performed a comprehensive assessment of mixture models based on the diverse criteria pertaining to goodness-of-fit, cross-validation and evaluation based on in-sample data for predictive accuracy of crash estimates. The assessment of model performance in terms of goodness-of-fit clearly established the superiority of the time-adjacency specification which was evidently more complex due to the addition of information borrowed from neighboring years, but this addition of parameters allowed significant advantage at posterior deviance which subsequently benefited overall fit to crash data. The Base models were also developed to study the comparison between the proposed mixture and traditional space-time components for each temporal model. The mixture models consistently outperformed the corresponding Base models due to the advantages of much lower deviance. For cross-validation comparison of predictive accuracy, linear time trend model was adjudged the best as it recorded the highest value of log pseudo marginal likelihood (LPML). Four other evaluation criteria were considered for typical validation using the same data for model development. Under each criterion, observed crash counts were compared with three types of data containing Bayesian estimated, normal predicted, and model replicated ones. The linear model again performed the best in most scenarios except one case of using model replicated data and two cases involving prediction without including random effects. These phenomena indicated the mediocre performance of linear trend when random effects were excluded for evaluation. This might be due to the flexible mixture space-time interaction which can efficiently absorb the residual variability escaping from the predictable part of the model. The comparison of Base and mixture models in terms of prediction accuracy further bolstered the superiority of the mixture models as the mixture ones generated more precise estimated crash counts across all four models, suggesting that the advantages associated with mixture component at model fit were transferable to prediction accuracy. Finally, the residual analysis demonstrated the consistently superior performance of random effect models which validates the importance of incorporating the correlation structures to account for unobserved heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Edge chipping and flexural resistance of monolithic ceramics☆

PubMed Central

Zhang, Yu; Lee, James J.-W.; Srikanth, Ramanathan; Lawn, Brian R.

2014-01-01

Objective Test the hypothesis that monolithic ceramics can be developed with combined esthetics and superior fracture resistance to circumvent processing and performance drawbacks of traditional all-ceramic crowns and fixed-dental-prostheses consisting of a hard and strong core with an esthetic porcelain veneer. Specifically, to demonstrate that monolithic prostheses can be produced with a much reduced susceptibility to fracture. Methods Protocols were applied for quantifying resistance to chipping as well as resistance to flexural failure in two classes of dental ceramic, microstructurally-modified zirconias and lithium disilicate glass–ceramics. A sharp indenter was used to induce chips near the edges of flat-layer specimens, and the results compared with predictions from a critical load equation. The critical loads required to produce cementation surface failure in monolithic specimens bonded to dentin were computed from established flexural strength relations and the predictions validated with experimental data. Results Monolithic zirconias have superior chipping and flexural fracture resistance relative to their veneered counterparts. While they have superior esthetics, glass–ceramics exhibit lower strength but higher chip fracture resistance relative to porcelain-veneered zirconias. Significance The study suggests a promising future for new and improved monolithic ceramic restorations, with combined durability and acceptable esthetics. PMID:24139756

Testing the performance of technical trading rules in the Chinese markets based on superior predictive test

NASA Astrophysics Data System (ADS)

Wang, Shan; Jiang, Zhi-Qiang; Li, Sai-Ping; Zhou, Wei-Xing

2015-12-01

Technical trading rules have a long history of being used by practitioners in financial markets. The profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousand traditional technical trading rules on the Shanghai Securities Composite Index (SSCI) from May 21, 1992 through June 30, 2013 and China Securities Index 300 (CSI 300) from April 8, 2005 through June 30, 2013 to check whether an effective trading strategy could be found by using the performance measurements based on the return and Sharpe ratio. To correct for the influence of the data-snooping effect, we adopt the Superior Predictive Ability test to evaluate if there exists a trading rule that can significantly outperform the benchmark. The result shows that for SSCI, technical trading rules offer significant profitability, while for CSI 300, this ability is lost. We further partition the SSCI into two sub-series and find that the efficiency of technical trading in sub-series, which have exactly the same spanning period as that of CSI 300, is severely weakened. By testing the trading rules on both indexes with a five-year moving window, we find that during the financial bubble from 2005 to 2007, the effectiveness of technical trading rules is greatly improved. This is consistent with the predictive ability of technical trading rules which appears when the market is less efficient.

Differential processing of melodic, rhythmic and simple tone deviations in musicians--an MEG study.

PubMed

Lappe, Claudia; Lappe, Markus; Pantev, Christo

2016-01-01

Rhythm and melody are two basic characteristics of music. Performing musicians have to pay attention to both, and avoid errors in either aspect of their performance. To investigate the neural processes involved in detecting melodic and rhythmic errors from auditory input we tested musicians on both kinds of deviations in a mismatch negativity (MMN) design. We found that MMN responses to a rhythmic deviation occurred at shorter latencies than MMN responses to a melodic deviation. Beamformer source analysis showed that the melodic deviation activated superior temporal, inferior frontal and superior frontal areas whereas the activation pattern of the rhythmic deviation focused more strongly on inferior and superior parietal areas, in addition to superior temporal cortex. Activation in the supplementary motor area occurred for both types of deviations. We also recorded responses to similar pitch and tempo deviations in a simple, non-musical repetitive tone pattern. In this case, there was no latency difference between the MMNs and cortical activation was smaller and mostly limited to auditory cortex. The results suggest that prediction and error detection of musical stimuli in trained musicians involve a broad cortical network and that rhythmic and melodic errors are processed in partially different cortical streams. Copyright © 2015 Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2013-10-01

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

Neural Prediction of Multidimensional Decisions in Monkey Superior Colliculus

NASA Astrophysics Data System (ADS)

Hasegawa, Ryohei P.; Hasegawa, Yukako T.; Segraves, Mark A.

To examine the function of the superior colliculus (SC) in decision-making processes and the application of its single trial activity for “neural mind reading,” we recorded from SC deep layers while two monkeys performed oculomotor go/no-go tasks. We have recently focused on monitoring single trial activities in single SC neurons, and designed a virtual decision function (VDF) to provide a good estimation of single-dimensional decisions (go/no-go decisions for a cue presented at a specific visual field, a response field of each neuron). In this study, we used two VDFs for multidimensional decisions (go/no-go decisions at two cue locations) with the ensemble activity which was simultaneously recorded from a small group (4 to 6) of neurons at both sides of the SC. VDFs predicted cue locations as well as go/no-go decisions. These results suggest that monitoring of ensemble SC activity had sufficient capacity to predict multidimensional decisions on a trial-by-trial basis, which is an ideal candidate to serve for cognitive brain-machine interfaces (BMI) such as two-dimensional word spellers.

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

PubMed

Cannon, Edward O; Amini, Ata; Bender, Andreas; Sternberg, Michael J E; Muggleton, Stephen H; Glen, Robert C; Mitchell, John B O

2007-05-01

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

Superior Diagnostic Performance of Malaria Rapid Diagnostic Tests as compared to Blood Smears in U.S. Clinical Practice

PubMed Central

Stauffer, William M.; Cartwright, Charles P.; Olson, Douglas; Juni, Billie Anne; Taylor, Charlotte M; Bowers, Susan H.; Hanson, Kevan L.; Rosenblatt, Jon E.; Boulware, David R.

2010-01-01

Background Approximately 4 million U.S. travelers to developing countries are ill enough to seek healthcare with 1,500 malaria cases reported in the U.S. annually. The diagnosis of malaria is frequently delayed due to the time to prepare malaria blood films and lack of technical expertise. An easy, reliable rapid diagnostic test (RDT) with high sensitivity and negative predictive value (NPV), particularly for Plasmodium falciparum, would be clinically useful. The study objective was to determine the diagnostic performance of the FDA-approved NOW® Malaria Test in comparison to traditional thick and thin blood smears for malaria diagnosis. Methods This prospective study tested 852 consecutive blood samples sent for thick and thin smears with blinded, malaria rapid tests at three hospital laboratories during 2003–2006. Polymerase chain reaction (PCR) verified positive tests and discordant results. Results Malaria occurred in 11% (95/852). The rapid test had superior performance than the standard Giemsa thick blood smear (P=.003). The rapid test’s sensitivity for all malaria was 97% (92/95) vs. 85% (81/95) by blood smear, and the RDT had superior NPV of 99.6% vs. 98.2% (P=.001). The P. falciparum performance was excellent with 100% rapid test sensitivity versus only 88% (65/74) by blood smear (P=.003). Conclusions This operational study demonstrates the FDA-approved rapid malaria test is superior to a single set of blood smears performed under routine U.S. clinical laboratory conditions. The most valuable clinical role of the RDT is in the rapid diagnosis or the exclusion of P. falciparum malaria, which is particularly useful in outpatient settings when evaluating febrile travelers. PMID:19686072

Discerning measures of conscious brain processes associated with superior early motor performance: Capacity, coactivation, and character.

PubMed

van Duijn, Tina; Buszard, Tim; Hoskens, Merel C J; Masters, Rich S W

2017-01-01

This study explored the relationship between working memory (WM) capacity, corticocortical communication (EEG coherence), and propensity for conscious control of movement during the performance of a complex far-aiming task. We were specifically interested in the role of these variables in predicting motor performance by novices. Forty-eight participants completed (a) an assessment of WM capacity (an adapted Rotation Span task), (b) a questionnaire that assessed the propensity to consciously control movement (the Movement Specific Reinvestment Scale), and (c) a hockey push-pass task. The hockey push-pass task was performed in a single task (movement only) condition and a combined task (movement plus decision) condition. Electroencephalography (EEG) was used to examine brain activity during the single task. WM capacity best predicted single task performance. WM capacity in combination with T8-Fz coherence (between the visuospatial and motor regions of the brain) best predicted combined task performance. We discuss the implied roles of visuospatial information processing capacity, neural coactivation, and propensity for conscious processing during performance of complex motor tasks. © 2017 Elsevier B.V. All rights reserved.

Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

NASA Astrophysics Data System (ADS)

Mey, Antonia S. J. S.; Jiménez, Jordi Juárez; Michel, Julien

2018-01-01

The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.

Correlation of clinical predictions and surgical results in maxillary superior repositioning.

PubMed

Tabrizi, Reza; Zamiri, Barbad; Kazemi, Hamidreza

2014-05-01

This is a prospective study to evaluate the accuracy of clinical predictions related to surgical results in subjects who underwent maxillary superior repositioning without anterior-posterior movement. Surgeons' predictions according to clinical (tooth show at rest and at the maximum smile) and cephalometric evaluation were documented for the amount of maxillary superior repositioning. Overcorrection or undercorrection was documented for every subject 1 year after the operations. Receiver operating characteristic curve test was used to find a cutoff point in prediction errors and to determine positive predictive value (PPV) and negative predictive value. Forty subjects (14 males and 26 females) were studied. Results showed a significant difference between changes in the tooth show at rest and at the maximum smile line before and after surgery. Analysis of the data demonstrated no correlation between the predictive data and the surgical results. The incidence of undercorrection (25%) was more common than overcorrection (7.5%). The cutoff point for errors in predictions was 5 mm for tooth show at rest and 15 mm at the maximum smile. When the amount of the presurgical tooth show at rest was more than 5 mm, 50.5% of clinical predictions did not match the clinical results (PPV), and 75% of clinical predictions showed the same results when the tooth show was less than 5 mm (negative predictive value). When the amount of presurgical tooth shown in the maximum smile line was more than 15 mm, 75% of clinical predictions did not match with clinical results (PPV), and 25% of the predictions had the same results because the tooth show at the maximum smile was lower than 15 mm. Clinical predictions according to the tooth show at rest and at the maximum smile have a poor correlation with clinical results in maxillary superior repositioning for vertical maxillary excess. The risk of errors in predictions increased when the amount of superior repositioning of the maxilla increased. Generally, surgeons have a tendency to undercorrect rather than overcorrect, although clinical prediction is an original guideline for surgeons, and it may be associated with variable clinical results.

Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.

PubMed

Crichton, Gamal; Guo, Yufan; Pyysalo, Sampo; Korhonen, Anna

2018-05-21

Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neural networks to create node representations which allow rich inputs to neural classifiers. Preliminary works were done on this and report promising results. However they did not use realistic settings like time-slicing, evaluate performances with comprehensive metrics or explain when or why neural network methods outperform. We investigated how inputs from four node representation algorithms affect performance of a neural link predictor on random- and time-sliced biomedical graphs of real-world sizes (∼ 6 million edges) containing information relevant to DTI, PPI and LBD. We compared the performance of the neural link predictor to those of established baselines and report performance across five metrics. In random- and time-sliced experiments when the neural network methods were able to learn good node representations and there was a negligible amount of disconnected nodes, those approaches outperformed the baselines. In the smallest graph (∼ 15,000 edges) and in larger graphs with approximately 14% disconnected nodes, baselines such as Common Neighbours proved a justifiable choice for link prediction. At low recall levels (∼ 0.3) the approaches were mostly equal, but at higher recall levels across all nodes and average performance at individual nodes, neural network approaches were superior. Analysis showed that neural network methods performed well on links between nodes with no previous common neighbours; potentially the most interesting links. Additionally, while neural network methods benefit from large amounts of data, they require considerable amounts of computational resources to utilise them. Our results indicate that when there is enough data for the neural network methods to use and there are a negligible amount of disconnected nodes, those approaches outperform the baselines. At low recall levels the approaches are mostly equal but at higher recall levels and average performance at individual nodes, neural network approaches are superior. Performance at nodes without common neighbours which indicate more unexpected and perhaps more useful links account for this.

Hemispheric processing asymmetries: implications for memory.

PubMed

Funnell, M G; Corballis, P M; Gazzaniga, M S

2001-01-01

Recent research has demonstrated that memory for words elicits left hemisphere activation, faces right hemisphere activation, and nameable objects bilateral activation. This pattern of results was attributed to dual coding of information, with the left hemisphere employing a verbal code and the right a nonverbal code. Nameable objects can be encoded either verbally or nonverbally and this accounts for their bilateral activation. We investigated this hypothesis in a callosotomy patient. Consistent with dual coding, the left hemisphere was superior to the right in memory for words, whereas the right was superior for faces. Contrary to prediction, performance on nameable pictures was not equivalent in the two hemispheres, but rather resulted in a right hemisphere superiority. In addition, memory for pictures was significantly better than for either words or faces. These findings suggest that the dual code hypothesis is an oversimplification of the processing capabilities of the two hemispheres.

Ensemble gene function prediction database reveals genes important for complex I formation in Arabidopsis thaliana.

PubMed

Hansen, Bjoern Oest; Meyer, Etienne H; Ferrari, Camilla; Vaid, Neha; Movahedi, Sara; Vandepoele, Klaas; Nikoloski, Zoran; Mutwil, Marek

2018-03-01

Recent advances in gene function prediction rely on ensemble approaches that integrate results from multiple inference methods to produce superior predictions. Yet, these developments remain largely unexplored in plants. We have explored and compared two methods to integrate 10 gene co-function networks for Arabidopsis thaliana and demonstrate how the integration of these networks produces more accurate gene function predictions for a larger fraction of genes with unknown function. These predictions were used to identify genes involved in mitochondrial complex I formation, and for five of them, we confirmed the predictions experimentally. The ensemble predictions are provided as a user-friendly online database, EnsembleNet. The methods presented here demonstrate that ensemble gene function prediction is a powerful method to boost prediction performance, whereas the EnsembleNet database provides a cutting-edge community tool to guide experimentalists. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

Fuzzy regression modeling for tool performance prediction and degradation detection.

PubMed

Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L

2010-10-01

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

The tendency for social submission predicts superior cognitive performance in previously isolated male mice

PubMed Central

Matzel, Louis D.; Kolata, Stefan; Light, Kenneth; Sauce, Bruno

2016-01-01

The imposition of subordination may negatively impact cognitive performance in common social settings (e.g., the classroom), and likewise, laboratory studies of animals indicate that the stress associated with social defeat can impair cognitive performance. It is less clear whether an animal’s predisposition for social subordination (i.e., a tendency that is expressed prior to experience with social defeat) is related to its cognitive abilities (e.g., “general” intelligence). Using genetically diverse CD-1 male mice, here we determined that in the absence of adult experience with social hierarchies or social defeat, the predisposition for social subordination was associated with superior general cognitive ability (aggregate performance across a battery of five learning tasks). The tendency for social subordination was not dependent on the mice’ body weight, but both general cognitive ability and the tendency for social subordination were directly related to high stress reactivity (i.e., free corticosterone elevations induced by mild stress). This pattern of results suggests that submissive behavior and sensitivity to stress may be associated with superior cognitive potential, and this can reflect a native predisposition that precedes exposure to social pressures. More broadly, these results raise the possibility that socially subordinate animals evolved compensatory strategies to facilitate their survival, and that absent the imposition of subordination, normally submissive individuals may be better prepared for cognitive/academic achievement. PMID:27457190

Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm.

PubMed

Al Ajmi, Eiman; Forghani, Behzad; Reinhold, Caroline; Bayat, Maryam; Forghani, Reza

2018-06-01

There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm. Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set. Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis. Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours. • We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

A grey NGM(1,1, k) self-memory coupling prediction model for energy consumption prediction.

PubMed

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

Early but not late-blindness leads to enhanced auditory perception.

PubMed

Wan, Catherine Y; Wood, Amanda G; Reutens, David C; Wilson, Sarah J

2010-01-01

The notion that blindness leads to superior non-visual abilities has been postulated for centuries. Compared to sighted individuals, blind individuals show different patterns of brain activation when performing auditory tasks. To date, no study has controlled for musical experience, which is known to influence auditory skills. The present study tested 33 blind (11 congenital, 11 early-blind, 11 late-blind) participants and 33 matched sighted controls. We showed that the performance of blind participants was better than that of sighted participants on a range of auditory perception tasks, even when musical experience was controlled for. This advantage was observed only for individuals who became blind early in life, and was even more pronounced for individuals who were blind from birth. Years of blindness did not predict task performance. Here, we provide compelling evidence that superior auditory abilities in blind individuals are not explained by musical experience alone. These results have implications for the development of sensory substitution devices, particularly for late-blind individuals.

Distinct structural alterations independently contributing to working memory deficits and symptomatology in paranoid schizophrenia.

PubMed

Zierhut, Kathrin C; Schulte-Kemna, Anna; Kaufmann, Jörn; Steiner, Johann; Bogerts, Bernhard; Schiltz, Kolja

2013-04-01

Schizophrenia is considered a brain disease with a quite heterogeneous clinical presentation. Studies in schizophrenia have yielded a wide array of correlations between structural and functional brain changes and clinical and cognitive symptoms. Reductions of grey matter volume (GMV) in the prefrontal and temporal cortex have been described which are crucial for the development of positive and negative symptoms and impaired working memory (WM). Associations between GMV reduction and positive and negative symptoms as well as WM impairment were assessed in schizophrenia patients (symptomatology in 34, WM in 26) and compared to healthy controls (36 total, WM in 26). GMV was determined by voxel-based morphometry and its relation to positive and negative symptoms as well as WM performance was assessed. In schizophrenia patients, reductions of GMV were evident in anterior cingulate cortex, ventrolateral prefrontal cortex (VLPFC), superior temporal cortex, and insula. GMV reductions in the superior temporal gyrus (STG) were associated with positive symptom severity as well as WM impairment. Furthermore, the absolute GMV of VLPFC was strongly related to negative symptoms. These predicted WM performance as well as processing speed. The present results support the assumption of two distinct pathomechanisms responsible for impaired WM in schizophrenia: (1) GMV reductions in the VLPFC predict the severity of negative symptoms. Increased negative symptoms in turn are associated with a slowing down of processing speed and predict an impaired WM. (2) GMV reductions in the temporal and mediofrontal cortex are involved in the development of positive symptoms and impair WM performance, too. Copyright © 2012 Elsevier Ltd. All rights reserved.

Lake Superior Zooplankton Biomass Predictions from LOPC Tow Surveys Compare Well with a Probability Based Net Survey

EPA Science Inventory

We conducted a probability-based sampling of Lake Superior in 2006 and compared the zooplankton biomass estimate with laser optical plankton counter (LOPC) predictions. The net survey consisted of 52 sites stratified across three depth zones (0-30, 30-150, >150 m). The LOPC tow...

Use of classification trees to apportion single echo detections to species: Application to the pelagic fish community of Lake Superior

USGS Publications Warehouse

Yule, Daniel L.; Adams, Jean V.; Hrabik, Thomas R.; Vinson, Mark R.; Woiak, Zebadiah; Ahrenstroff, Tyler D.

2013-01-01

Acoustic methods are used to estimate the density of pelagic fish in large lakes with results of midwater trawling used to assign species composition. Apportionment in lakes having mixed species can be challenging because only a small fraction of the water sampled acoustically is sampled with trawl gear. Here we describe a new method where single echo detections (SEDs) are assigned to species based on classification tree models developed from catch data that separate species based on fish size and the spatial habitats they occupy. During the summer of 2011, we conducted a spatially-balanced lake-wide acoustic and midwater trawl survey of Lake Superior. A total of 51 sites in four bathymetric depth strata (0–30 m, 30–100 m, 100–200 m, and >200 m) were sampled. We developed classification tree models for each stratum and found fish length was the most important variable for separating species. To apply these trees to the acoustic data, we needed to identify a target strength to length (TS-to-L) relationship appropriate for all abundant Lake Superior pelagic species. We tested performance of 7 general (i.e., multi-species) relationships derived from three published studies. The best-performing relationship was identified by comparing predicted and observed catch compositions using a second independent Lake Superior data set. Once identified, the relationship was used to predict lengths of SEDs from the lake-wide survey, and the classification tree models were used to assign each SED to a species. Exotic rainbow smelt (Osmerus mordax) were the most common species at bathymetric depths 100 m (384 million; 6.0 kt). Cisco (Coregonus artedi) were widely distributed over all strata with their population estimated at 182 million (44 kt). The apportionment method we describe should be transferable to other large lakes provided fish are not tightly aggregated, and an appropriate TS-to-L relationship for abundant pelagic fish species can be determined.

Integrative Approaches for Predicting in vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-term Biological Assays

PubMed Central

Low, Yen S.; Sedykh, Alexander; Rusyn, Ivan; Tropsha, Alexander

2017-01-01

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity. PMID:24805064

Application of ANN and fuzzy logic algorithms for streamflow modelling of Savitri catchment

NASA Astrophysics Data System (ADS)

Kothari, Mahesh; Gharde, K. D.

2015-07-01

The streamflow prediction is an essentially important aspect of any watershed modelling. The black box models (soft computing techniques) have proven to be an efficient alternative to physical (traditional) methods for simulating streamflow and sediment yield of the catchments. The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years (1992-2011) rainfall and other hydrological data were considered, of which 13 years (1992-2004) was for training and rest 7 years (2005-2011) for validation of the models. The mode performance was evaluated by R, RMSE, EV, CE, and MAD statistical parameters. It was found that, ANN model performance improved with increasing input vectors. The results with fuzzy logic models predict the streamflow with single input as rainfall better in comparison to multiple input vectors. While comparing both ANN and FL algorithms for prediction of streamflow, ANN model performance is quite superior.

The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing

PubMed Central

Weber, Kirsten; Lau, Ellen F.; Stillerman, Benjamin; Kuperberg, Gina R.

2016-01-01

Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions—a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment. PMID:27010386

The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing.

PubMed

Weber, Kirsten; Lau, Ellen F; Stillerman, Benjamin; Kuperberg, Gina R

2016-01-01

Probabilistic prediction plays a crucial role in language comprehension. When predictions are fulfilled, the resulting facilitation allows for fast, efficient processing of ambiguous, rapidly-unfolding input; when predictions are not fulfilled, the resulting error signal allows us to adapt to broader statistical changes in this input. We used functional Magnetic Resonance Imaging to examine the neuroanatomical networks engaged in semantic predictive processing and adaptation. We used a relatedness proportion semantic priming paradigm, in which we manipulated the probability of predictions while holding local semantic context constant. Under conditions of higher (versus lower) predictive validity, we replicate previous observations of reduced activity to semantically predictable words in the left anterior superior/middle temporal cortex, reflecting facilitated processing of targets that are consistent with prior semantic predictions. In addition, under conditions of higher (versus lower) predictive validity we observed significant differences in the effects of semantic relatedness within the left inferior frontal gyrus and the posterior portion of the left superior/middle temporal gyrus. We suggest that together these two regions mediated the suppression of unfulfilled semantic predictions and lexico-semantic processing of unrelated targets that were inconsistent with these predictions. Moreover, under conditions of higher (versus lower) predictive validity, a functional connectivity analysis showed that the left inferior frontal and left posterior superior/middle temporal gyrus were more tightly interconnected with one another, as well as with the left anterior cingulate cortex. The left anterior cingulate cortex was, in turn, more tightly connected to superior lateral frontal cortices and subcortical regions-a network that mediates rapid learning and adaptation and that may have played a role in switching to a more predictive mode of processing in response to the statistical structure of the wider environmental context. Together, these findings highlight close links between the networks mediating semantic prediction, executive function and learning, giving new insights into how our brains are able to flexibly adapt to our environment.

Intrinsic spontaneous brain activity predicts individual variability in associative memory in older adults.

PubMed

Zheng, Zhiwei; Li, Rui; Xiao, Fengqiu; He, Rongqiao; Zhang, Shouzi; Li, Juan

2018-06-01

Older adults demonstrate notable individual differences in associative memory. Here, resting-state functional magnetic resonance imaging (rsfMRI) was used to investigate whether intrinsic brain activity at rest could predict individual differences in associative memory among cognitively healthy older adults. Regional amplitude of low-frequency fluctuations (ALFF) analysis and a correlation-based resting-state functional connectivity (RSFC) approach were used to analyze data acquired from 102 cognitively normal elderly who completed the paired-associative learning test (PALT) and underwent fMRI scans. Participants were divided into two groups based on the retrospective self-reports on whether or not they utilized encoding strategies during the PALT. The behavioral results revealed better associative memory performance in the participants who reported utilizing memory strategies compared with participants who reported not doing so. The fMRI results showed that higher associative memory performance was associated with greater functional connectivity between the right superior frontal gyrus and the right posterior cerebellum lobe in the strategy group. The regional ALFF values in the right superior frontal gyrus were linked to associative memory performance in the no-strategy group. These findings suggest that the regional spontaneous fluctuations and functional connectivity during rest may subserve the individual differences in the associative memory in older adults, and that this is modulated by self-initiated memory strategy use. © 2018 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

Do workaholism and work engagement predict employee well-being and performance in opposite directions?

PubMed

Shimazu, Akihito; Schaufeli, Wilmar B; Kubota, Kazumi; Kawakami, Norito

2012-01-01

This study investigated the distinctiveness between workaholism and work engagement by examining their longitudinal relationships (measurement interval=7 months) with well-being and performance in a sample of 1,967 Japanese employees from various occupations. Based on a previous cross-sectional study (Shimazu & Schaufeli, 2009), we expected that workaholism predicts future unwell-being (i.e., high ill-health and low life satisfaction) and poor job performance, whereas work engagement predicts future well-being (i.e., low ill-health and high life satisfaction) and superior job performance. T1-T2 changes in ill-health, life satisfaction and job performance were measured as residual scores that were then included in the structural equation model. Results showed that workaholism and work engagement were weakly and positively related to each other. In addition, workaholism was related to an increase in ill-health and to a decrease in life satisfaction. In contrast, work engagement was related to a decrease in ill-health and to increases in both life satisfaction and job performance. These findings suggest that workaholism and work engagement are two different kinds of concepts that are oppositely related to well-being and performance.

Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians.

PubMed

Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J; MacLeod, Mary Joan

2017-05-01

physicians are often asked to prognosticate soon after a patient presents with stroke. This study aimed to compare two outcome prediction scores (Five Simple Variables [FSV] score and the PLAN [Preadmission comorbidities, Level of consciousness, Age, and focal Neurologic deficit]) with informal prediction by physicians. demographic and clinical variables were prospectively collected from consecutive patients hospitalised with acute ischaemic or haemorrhagic stroke (2012-13). In-person or telephone follow-up at 6 months established vital and functional status (modified Rankin score [mRS]). Area under the receiver operating curves (AUC) was used to establish prediction score performance. five hundred and seventy-five patients were included; 46% female, median age 76 years, 88% ischaemic stroke. Six months after stroke, 47% of patients had a good outcome (alive and independent, mRS 0-2) and 26% a devastating outcome (dead or severely dependent, mRS 5-6). The FSV and PLAN scores were superior to physician prediction (AUCs of 0.823-0.863 versus 0.773-0.805, P < 0.0001) for good and devastating outcomes. The FSV score was superior to the PLAN score for predicting good outcomes and vice versa for devastating outcomes (P < 0.001). Outcome prediction was more accurate for those with later presentations (>24 hours from onset). the FSV and PLAN scores are validated in this population for outcome prediction after both ischaemic and haemorrhagic stroke. The FSV score is the least complex of all developed scores and can assist outcome prediction by physicians. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

Improving the performance of computer color matching procedures.

PubMed

Karbasi, A; Moradian, S; Asiaban, S

2008-09-01

A premise was set up entailing the possibility of a synergistical combination of advantages of spectrophotometric and colorimetric matching procedures. Attempts were therefore made to test the performances of fifteen matching procedures, all based on the Kubelka-Munk theory, including two procedures utilizing the fundamental color stimulus R(FCS) of the spectral decomposition theory. Color differences CIE DeltaE(00) as well as concentration differences DeltaC(AVE) were used to theoretically rank the fifteen color matching procedures. Results showed that procedures based on R(FCS) were superior in accurately predicting colors and concentrations. Additionally, the metameric black component R(MB) of the decomposition theory also showed promise in predicting degrees of metamerism. This preliminary study, therefore, provides evidence for the premise of this investigation.

An application of locally linear model tree algorithm with combination of feature selection in credit scoring

NASA Astrophysics Data System (ADS)

Siami, Mohammad; Gholamian, Mohammad Reza; Basiri, Javad

2014-10-01

Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets - Australian and German - from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.

Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning

PubMed Central

Zhang, Yudong; Dong, Zhengchao; Phillips, Preetha; Wang, Shuihua; Ji, Genlin; Yang, Jiquan; Yuan, Ti-Fei

2015-01-01

Purpose: Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions. Method: First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC. Results: The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures. Conclusion: The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning. PMID:26082713

Predictive coding in autism spectrum disorder and attention deficit hyperactivity disorder.

PubMed

Gonzalez-Gadea, Maria Luz; Chennu, Srivas; Bekinschtein, Tristan A; Rattazzi, Alexia; Beraudi, Ana; Tripicchio, Paula; Moyano, Beatriz; Soffita, Yamila; Steinberg, Laura; Adolfi, Federico; Sigman, Mariano; Marino, Julian; Manes, Facundo; Ibanez, Agustin

2015-11-01

Predictive coding has been proposed as a framework to understand neural processes in neuropsychiatric disorders. We used this approach to describe mechanisms responsible for attentional abnormalities in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). We monitored brain dynamics of 59 children (8-15 yr old) who had ASD or ADHD or who were control participants via high-density electroencephalography. We performed analysis at the scalp and source-space levels while participants listened to standard and deviant tone sequences. Through task instructions, we manipulated top-down expectation by presenting expected and unexpected deviant sequences. Children with ASD showed reduced superior frontal cortex (FC) responses to unexpected events but increased dorsolateral prefrontal cortex (PFC) activation to expected events. In contrast, children with ADHD exhibited reduced cortical responses in superior FC to expected events but strong PFC activation to unexpected events. Moreover, neural abnormalities were associated with specific control mechanisms, namely, inhibitory control in ASD and set-shifting in ADHD. Based on the predictive coding account, top-down expectation abnormalities could be attributed to a disproportionate reliance (precision) allocated to prior beliefs in ASD and to sensory input in ADHD. Copyright © 2015 the American Physiological Society.

Predictive coding in autism spectrum disorder and attention deficit hyperactivity disorder

PubMed Central

Gonzalez-Gadea, Maria Luz; Chennu, Srivas; Bekinschtein, Tristan A.; Rattazzi, Alexia; Beraudi, Ana; Tripicchio, Paula; Moyano, Beatriz; Soffita, Yamila; Steinberg, Laura; Adolfi, Federico; Sigman, Mariano; Marino, Julian; Manes, Facundo

2015-01-01

Predictive coding has been proposed as a framework to understand neural processes in neuropsychiatric disorders. We used this approach to describe mechanisms responsible for attentional abnormalities in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). We monitored brain dynamics of 59 children (8–15 yr old) who had ASD or ADHD or who were control participants via high-density electroencephalography. We performed analysis at the scalp and source-space levels while participants listened to standard and deviant tone sequences. Through task instructions, we manipulated top-down expectation by presenting expected and unexpected deviant sequences. Children with ASD showed reduced superior frontal cortex (FC) responses to unexpected events but increased dorsolateral prefrontal cortex (PFC) activation to expected events. In contrast, children with ADHD exhibited reduced cortical responses in superior FC to expected events but strong PFC activation to unexpected events. Moreover, neural abnormalities were associated with specific control mechanisms, namely, inhibitory control in ASD and set-shifting in ADHD. Based on the predictive coding account, top-down expectation abnormalities could be attributed to a disproportionate reliance (precision) allocated to prior beliefs in ASD and to sensory input in ADHD. PMID:26311184

Prediction of Indentation Behavior of Superelastic TiNi

NASA Astrophysics Data System (ADS)

Neupane, Rabin; Farhat, Zoheir

2014-09-01

Superelastic TiNi shape memory alloys have been extensively used in various applications. The great interest in TiNi alloys is due to its unique shape memory and superelastic effects, along with its superior wear and dent resistance. Assessment of mechanical properties and dent resistance of superelastic TiNi is commonly performed using indentation techniques. However, the coupling of deformation and reversible martensitic transformation of TiNi under indentation conditions makes the interpretation of results challenging. An attempt is made to enhance current interpretation of indentation data. A load-depth curve is predicted that takes into consideration the reversible martensitic transformation. The predicted curve is in good agreement with experimental results. It is found in this study that the elastic modulus is a function of indentation depth. At shallow depths, the elastic modulus is high due to austenite dominance, while at high depths, the elastic modulus drops as the depth increases due to austenite to martensite transition, i.e., martensite dominance. It is also found that TiNi exhibits superior dent resistance compared to AISI 304 steel. There is two orders of magnitude improvement in dent resistance of TiNi in comparison to AISI 304 steel.

The rise of deep learning in drug discovery.

PubMed

Chen, Hongming; Engkvist, Ola; Wang, Yinhai; Olivecrona, Marcus; Blaschke, Thomas

2018-06-01

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Diagnostic methods for CW laser damage testing

NASA Astrophysics Data System (ADS)

Stewart, Alan F.; Shah, Rashmi S.

2004-06-01

High performance optical coatings are an enabling technology for many applications - navigation systems, telecom, fusion, advanced measurement systems of many types as well as directed energy weapons. The results of recent testing of superior optical coatings conducted at high flux levels will be presented. The diagnostics used in this type of nondestructive testing and the analysis of the data demonstrates the evolution of test methodology. Comparison of performance data under load to the predictions of thermal and optical models shows excellent agreement. These tests serve to anchor the models and validate the performance of the materials and coatings.

Superior Disembedding Performance in Childhood Predicts Adolescent Severity of Repetitive Behaviors: A Seven Years Follow-Up of Individuals With Autism Spectrum Disorder.

PubMed

Eussen, Mart L J M; Van Gool, Arthur R; Louwerse, Anneke; Verhulst, Frank C; Greaves-Lord, Kirstin

2016-02-01

Previous research suggests that individuals with autism spectrum disorder (ASD) show a detail-focused cognitive style. The aim of the current longitudinal study was to investigate whether this detail-focused cognitive style in childhood predicted a higher symptom severity of repetitive and restrictive behaviors and interests (RRBI) in adolescence. The Childhood Embedded Figures Test (CEFT) and the Autism Diagnostic Observation Schedule (ADOS) were administered in 87 children with ASD at the age of 6-12 years old (T1), and the ADOS was readministered 7 years later when the participants were 12-19 years old (T2). Linear regression analyses were performed to investigate whether accuracy and reaction time in the complex versus simple CEFT condition and performance in the complex condition predicted T2 ADOS RRBI calibrated severity scores (CSS), while taking into consideration relevant covariates and ADOS RRBI CSS at T1. The CEFT performance (accuracy in the complex condition divided by the time needed) significantly predicted higher ADOS RRBI CSS at T2 (ΔR(2)  = 15%). This finding further supports the detail-focused cognitive style in individuals with ASD, and shows that it is also predictive of future RRBI symptoms over time. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Examining the predictive accuracy of the novel 3D N-linear algebraic molecular codifications on benchmark datasets.

PubMed

García-Jacas, César R; Contreras-Torres, Ernesto; Marrero-Ponce, Yovani; Pupo-Meriño, Mario; Barigye, Stephen J; Cabrera-Leyva, Lisset

2016-01-01

Recently, novel 3D alignment-free molecular descriptors (also known as QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been introduced. These descriptors codify chemical information for relations between two, three and four atoms by using several (dis-)similarity metrics and multi-metrics. Several studies aimed at assessing the quality of these novel descriptors have been performed. However, a deeper analysis of their performance is necessary. Therefore, in the present manuscript an assessment and statistical validation of the performance of these novel descriptors in QSAR studies is performed. To this end, eight molecular datasets (angiotensin converting enzyme, acetylcholinesterase inhibitors, benzodiazepine receptor, cyclooxygenase-2 inhibitors, dihydrofolate reductase inhibitors, glycogen phosphorylase b, thermolysin inhibitors, thrombin inhibitors) widely used as benchmarks in the evaluation of several procedures are utilized. Three to nine variable QSAR models based on Multiple Linear Regression are built for each chemical dataset according to the original division into training/test sets. Comparisons with respect to leave-one-out cross-validation correlation coefficients[Formula: see text] reveal that the models based on QuBiLS-MIDAS indices possess superior predictive ability in 7 of the 8 datasets analyzed, outperforming methodologies based on similar or more complex techniques such as: Partial Least Square, Neural Networks, Support Vector Machine and others. On the other hand, superior external correlation coefficients[Formula: see text] are attained in 6 of the 8 test sets considered, confirming the good predictive power of the obtained models. For the [Formula: see text] values non-parametric statistic tests were performed, which demonstrated that the models based on QuBiLS-MIDAS indices have the best global performance and yield significantly better predictions in 11 of the 12 QSAR procedures used in the comparison. Lastly, a study concerning to the performance of the indices according to several conformer generation methods was performed. This demonstrated that the quality of predictions of the QSAR models based on QuBiLS-MIDAS indices depend on 3D structure generation method considered, although in this preliminary study the results achieved do not present significant statistical differences among them. As conclusions it can be stated that the QuBiLS-MIDAS indices are suitable for extracting structural information of the molecules and thus, constitute a promissory alternative to build models that contribute to the prediction of pharmacokinetic, pharmacodynamics and toxicological properties on novel compounds.Graphical abstractComparative graphical representation of the performance of the novel QuBiLS-MIDAS 3D-MDs with respect to other methodologies in QSAR modeling of eight chemical datasets.

Blocking performance of the hose model and the pipe model for VPN service provisioning over WDM optical networks

NASA Astrophysics Data System (ADS)

Wang, Haibo; Swee Poo, Gee

2004-08-01

We study the provisioning of virtual private network (VPN) service over WDM optical networks. For this purpose, we investigate the blocking performance of the hose model versus the pipe model for the provisioning. Two techniques are presented: an analytical queuing model and a discrete event simulation. The queuing model is developed from the multirate reduced-load approximation technique. The simulation is done with the OPNET simulator. Several experimental situations were used. The blocking probabilities calculated from the two approaches show a close match, indicating that the multirate reduced-load approximation technique is capable of predicting the blocking performance for the pipe model and the hose model in WDM networks. A comparison of the blocking behavior of the two models shows that the hose model has superior blocking performance as compared with pipe model. By and large, the blocking probability of the hose model is better than that of the pipe model by a few orders of magnitude, particularly at low load regions. The flexibility of the hose model allowing for the sharing of resources on a link among all connections accounts for its superior performance.

A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

PubMed

Van Looy, Stijn; Verplancke, Thierry; Benoit, Dominique; Hoste, Eric; Van Maele, Georges; De Turck, Filip; Decruyenaere, Johan

2007-01-01

Tacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR). Tacrolimus blood concentrations, together with 35 other relevant variables from 50 liver transplantation patients, were extracted from our ICU database. This resulted in a dataset of 457 blood samples, on average between 9 and 10 samples per patient, finally resulting in a database of more than 16,000 data values. Nonlinear RBF SVR, linear SVR, and MLR were performed after selection of clinically relevant input variables and model parameters. Differences between observed and predicted tacrolimus blood concentrations were calculated. Prediction accuracy of the three methods was compared after fivefold cross-validation (Friedman test and Wilcoxon signed rank analysis). Linear SVR and nonlinear RBF SVR had mean absolute differences between observed and predicted tacrolimus blood concentrations of 2.31 ng/ml (standard deviation [SD] 2.47) and 2.38 ng/ml (SD 2.49), respectively. MLR had a mean absolute difference of 2.73 ng/ml (SD 3.79). The difference between linear SVR and MLR was statistically significant (p < 0.001). RBF SVR had the advantage of requiring only 2 input variables to perform this prediction in comparison to 15 and 16 variables needed by linear SVR and MLR, respectively. This is an indication of the superior prediction capability of nonlinear SVR. Prediction of tacrolimus blood concentration with linear and nonlinear SVR was excellent, and accuracy was superior in comparison with an MLR model.

The injury severity score or the new injury severity score for predicting mortality, intensive care unit admission and length of hospital stay: experience from a university hospital in a developing country.

PubMed

Tamim, Hala; Al Hazzouri, Adina Zeki; Mahfoud, Ziad; Atoui, Maria; El-Chemaly, Souheil

2008-01-01

Limited research has been performed to compare the predictive abilities of the injury severity score (ISS) and the new ISS (NISS) in the developing world. From January 2001 until January 2003 all trauma patients admitted to the American University of Beirut Medical Centre were enrolled. The statistical performance of the ISS/NISS in predicting mortality, admission to the intensive care unit (ICU) and length of hospital stay (LOS dichotomised as <10 or > or =10 days) was evaluated using receiver operating characteristic and the Hosmer-Lemeshow calibration statistic. A total of 891 consecutive patients were enrolled. The ISS and NISS were equivalent in predicting survival, and both performed better in patients younger than 65 years of age. However, the ISS predicted ICU admission and LOS better than the NISS. However, these predictive abilities were lower for the geriatric trauma patients aged 65 years and above compared to the other age groups. There are conflicting results in the literature about the abilities of ISS and NISS to predict mortality. However, this is the first study to report that ISS has a superior ability in predicting both LOS and ICU admission. The scoring of trauma severity may need to be individualised to different countries and trauma systems.

Prediction of R-curves from small coupon tests

NASA Technical Reports Server (NTRS)

Yeh, J. R.; Bray, G. H.; Bucci, R. J.; Macheret, Y.

1994-01-01

R-curves were predicted for Alclad 2024-T3 and C188-T3 sheet using the results of small-coupon Kahn tear tests in combination with two-dimensional elastic-plastic finite element stress analyses. The predictions were compared to experimental R-curves from 6.3, 16 and 60-inch wide M(T) specimens and good agreement was obtained. The method is an inexpensive alternative to wide panel testing for characterizing the fracture toughness of damage-tolerant sheet alloys. The usefulness of this approach was demonstrated by performing residual strength calculations for a two-bay crack in a representative fuselage structure. C188-T3 was predicted to have a 24 percent higher load carrying capability than 2024-T3 in this application as a result of its superior fracture toughness.

A Grey NGM(1,1, k) Self-Memory Coupling Prediction Model for Energy Consumption Prediction

PubMed Central

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey NGM(1,1, k) self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey NGM(1,1, k) model. The traditional grey model's weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of NGM(1,1, k) self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span. PMID:25054174

A Prediction for the Outcome of Press-Enterprise Co. v. Superior Court (II).

ERIC Educational Resources Information Center

Schwartz, Thomas A.

To determine whether U.S. Supreme Court judges have a systematic attitude toward court cases dealing with the law of newsgathering and fair trial-free press, and whether that attitude can help predict the outcome of the pending case Press-Enterprise Co. v. Superior Court (II), this paper applies an attitudinal theory from the field of social…

Grain-boundary physics in polycrystalline CuInSe2 revisited: experiment and theory.

PubMed

Yan, Yanfa; Noufi, R; Al-Jassim, M M

2006-05-26

Current studies have attributed the remarkable performance of polycrystalline CuInSe2 (CIS) to anomalous grain-boundary (GB) physics in CIS. The recent theory predicts that GBs in CIS are hole barriers, which prevent GB electrons from recombining. We examine the atomic structure and chemical composition of (112) GBs in Cu(In,Ga)Se2 (CIGS) using high-resolution Z-contrast imaging and nanoprobe x-ray energy-dispersive spectroscopy. We show that the theoretically predicted Cu-vacancy rows are not observed in (112) GBs in CIGS. Our first-principles modeling further reveals that the (112) GBs in CIS do not act as hole barriers. Our results suggest that the superior performance of polycrystalline CIS should not be explained solely by the GB behaviors.

An Evaluation of the Predictability of Austral Summer Season Precipitation over South America.

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu

2004-03-01

In this study predictability of austral summer seasonal precipitation over South America is investigated using a 12-yr set of a 3.5-month range (seasonal) and a 17-yr range (continuous multiannual) five-member ensemble integrations of the Center for Ocean Land Atmosphere Studies (COLA) atmospheric general circulation model (AGCM). These integrations were performed with prescribed observed sea surface temperature (SST); therefore, skill attained represents an estimate of the upper bound of the skill achievable by COLA AGCM with predicted SST. The seasonal runs outperform the multiannual model integrations both in deterministic and probabilistic skill. The simulation of the January February March (JFM) seasonal climatology of precipitation is vastly superior in the seasonal runs except over the Nordeste region where the multiannual runs show a marginal improvement. The teleconnection of the ensemble mean JFM precipitation over tropical South America with global contemporaneous observed sea surface temperature in the seasonal runs conforms more closely to observations than in the multiannual runs. Both the sets of runs clearly beat persistence in predicting the interannual precipitation anomalies over the Amazon River basin, Nordeste, South Atlantic convergence zone, and subtropical South America. However, both types of runs display poorer simulations over subtropical regions than the tropical areas of South America. The examination of probabilistic skill of precipitation supports the conclusions from deterministic skill analysis that the seasonal runs yield superior simulations than the multiannual-type runs.

Measuring Financial Gains from Genetically Superior Trees

Treesearch

George Dutrow; Clark Row

1976-01-01

Planting genetically superior loblolly pines will probably yield high profits.Forest economists have made computer simulations that predict financial gains expected from a tree improvement program under actual field conditions.

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

PubMed Central

Yang, Chuanlei; Wang, Yinyan; Wang, Hechun

2018-01-01

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

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

PubMed

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

2018-01-01

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

Comparison of high-resolution magnification narrow-band imaging and white-light endoscopy in the prediction of histology in Barrett's oesophagus.

PubMed

Singh, Rajvinder; Karageorgiou, Haris; Owen, Victoria; Garsed, Klara; Fortun, Paul J; Fogden, Edward; Subramaniam, Venkataraman; Shonde, Anthony; Kaye, Philip; Hawkey, Christopher J; Ragunath, Krish

2009-01-01

To evaluate whether there is any appreciable difference in imaging characteristics between high-resolution magnification white-light endoscopy (WLE-Z) and narrow-band imaging (NBI-Z) in Barrett's oesophagus (BE) and if this translates into superior prediction of histology. This was a prospective single-centre study involving 21 patients (75 areas, corresponding NBI-Z and WLE-Z images) with BE. Mucosal patterns (pit pattern and microvascular morphology) were evaluated for their image quality on a visual analogue scale (VAS) of 1-10 by five expert endoscopists. The endoscopists then predicted mucosal morphology based on four subtypes which can be visualized in BE. Type A: round pits, regular microvasculature; type B: villous/ridge pits, regular microvasculature; type C: absent pits, regular microvasculature; type D: distorted pits, irregular microvasculature. The sensitivity (Sn), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were then compared with the final histopathological analysis and the interobserver variability calculated. The overall pit and microvasculature quality was significantly higher for NBI-Z, pit: NBI-Z=6, WLE-Z=4.5, p < 0.001; microvasculature: NBI-Z=7.3, WLE-Z=4.9, p < 0.001. This translated into a superior prediction of histology (Sn: NBI-Z: 88.9, WLE-Z: 71.9, p < 0.001). For the prediction of dysplasia, NBI-Z was superior to WLE-Z (chi(2)=10.3, p < 0.05). The overall kappa agreement among the five endoscopists for NBI-Z and WLE-Z, respectively, was 0.59 and 0.31 (p < 0.001). NBI-Z is superior to WLE-Z in the prediction of histology in BE, with good reproducibility. This novel imaging modality could be an important tool for surveillance of patients with BE.

Placental Alpha Microglobulin-1 Compared With Fetal Fibronectin to Predict Preterm Delivery in Symptomatic Women.

PubMed

Wing, Deborah A; Haeri, Sina; Silber, Angela C; Roth, Cheryl K; Weiner, Carl P; Echebiri, Nelson C; Franco, Albert; Pappas, Lanissa M; Yeast, John D; Brebnor, Angelle A; Quirk, J Gerald; Murphy, Aisling M; Laurent, Louise C; Field, Nancy T; Norton, Mary E

2017-12-01

To compare the rapid bedside test for placental α microglobulin-1 with the instrumented fetal fibronectin test for prediction of imminent spontaneous preterm delivery among women with symptoms of preterm labor. We conducted a prospective observational study on pregnant women with signs or symptoms suggestive of preterm labor between 24 and 35 weeks of gestation with intact membranes and cervical dilatation less than 3 cm. Participants were prospectively enrolled at 15 U.S. academic and community centers. Placental α microglobulin-1 samples did not require a speculum examination. Health care providers were blinded to placental α microglobulin-1 results. Fetal fibronectin samples were collected through speculum examination per manufacturer requirements and sent to a certified laboratory for testing using a cutoff of 50 ng/mL. The coprimary endpoints were positive predictive value (PPV) superiority and negative predictive value (NPV) noninferiority of placental α microglobulin-1 compared with fetal fibronectin for the prediction of spontaneous preterm birth within 7 days and within 14 days. Of 796 women included in the study cohort, 711 (89.3%) had both placental α microglobulin-1 and fetal fibronectin results and valid delivery outcomes available for analysis. The overall rate of preterm birth was 2.4% (17/711) within 7 days of testing and 4.2% (30/711) within 14 days of testing with respective rates of spontaneous preterm birth of 1.3% (9/703) and 2.9% (20/701). Fetal fibronectin was detected in 15.5% (110/711), and placental α microglobulin-1 was detected in 2.4% (17/711). The PPVs for spontaneous preterm delivery within 7 days or less among singleton gestations (n=13) for placental α microglobulin-1 and fetal fibronectin were 23.1% (3/13) and 4.3% (4/94), respectively (P<.025 for superiority). The NPVs were 99.5% (619/622) and 99.6% (539/541) for placental α microglobulin-1 and fetal fibronectin, respectively (P<.001 for noninferiority). Although placental α microglobulin-1 performed the same as fetal fibronectin in ruling out spontaneous preterm delivery among symptomatic women, it demonstrated statistical superiority in predicting it.

Iterative procedures for space shuttle main engine performance models

NASA Technical Reports Server (NTRS)

Santi, L. Michael

1989-01-01

Performance models of the Space Shuttle Main Engine (SSME) contain iterative strategies for determining approximate solutions to nonlinear equations reflecting fundamental mass, energy, and pressure balances within engine flow systems. Both univariate and multivariate Newton-Raphson algorithms are employed in the current version of the engine Test Information Program (TIP). Computational efficiency and reliability of these procedures is examined. A modified trust region form of the multivariate Newton-Raphson method is implemented and shown to be superior for off nominal engine performance predictions. A heuristic form of Broyden's Rank One method is also tested and favorable results based on this algorithm are presented.

Soccer athletes are superior to non-athletes at perceiving soccer-specific and non-sport specific human biological motion

PubMed Central

Romeas, Thomas; Faubert, Jocelyn

2015-01-01

Recent studies have shown that athletes’ domain specific perceptual-cognitive expertise can transfer to everyday tasks. Here we assessed the perceptual-cognitive expertise of athletes and non-athletes using sport specific and non-sport specific biological motion perception (BMP) tasks. Using a virtual environment, university-level soccer players and university students’ non-athletes were asked to perceive the direction of a point-light walker and to predict the trajectory of a masked-ball during a point-light soccer kick. Angles of presentation were varied for orientation (upright, inverted) and distance (2 m, 4 m, 16 m). Accuracy and reaction time were measured to assess observers’ performance. The results highlighted athletes’ superior ability compared to non-athletes to accurately predict the trajectory of a masked soccer ball presented at 2 m (reaction time), 4 m (accuracy and reaction time), and 16 m (accuracy) of distance. More interestingly, experts also displayed greater performance compared to non-athletes throughout the more fundamental and general point-light walker direction task presented at 2 m (reaction time), 4 m (accuracy and reaction time), and 16 m (reaction time) of distance. In addition, athletes showed a better performance throughout inverted conditions in the walker (reaction time) and soccer kick (accuracy and reaction time) tasks. This implies that during human BMP, athletes demonstrate an advantage for recognizing body kinematics that goes beyond sport specific actions. PMID:26388828

Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

NASA Astrophysics Data System (ADS)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

Is the superior verbal memory span of Mandarin speakers due to faster rehearsal?

PubMed

Mattys, Sven L; Baddeley, Alan; Trenkic, Danijela

2018-04-01

It is well established that digit span in native Chinese speakers is atypically high. This is commonly attributed to a capacity for more rapid subvocal rehearsal for that group. We explored this hypothesis by testing a group of English-speaking native Mandarin speakers on digit span and word span in both Mandarin and English, together with a measure of speed of articulation for each. When compared to the performance of native English speakers, the Mandarin group proved to be superior on both digit and word spans while predictably having lower spans in English. This suggests that the Mandarin advantage is not limited to digits. Speed of rehearsal correlated with span performance across materials. However, this correlation was more pronounced for English speakers than for any of the Chinese measures. Further analysis suggested that speed of rehearsal did not provide an adequate account of differences between Mandarin and English spans or for the advantage of digits over words. Possible alternative explanations are discussed.

Higher integrity of the motor and visual pathways in long-term video game players.

PubMed

Zhang, Yang; Du, Guijin; Yang, Yongxin; Qin, Wen; Li, Xiaodong; Zhang, Quan

2015-01-01

Long term video game players (VGPs) exhibit superior visual and motor skills compared with non-video game control subjects (NVGCs). However, the neural basis underlying the enhanced behavioral performance remains largely unknown. To clarify this issue, the present study compared the whiter matter integrity within the corticospinal tracts (CST), the superior longitudinal fasciculus (SLF), the inferior longitudinal fasciculus (ILF), and the inferior fronto-occipital fasciculus (IFOF) between the VGPs and the NVGCs using diffusion tensor imaging. Compared with the NVGCs, voxel-wise comparisons revealed significantly higher fractional anisotropy (FA) values in some regions within the left CST, left SLF, bilateral ILF, and IFOF in VGPs. Furthermore, higher FA values in the left CST at the level of cerebral peduncle predicted a faster response in visual attention tasks. These results suggest that higher white matter integrity in the motor and higher-tier visual pathways is associated with long-term video game playing, which may contribute to the understanding on how video game play influences motor and visual performance.

Higher integrity of the motor and visual pathways in long-term video game players

PubMed Central

Du, Guijin; Yang, Yongxin; Qin, Wen; Li, Xiaodong; Zhang, Quan

2015-01-01

Long term video game players (VGPs) exhibit superior visual and motor skills compared with non-video game control subjects (NVGCs). However, the neural basis underlying the enhanced behavioral performance remains largely unknown. To clarify this issue, the present study compared the whiter matter integrity within the corticospinal tracts (CST), the superior longitudinal fasciculus (SLF), the inferior longitudinal fasciculus (ILF), and the inferior fronto-occipital fasciculus (IFOF) between the VGPs and the NVGCs using diffusion tensor imaging. Compared with the NVGCs, voxel-wise comparisons revealed significantly higher fractional anisotropy (FA) values in some regions within the left CST, left SLF, bilateral ILF, and IFOF in VGPs. Furthermore, higher FA values in the left CST at the level of cerebral peduncle predicted a faster response in visual attention tasks. These results suggest that higher white matter integrity in the motor and higher-tier visual pathways is associated with long-term video game playing, which may contribute to the understanding on how video game play influences motor and visual performance. PMID:25805981

Operational planning using Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS)

NASA Astrophysics Data System (ADS)

O'Connor, Alison; Kirtman, Benjamin; Harrison, Scott; Gorman, Joe

2016-05-01

The US Navy faces several limitations when planning operations in regard to forecasting environmental conditions. Currently, mission analysis and planning tools rely heavily on short-term (less than a week) forecasts or long-term statistical climate products. However, newly available data in the form of weather forecast ensembles provides dynamical and statistical extended-range predictions that can produce more accurate predictions if ensemble members can be combined correctly. Charles River Analytics is designing the Climatological Observations for Maritime Prediction and Analysis Support Service (COMPASS), which performs data fusion over extended-range multi-model ensembles, such as the North American Multi-Model Ensemble (NMME), to produce a unified forecast for several weeks to several seasons in the future. We evaluated thirty years of forecasts using machine learning to select predictions for an all-encompassing and superior forecast that can be used to inform the Navy's decision planning process.

Performance Evaluation of Five Different Disseminated Intravascular Coagulation (DIC) Diagnostic Criteria for Predicting Mortality in Patients with Complicated Sepsis.

PubMed

Ha, Sang Ook; Park, Sang Hyuk; Hong, Sang Bum; Jang, Seongsoo

2016-11-01

Disseminated intravascular coagulation (DIC) is a major complication in sepsis patients. We compared the performance of five DIC diagnostic criteria, focusing on the prediction of mortality. One hundred patients with severe sepsis or septic shock admitted to intensive care unit (ICU) were enrolled. Routine DIC laboratory tests were performed over the first 4 days after admission. The overall ICU and 28-day mortality in DIC patients diagnosed from five criteria (International Society on Thrombosis and Haemostasis [ISTH], the Japanese Association for Acute Medicine [JAAM], the revised JAAM [R-JAAM], the Japanese Ministry of Health and Welfare [JMHW] and the Korean Society on Thrombosis and Hemostasis [KSTH]) were compared. Both KSTH and JMHW criteria showed superior performance than ISTH, JAAM and R-JAAM criteria in the prediction of overall ICU mortality in DIC patients (odds ratio 3.828 and 5.181, P = 0.018 and 0.006, 95% confidence interval 1.256-11.667 and 1.622-16.554, respectively) when applied at day 1 after admission, and survival analysis demonstrated significant prognostic impact of KSTH and JMHW criteria on the prediction of 28-day mortality (P = 0.007 and 0.049, respectively) when applied at day 1 after admission. In conclusion, both KSTH and JMHW criteria would be more useful than other three criteria in predicting prognosis in DIC patients with severe sepsis or septic shock.

Prediction of Chl-a concentrations in an eutrophic lake using ANN models with hybrid inputs

NASA Astrophysics Data System (ADS)

Aksoy, A.; Yuzugullu, O.

2017-12-01

Chlorophyll-a (Chl-a) concentrations in water bodies exhibit both spatial and temporal variations. As a result, frequent sampling is required with higher number of samples. This motivates the use of remote sensing as a monitoring tool. Yet, prediction performances of models that convert radiance values into Chl-a concentrations can be poor in shallow lakes. In this study, Chl-a concentrations in Lake Eymir, a shallow eutrophic lake in Ankara (Turkey), are determined using artificial neural network (ANN) models that use hybrid inputs composed of water quality and meteorological data as well as remotely sensed radiance values to improve prediction performance. Following a screening based on multi-collinearity and principal component analysis (PCA), dissolved-oxygen concentration (DO), pH, turbidity, and humidity were selected among several parameters as the constituents of the hybrid input dataset. Radiance values were obtained from QuickBird-2 satellite. Conversion of the hybrid input into Chl-a concentrations were studied for two different periods in the lake. ANN models were successful in predicting Chl-a concentrations. Yet, prediction performance declined for low Chl-a concentrations in the lake. In general, models with hybrid inputs were superior over the ones that solely used remotely sensed data.

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials.

PubMed

Michel, Sebastian; Ametz, Christian; Gungor, Huseyin; Akgöl, Batuhan; Epure, Doru; Grausgruber, Heinrich; Löschenberger, Franziska; Buerstmayr, Hermann

2017-02-01

Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

Impact of input mask signals on delay-based photonic reservoir computing with semiconductor lasers.

PubMed

Kuriki, Yoma; Nakayama, Joma; Takano, Kosuke; Uchida, Atsushi

2018-03-05

We experimentally investigate delay-based photonic reservoir computing using semiconductor lasers with optical feedback and injection. We apply different types of temporal mask signals, such as digital, chaos, and colored-noise mask signals, as the weights between the input signal and the virtual nodes in the reservoir. We evaluate the performance of reservoir computing by using a time-series prediction task for the different mask signals. The chaos mask signal shows superior performance than that of the digital mask signals. However, similar prediction errors can be achieved for the chaos and colored-noise mask signals. Mask signals with larger amplitudes result in better performance for all mask signals in the range of the amplitude accessible in our experiment. The performance of reservoir computing is strongly dependent on the cut-off frequency of the colored-noise mask signals, which is related to the resonance of the relaxation oscillation frequency of the laser used as the reservoir.

FDG-PET Response Prediction in Pediatric Hodgkin's Lymphoma: Impact of Metabolically Defined Tumor Volumes and Individualized SUV Measurements on the Positive Predictive Value.

PubMed

Hussien, Amr Elsayed M; Furth, Christian; Schönberger, Stefan; Hundsdoerfer, Patrick; Steffen, Ingo G; Amthauer, Holger; Müller, Hans-Wilhelm; Hautzel, Hubertus

2015-01-28

In pediatric Hodgkin's lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in %) were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity 100%; specificity 85.4%; PPV 46.2%; NPV 100%; accuracy, 87.0%) but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV.

Paradigm of pretest risk stratification before coronary computed tomography.

PubMed

Jensen, Jesper Møller; Ovrehus, Kristian A; Nielsen, Lene H; Jensen, Jesper K; Larsen, Henrik M; Nørgaard, Bjarne L

2009-01-01

The optimal method of determining the pretest risk of coronary artery disease as a patient selection tool before coronary multidetector computed tomography (MDCT) is unknown. We investigated the ability of 3 different clinical risk scores to predict the outcome of coronary MDCT. This was a retrospective study of 551 patients consecutively referred for coronary MDCT on a suspicion of coronary artery disease. Diamond-Forrester, Duke, and Morise risk models were used to predict coronary artery stenosis (>50%) as assessed by coronary MDCT. The models were compared by receiver operating characteristic analysis. The distribution of low-, intermediate-, and high-risk persons, respectively, was established and compared for each of the 3 risk models. Overall, all risk prediction models performed equally well. However, the Duke risk model classified the low-risk patients more correctly than did the other models (P < 0.01). In patients without coronary artery calcification (CAC), the predictive value of the Duke risk model was superior to the other risk models (P < 0.05). Currently available risk prediction models seem to perform better in patients without CAC. Between the risk prediction models, there was a significant discrepancy in the distribution of patients at low, intermediate, or high risk (P < 0.01). The 3 risk prediction models perform equally well, although the Duke risk score may have advantages in subsets of patients. The choice of risk prediction model affects the referral pattern to MDCT. Copyright (c) 2009 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

The AIMS65 score compared with the Glasgow-Blatchford score in predicting outcomes in upper GI bleeding.

PubMed

Hyett, Brian H; Abougergi, Marwan S; Charpentier, Joseph P; Kumar, Navin L; Brozovic, Suzana; Claggett, Brian L; Travis, Anne C; Saltzman, John R

2013-04-01

We previously derived and validated the AIMS65 score, a mortality prognostic scale for upper GI bleeding (UGIB). To validate the AIMS65 score in a different patient population and compare it with the Glasgow-Blatchford risk score (GBRS). Retrospective cohort study. Adults with a primary diagnosis of UGIB. inpatient mortality. composite clinical endpoint of inpatient mortality, rebleeding, and endoscopic, radiologic or surgical intervention; blood transfusion; intensive care unit admission; rebleeding; length of stay; timing of endoscopy. The area under the receiver-operating characteristic curve (AUROC) was calculated for each score. Of the 278 study patients, 6.5% died and 35% experienced the composite clinical endpoint. The AIMS65 score was superior in predicting inpatient mortality (AUROC, 0.93 vs 0.68; P < .001), whereas the GBRS was superior in predicting blood transfusions (AUROC, 0.85 vs 0.65; P < .01) The 2 scores were similar in predicting the composite clinical endpoint (AUROC, 0.62 vs 0.68; P = .13) as well as the secondary outcomes. A GBRS of 10 and 12 or more maximized the sum of the sensitivity and specificity for inpatient mortality and rebleeding, respectively. The cutoff was 2 or more for the AIMS65 score for both outcomes. Retrospective, single-center study. The AIMS65 score is superior to the GBRS in predicting inpatient mortality from UGIB, whereas the GBRS is superior for predicting blood transfusion. Both scores are similar in predicting the composite clinical endpoint and other outcomes in clinical care and resource use. Copyright © 2013 American Society for Gastrointestinal Endoscopy. Published by Mosby, Inc. All rights reserved.

A Probabilistic Strategy for Understanding Action Selection

PubMed Central

Kim, Byounghoon; Basso, Michele A.

2010-01-01

Brain regions involved in transforming sensory signals into movement commands are the likely sites where decisions are formed. Once formed, a decision must be read-out from the activity of populations of neurons to produce a choice of action. How this occurs remains unresolved. We recorded from four superior colliculus (SC) neurons simultaneously while monkeys performed a target selection task. We implemented three models to gain insight into the computational principles underlying population coding of action selection. We compared the population vector average (PVA), winner-takes-all (WTA) and a Bayesian model, maximum a posteriori estimate (MAP) to determine which predicted choices most often. The probabilistic model predicted more trials correctly than both the WTA and the PVA. The MAP model predicted 81.88% whereas WTA predicted 71.11% and PVA/OLE predicted the least number of trials at 55.71 and 69.47%. Recovering MAP estimates using simulated, non-uniform priors that correlated with monkeys’ choice performance, improved the accuracy of the model by 2.88%. A dynamic analysis revealed that the MAP estimate evolved over time and the posterior probability of the saccade choice reached a maximum at the time of the saccade. MAP estimates also scaled with choice performance accuracy. Although there was overlap in the prediction abilities of all the models, we conclude that movement choice from populations of neurons may be best understood by considering frameworks based on probability. PMID:20147560

CCTOP: a Consensus Constrained TOPology prediction web server.

PubMed

Dobson, László; Reményi, István; Tusnády, Gábor E

2015-07-01

The Consensus Constrained TOPology prediction (CCTOP; http://cctop.enzim.ttk.mta.hu) server is a web-based application providing transmembrane topology prediction. In addition to utilizing 10 different state-of-the-art topology prediction methods, the CCTOP server incorporates topology information from existing experimental and computational sources available in the PDBTM, TOPDB and TOPDOM databases using the probabilistic framework of hidden Markov model. The server provides the option to precede the topology prediction with signal peptide prediction and transmembrane-globular protein discrimination. The initial result can be recalculated by (de)selecting any of the prediction methods or mapped experiments or by adding user specified constraints. CCTOP showed superior performance to existing approaches. The reliability of each prediction is also calculated, which correlates with the accuracy of the per protein topology prediction. The prediction results and the collected experimental information are visualized on the CCTOP home page and can be downloaded in XML format. Programmable access of the CCTOP server is also available, and an example of client-side script is provided. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Beta Oscillatory Dynamics in the Prefrontal and Superior Temporal Cortices Predict Spatial Working Memory Performance.

PubMed

Proskovec, Amy L; Wiesman, Alex I; Heinrichs-Graham, Elizabeth; Wilson, Tony W

2018-05-31

The oscillatory dynamics serving spatial working memory (SWM), and how such dynamics relate to performance, are poorly understood. To address these topics, the present study recruited 22 healthy adults to perform a SWM task during magnetoencephalography (MEG). The resulting MEG data were transformed into the time-frequency domain, and significant oscillatory responses were imaged using a beamformer. Voxel time series data were extracted from the cluster peaks to quantify the dynamics, while whole-brain partial correlation maps were computed to identify regions where oscillatory strength varied with accuracy on the SWM task. The results indicated transient theta oscillations in spatially distinct subregions of the prefrontal cortices at the onset of encoding and maintenance, which may underlie selection of goal-relevant information. Additionally, strong and persistent decreases in alpha and beta oscillations were observed throughout encoding and maintenance in parietal, temporal, and occipital regions, which could serve sustained attention and maintenance processes during SWM performance. The neuro-behavioral correlations revealed that beta activity within left dorsolateral prefrontal control regions and bilateral superior temporal integration regions was negatively correlated with SWM accuracy. Notably, this is the first study to employ a whole-brain approach to significantly link neural oscillations to behavioral performance in the context of SWM.

What to do on spring break? The role of predicted, on-line, and remembered experience in future choice.

PubMed

Wirtz, Derrick; Kruger, Justin; Napa Scollon, Christie; Diener, Ed

2003-09-01

When individuals choose future activities on the basis of their past experiences, what guides those choices? The present study compared students' predicted, on-line, and remembered spring-break experiences, as well as the influence of these factors on students' desire to take a similar vacation in the future. Predicted and remembered experiences were both more positive-and, paradoxically, more negative-than on-line experiences. Of key importance, path analyses revealed that remembered experience, but neither on-line nor anticipated experience, directly predicted the desire to repeat the experience. These results suggest that although on-line measures may be superior to retrospective measures for approximating objective experience, retrospective measures may be superior for predicting choice.

Predicting protein function and other biomedical characteristics with heterogeneous ensembles

PubMed Central

Whalen, Sean; Pandey, Om Prakash

2015-01-01

Prediction problems in biomedical sciences, including protein function prediction (PFP), are generally quite difficult. This is due in part to incomplete knowledge of the cellular phenomenon of interest, the appropriateness and data quality of the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor for specific problems. In such scenarios, a powerful approach to improving prediction performance is to construct heterogeneous ensemble predictors that combine the output of diverse individual predictors that capture complementary aspects of the problems and/or datasets. In this paper, we demonstrate the potential of such heterogeneous ensembles, derived from stacking and ensemble selection methods, for addressing PFP and other similar biomedical prediction problems. Deeper analysis of these results shows that the superior predictive ability of these methods, especially stacking, can be attributed to their attention to the following aspects of the ensemble learning process: (i) better balance of diversity and performance, (ii) more effective calibration of outputs and (iii) more robust incorporation of additional base predictors. Finally, to make the effective application of heterogeneous ensembles to large complex datasets (big data) feasible, we present DataSink, a distributed ensemble learning framework, and demonstrate its sound scalability using the examined datasets. DataSink is publicly available from https://github.com/shwhalen/datasink. PMID:26342255

A Superior Kirchhoff Method for Aeroacoustic Noise Prediction: The Ffowcs Williams-Hawkings Equation

NASA Technical Reports Server (NTRS)

Brentner, Kenneth S.

1997-01-01

The prediction of aeroacoustic noise is important; all new aircraft must meet noise certification requirements. Local noise standards can be even more stringent. The NASA noise reduction goal is to reduce perceived noise levels by a factor of two in 10 years. The objective of this viewgraph presentation is to demonstrate the superiority of the FW-H approach over the Kirchoff method for aeroacoustics, both analytically and numerically.

ERPs and oscillations during encoding predict retrieval of digit memory in superior mnemonists.

PubMed

Pan, Yafeng; Li, Xianchun; Chen, Xi; Ku, Yixuan; Dong, Yujie; Dou, Zheng; He, Lin; Hu, Yi; Li, Weidong; Zhou, Xiaolin

2017-10-01

Previous studies have consistently demonstrated that superior mnemonists (SMs) outperform normal individuals in domain-specific memory tasks. However, the neural correlates of memory-related processes remain unclear. In the current EEG study, SMs and control participants performed a digit memory task during which their brain activity was recorded. Chinese SMs used a digit-image mnemonic for encoding digits, in which they associated 2-digit groups with images immediately after the presentation of each even-position digit in sequences. Behaviorally, SMs' memory of digit sequences was better than the controls'. During encoding in the study phase, SMs showed an increased right central P2 (150-250ms post onset) and a larger right posterior high-alpha (10-14Hz, 500-1720ms) oscillation on digits at even-positions compared with digits at odd-positions. Both P2 and high-alpha oscillations in the study phase co-varied with performance in the recall phase, but only in SMs, indicating that neural dynamics during encoding could predict successful retrieval of digit memory in SMs. Our findings suggest that representation of a digit sequence in SMs using mnemonics may recruit both the early-stage attention allocation process and the sustained information preservation process. This study provides evidence for the role of dynamic and efficient neural encoding processes in mnemonists. Copyright © 2017. Published by Elsevier Inc.

Differentiating benign from malignant solid breast masses: value of shear wave elastography according to lesion stiffness combined with greyscale ultrasound according to BI-RADS classification.

PubMed

Evans, A; Whelehan, P; Thomson, K; Brauer, K; Jordan, L; Purdie, C; McLean, D; Baker, L; Vinnicombe, S; Thompson, A

2012-07-10

The aim of this study was to assess the performance of shear wave elastography combined with BI-RADS classification of greyscale ultrasound images for benign/malignant differentiation in a large group of patients. One hundred and seventy-five consecutive patients with solid breast masses on routine ultrasonography undergoing percutaneous biopsy had the greyscale findings classified according to the American College of Radiology BI-RADS. The mean elasticity values from four shear wave images were obtained. For mean elasticity vs greyscale BI-RADS, the performance results against histology were sensitivity: 95% vs 95%, specificity: 77% vs 69%, Positive Predictive Value (PPV): 88% vs 84%, Negative Predictive Value (NPV): 90% vs 91%, and accuracy: 89% vs 86% (all P>0.05). The results for the combination (positive result from either modality counted as malignant) were sensitivity 100%, specificity 61%, PPV 82%, NPV 100%, and accuracy 86%. The combination of BI-RADS greyscale and shear wave elastography yielded superior sensitivity to BI-RADS alone (P=0.03) or shear wave alone (P=0.03). The NPV was superior in combination compared with either alone (BI-RADS P=0.01 and shear wave P=0.02). Together, BI-RADS assessment of greyscale ultrasound images and shear wave ultrasound elastography are extremely sensitive for detection of malignancy.

Differentiating benign from malignant solid breast masses: value of shear wave elastography according to lesion stiffness combined with greyscale ultrasound according to BI-RADS classification

PubMed Central

Evans, A; Whelehan, P; Thomson, K; Brauer, K; Jordan, L; Purdie, C; McLean, D; Baker, L; Vinnicombe, S; Thompson, A

2012-01-01

Background: The aim of this study was to assess the performance of shear wave elastography combined with BI-RADS classification of greyscale ultrasound images for benign/malignant differentiation in a large group of patients. Methods: One hundred and seventy-five consecutive patients with solid breast masses on routine ultrasonography undergoing percutaneous biopsy had the greyscale findings classified according to the American College of Radiology BI-RADS. The mean elasticity values from four shear wave images were obtained. Results: For mean elasticity vs greyscale BI-RADS, the performance results against histology were sensitivity: 95% vs 95%, specificity: 77% vs 69%, Positive Predictive Value (PPV): 88% vs 84%, Negative Predictive Value (NPV): 90% vs 91%, and accuracy: 89% vs 86% (all P>0.05). The results for the combination (positive result from either modality counted as malignant) were sensitivity 100%, specificity 61%, PPV 82%, NPV 100%, and accuracy 86%. The combination of BI-RADS greyscale and shear wave elastography yielded superior sensitivity to BI-RADS alone (P=0.03) or shear wave alone (P=0.03). The NPV was superior in combination compared with either alone (BI-RADS P=0.01 and shear wave P=0.02). Conclusion: Together, BI-RADS assessment of greyscale ultrasound images and shear wave ultrasound elastography are extremely sensitive for detection of malignancy. PMID:22691969

Accuracy Quantification of the Loci-CHEM Code for Chamber Wall Heat Transfer in a GO2/GH2 Single Element Model Problem

NASA Technical Reports Server (NTRS)

West, Jeff; Westra, Doug; Lin, Jeff; Tucker, Kevin

2006-01-01

All solutions with Loci-CHEM achieved demonstrated steady state and mesh convergence. Preconditioning had no effect on solution accuracy and typically yields a 3-5times solution speed-up. The SST turbulence model has superior performance, relative to the data in the head end region, for the rise rate and peak heat flux. It was slightly worse than the others in the downstream region where all over-predicted the data by 30-100%.There was systematic mesh refinement in the unstructured volume and structured boundary layer areas produced only minor solution differences. Mesh convergence was achieved. Overall, Loci-CHEM satisfactorily predicts heat flux rise rate and peak heat flux and significantly over predicts the downstream heat flux.

Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring

NASA Astrophysics Data System (ADS)

Zhang, Duo; Lindholm, Geir; Ratnaweera, Harsha

2018-01-01

Combined sewer overflow causes severe water pollution, urban flooding and reduced treatment plant efficiency. Understanding the behavior of CSO structures is vital for urban flooding prevention and overflow control. Neural networks have been extensively applied in water resource related fields. In this study, we collect data from an Internet of Things monitoring CSO structure and build different neural network models for simulating and predicting the water level of the CSO structure. Through a comparison of four different neural networks, namely multilayer perceptron (MLP), wavelet neural network (WNN), long short-term memory (LSTM) and gated recurrent unit (GRU), the LSTM and GRU present superior capabilities for multi-step-ahead time series prediction. Furthermore, GRU achieves prediction performances similar to LSTM with a quicker learning curve.

Machine learning approaches for estimation of prediction interval for the model output.

PubMed

Shrestha, Durga L; Solomatine, Dimitri P

2006-03-01

A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well.

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

PubMed Central

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J.; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T.; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J.

2017-01-01

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival. PMID:28401902

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound.

PubMed

Tadayyon, Hadi; Sannachi, Lakshmanan; Gangeh, Mehrdad J; Kim, Christina; Ghandi, Sonal; Trudeau, Maureen; Pritchard, Kathleen; Tran, William T; Slodkowska, Elzbieta; Sadeghi-Naini, Ali; Czarnota, Gregory J

2017-04-12

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

Modeling and simulation to support dose selection and clinical development of SC-75416, a selective COX-2 inhibitor for the treatment of acute and chronic pain.

PubMed

Kowalski, K G; Olson, S; Remmers, A E; Hutmacher, M M

2008-06-01

Pharmacokinetic/pharmacodynamic (PK/PD) models were developed and clinical trial simulations were conducted to recommend a study design to test the hypothesis that a dose of SC-75416, a selective cyclooxygenase-2 inhibitor, can be identified that achieves superior pain relief (PR) compared to 400 mg ibuprofen in a post-oral surgery pain model. PK/PD models were developed for SC-75416, rofecoxib, valdecoxib, and ibuprofen relating plasma concentrations to PR scores using a nonlinear logistic-normal model. Clinical trial simulations conducted using these models suggested that 360 mg SC-75416 could achieve superior PR compared to 400 mg ibuprofen. A placebo- and positive-controlled parallel-group post-oral surgery pain study was conducted evaluating placebo, 60, 180, and 360 mg SC-75416 oral solution, and 400 mg ibuprofen. The study results confirmed the hypothesis that 360 mg SC-75416 achieved superior PR relative to 400 mg ibuprofen (DeltaTOTPAR6=3.3, P<0.05) and demonstrated the predictive performance of the PK/PD models.

Gray Matter Features of Reading Disability: A Combined Meta-Analytic and Direct Analysis Approach1234

PubMed Central

Berninger, Virginia W.; Gebregziabher, Mulugeta; Tsu, Loretta

2016-01-01

Abstract Meta-analysis of voxel-based morphometry dyslexia studies and direct analysis of 293 reading disability and control cases from six different research sites were performed to characterize defining gray matter features of reading disability. These analyses demonstrated consistently lower gray matter volume in left posterior superior temporal sulcus/middle temporal gyrus regions and left orbitofrontal gyrus/pars orbitalis regions. Gray matter volume within both of these regions significantly predicted individual variation in reading comprehension after correcting for multiple comparisons. These regional gray matter differences were observed across published studies and in the multisite dataset after controlling for potential age and gender effects, and despite increased anatomical variance in the reading disability group, but were not significant after controlling for total gray matter volume. Thus, the orbitofrontal and posterior superior temporal sulcus gray matter findings are relatively reliable effects that appear to be dependent on cases with low total gray matter volume. The results are considered in the context of genetics studies linking orbitofrontal and superior temporal sulcus regions to alleles that confer risk for reading disability. PMID:26835509

Transient Elastography vs. Aspartate Aminotransferase to Platelet Ratio Index in Hepatitis C: A Meta-Analysis.

PubMed

Mattos, A Z; Mattos, A A

Many different non-invasive methods have been studied with the purpose of staging liver fibrosis. The objective of this study was verifying if transient elastography is superior to aspartate aminotransferase to platelet ratio index for staging fibrosis in patients with chronic hepatitis C. A systematic review with meta-analysis of studies which evaluated both non-invasive tests and used biopsy as the reference standard was performed. A random-effects model was used, anticipating heterogeneity among studies. Diagnostic odds ratio was the main effect measure, and summary receiver operating characteristic curves were created. A sensitivity analysis was planned, in which the meta-analysis would be repeated excluding each study at a time. Eight studies were included in the meta-analysis. Regarding the prediction of significant fibrosis, transient elastography and aspartate aminotransferase to platelet ratio index had diagnostic odds ratios of 11.70 (95% confidence interval = 7.13-19.21) and 8.56 (95% confidence interval = 4.90-14.94) respectively. Concerning the prediction of cirrhosis, transient elastography and aspartate aminotransferase to platelet ratio index had diagnostic odds ratios of 66.49 (95% confidence interval = 23.71-186.48) and 7.47 (95% confidence interval = 4.88-11.43) respectively. In conclusion, there was no evidence of significant superiority of transient elastography over aspartate aminotransferase to platelet ratio index regarding the prediction of significant fibrosis, but the former proved to be better than the latter concerning prediction of cirrhosis.

Cross-modal activation of auditory regions during visuo-spatial working memory in early deafness.

PubMed

Ding, Hao; Qin, Wen; Liang, Meng; Ming, Dong; Wan, Baikun; Li, Qiang; Yu, Chunshui

2015-09-01

Early deafness can reshape deprived auditory regions to enable the processing of signals from the remaining intact sensory modalities. Cross-modal activation has been observed in auditory regions during non-auditory tasks in early deaf subjects. In hearing subjects, visual working memory can evoke activation of the visual cortex, which further contributes to behavioural performance. In early deaf subjects, however, whether and how auditory regions participate in visual working memory remains unclear. We hypothesized that auditory regions may be involved in visual working memory processing and activation of auditory regions may contribute to the superior behavioural performance of early deaf subjects. In this study, 41 early deaf subjects (22 females and 19 males, age range: 20-26 years, age of onset of deafness < 2 years) and 40 age- and gender-matched hearing controls underwent functional magnetic resonance imaging during a visuo-spatial delayed recognition task that consisted of encoding, maintenance and recognition stages. The early deaf subjects exhibited faster reaction times on the spatial working memory task than did the hearing controls. Compared with hearing controls, deaf subjects exhibited increased activation in the superior temporal gyrus bilaterally during the recognition stage. This increased activation amplitude predicted faster and more accurate working memory performance in deaf subjects. Deaf subjects also had increased activation in the superior temporal gyrus bilaterally during the maintenance stage and in the right superior temporal gyrus during the encoding stage. These increased activation amplitude also predicted faster reaction times on the spatial working memory task in deaf subjects. These findings suggest that cross-modal plasticity occurs in auditory association areas in early deaf subjects. These areas are involved in visuo-spatial working memory. Furthermore, amplitudes of cross-modal activation during the maintenance stage were positively correlated with the age of onset of hearing aid use and were negatively correlated with the percentage of lifetime hearing aid use in deaf subjects. These findings suggest that earlier and longer hearing aid use may inhibit cross-modal reorganization in early deaf subjects. Granger causality analysis revealed that, compared to the hearing controls, the deaf subjects had an enhanced net causal flow from the frontal eye field to the superior temporal gyrus. These findings indicate that a top-down mechanism may better account for the cross-modal activation of auditory regions in early deaf subjects.See MacSweeney and Cardin (doi:10/1093/awv197) for a scientific commentary on this article. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Novel and conventional serum biomarkers predicting acute kidney injury in adult cardiac surgery--a prospective cohort study.

PubMed

Haase-Fielitz, Anja; Bellomo, Rinaldo; Devarajan, Prasad; Story, David; Matalanis, George; Dragun, Duska; Haase, Michael

2009-02-01

To compare the value of novel with conventional serum biomarkers in the prediction of acute kidney injury (AKI) in adult cardiac surgical patients according to preoperative renal function. Single-center, prospective observational study. Tertiary hospital. One hundred adult cardiac surgical patients. We measured concentrations of plasma neutrophil gelatinase-associated lipocalin (NGAL), and serum cystatin C, and creatinine and urea at baseline, on arrival in the intensive care unit (ICU) and at 24 hours postoperatively. We assessed such biomarkers in relation to the development of AKI (>50% increase in creatinine from baseline) and to a composite end point (need for renal replacement therapy and in-hospital mortality). We defined an area under the receiver operating characteristic curve of 0.60-0.69 as poor, 0.70-0.79 as fair, 0.80-0.89 as good, and 0.90-1.00 as excellent in terms of predictive value. On arrival in ICU, plasma NGAL and serum cystatin C were of good predictive value, but creatinine and urea were of poor predictive value. After exclusion of patients with preoperative renal impairment (estimated glomerular filtration rate <60 mL/min), the predictive performance for AKI of all renal biomarkers on arrival in ICU remained unchanged except for cystatin C, which was of fair value in such patients. At 24 hours postoperatively, all renal biomarkers were of good predictive value. On arrival in ICU, novel biomarkers were superior to conventional biomarkers (p < 0.05). Plasma NGAL (p = 0.015) and serum cystatin C (p = 0.007) were independent predictors of AKI and of excellent value in the prediction of the composite end point. Early postoperative measurement of plasma NGAL was of good value in identifying patients who developed AKI after adult cardiac surgery. Plasma NGAL and serum cystatin C were superior to conventional biomarkers in the prediction of AKI and were also of prognostic value in this setting.

A study on multiresolution lossless video coding using inter/intra frame adaptive prediction

NASA Astrophysics Data System (ADS)

Nakachi, Takayuki; Sawabe, Tomoko; Fujii, Tetsuro

2003-06-01

Lossless video coding is required in the fields of archiving and editing digital cinema or digital broadcasting contents. This paper combines a discrete wavelet transform and adaptive inter/intra-frame prediction in the wavelet transform domain to create multiresolution lossless video coding. The multiresolution structure offered by the wavelet transform facilitates interchange among several video source formats such as Super High Definition (SHD) images, HDTV, SDTV, and mobile applications. Adaptive inter/intra-frame prediction is an extension of JPEG-LS, a state-of-the-art lossless still image compression standard. Based on the image statistics of the wavelet transform domains in successive frames, inter/intra frame adaptive prediction is applied to the appropriate wavelet transform domain. This adaptation offers superior compression performance. This is achieved with low computational cost and no increase in additional information. Experiments on digital cinema test sequences confirm the effectiveness of the proposed algorithm.

A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

NASA Astrophysics Data System (ADS)

Shamshirband, Shahaboddin; Mohammadi, Kasra; Tong, Chong Wen; Zamani, Mazdak; Motamedi, Shervin; Ch, Sudheer

2016-07-01

In this study, a hybrid support vector machine-firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination ( R 2) are 3.3252 %, 0.1859 kWh/m2, 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

Identifying factors that predict the choice and success rate of radial artery catheterisation in contemporary real world cardiology practice: a sub-analysis of the PREVAIL study data.

PubMed

Pristipino, Christian; Roncella, Adriana; Trani, Carlo; Nazzaro, Marco S; Berni, Andrea; Di Sciascio, Germano; Sciahbasi, Alessandro; Musarò, Salvatore Donato; Mazzarotto, Pietro; Gioffrè, Gaetano; Speciale, Giulio

2010-06-01

To assess: the reasons behind an operator choosing to perform radial artery catheterisation (RAC) as against femoral arterial catheterisation, and to explore why RAC may fail in the real world. A pre-determined analysis of PREVAIL study database was performed. Relevant data were collected in a prospective, observational survey of 1,052 consecutive patients undergoing invasive cardiovascular procedures at nine Italian hospitals over a one month observation period. By multivariate analysis, the independent predictors of RAC choice were having the procedure performed: (1) at a high procedural volume centre; and (2) by an operator who performs a high volume of radial procedures; clinical variables played no statistically significant role. RAC failure was predicted independently by (1) a lower operator propensity to use RAC; and (2) the presence of obstructive peripheral artery disease. A 10-fold lower rate of RAC failure was observed among operators who perform RAC for > 85% of their personal caseload than among those who use RAC < 25% of the time (3.8% vs. 33.0%, respectively); by receiver operator characteristic (ROC) analysis, no threshold value for operator RAC volume predicted RAC failure. A routine RAC in all-comers is superior to a selective strategy in terms of feasibility and success rate.

White Matter Tract Integrity Predicts Visual Search Performance in Young and Older Adults

PubMed Central

Bennett, Ilana J.; Motes, Michael A.; Rao, Neena K.; Rypma, Bart

2011-01-01

Functional imaging research has identified fronto-parietal attention networks involved in visual search, with mixed evidence regarding whether different networks are engaged when the search target differs from distracters by a single (elementary) versus multiple (conjunction) features. Neural correlates of visual search, and their potential dissociation, were examined here using integrity of white matter connecting the fronto-parietal networks. The effect of aging on these brain-behavior relationships was also of interest. Younger and older adults performed a visual search task and underwent diffusion tensor imaging (DTI) to reconstruct two fronto-parietal (superior and inferior longitudinal fasciculus, SLF and ILF) and two midline (genu, splenium) white matter tracts. As expected, results revealed age-related declines in conjunction, but not elementary, search performance; and in ILF and genu tract integrity. Importantly, integrity of the SLF, ILF, and genu tracts predicted search performance (conjunction and elementary), with no significant age group differences in these relationships. Thus, integrity of white matter tracts connecting fronto-parietal attention networks contributes to search performance in younger and older adults. PMID:21402431

Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data

NASA Astrophysics Data System (ADS)

Jothiprakash, V.; Magar, R. B.

2012-07-01

SummaryIn this study, artificial intelligent (AI) techniques such as artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS) and Linear genetic programming (LGP) are used to predict daily and hourly multi-time-step ahead intermittent reservoir inflow. To illustrate the applicability of AI techniques, intermittent Koyna river watershed in Maharashtra, India is chosen as a case study. Based on the observed daily and hourly rainfall and reservoir inflow various types of time-series, cause-effect and combined models are developed with lumped and distributed input data. Further, the model performance was evaluated using various performance criteria. From the results, it is found that the performances of LGP models are found to be superior to ANN and ANFIS models especially in predicting the peak inflows for both daily and hourly time-step. A detailed comparison of the overall performance indicated that the combined input model (combination of rainfall and inflow) performed better in both lumped and distributed input data modelling. It was observed that the lumped input data models performed slightly better because; apart from reducing the noise in the data, the better techniques and their training approach, appropriate selection of network architecture, required inputs, and also training-testing ratios of the data set. The slight poor performance of distributed data is due to large variations and lesser number of observed values.

Parallel protein secondary structure prediction based on neural networks.

PubMed

Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

2004-01-01

Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

Simulation of quantum dynamics based on the quantum stochastic differential equation.

PubMed

Li, Ming

2013-01-01

The quantum stochastic differential equation derived from the Lindblad form quantum master equation is investigated. The general formulation in terms of environment operators representing the quantum state diffusion is given. The numerical simulation algorithm of stochastic process of direct photodetection of a driven two-level system for the predictions of the dynamical behavior is proposed. The effectiveness and superiority of the algorithm are verified by the performance analysis of the accuracy and the computational cost in comparison with the classical Runge-Kutta algorithm.

Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature

NASA Astrophysics Data System (ADS)

Zounemat-Kermani, Mohammad

2012-08-01

In this study, the ability of two models of multi linear regression (MLR) and Levenberg-Marquardt (LM) feed-forward neural network was examined to estimate the hourly dew point temperature. Dew point temperature is the temperature at which water vapor in the air condenses into liquid. This temperature can be useful in estimating meteorological variables such as fog, rain, snow, dew, and evapotranspiration and in investigating agronomical issues as stomatal closure in plants. The availability of hourly records of climatic data (air temperature, relative humidity and pressure) which could be used to predict dew point temperature initiated the practice of modeling. Additionally, the wind vector (wind speed magnitude and direction) and conceptual input of weather condition were employed as other input variables. The three quantitative standard statistical performance evaluation measures, i.e. the root mean squared error, mean absolute error, and absolute logarithmic Nash-Sutcliffe efficiency coefficient ( {| {{{Log}}({{NS}})} |} ) were employed to evaluate the performances of the developed models. The results showed that applying wind vector and weather condition as input vectors along with meteorological variables could slightly increase the ANN and MLR predictive accuracy. The results also revealed that LM-NN was superior to MLR model and the best performance was obtained by considering all potential input variables in terms of different evaluation criteria.

SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

PubMed

Yang, Xiaoxia; Wang, Jia; Sun, Jun; Liu, Rong

2015-01-01

Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

Evaluation of PSA-age volume score in predicting prostate cancer in Chinese populationArticle Subject.

PubMed

Wu, Yi-Shuo; Wu, Xiao-Bo; Zhang, Ning; Jiang, Guang-Liang; Yu, Yang; Tong, Shi-Jun; Jiang, Hao-Wen; Mao, Shan-Hua; Na, Rong; Ding, Qiang

2018-02-06

This study was performed to evaluate prostate-specific antigen-age volume (PSA-AV) scores in predicting prostate cancer (PCa) in a Chinese biopsy population. A total of 2355 men who underwent initial prostate biopsy from January 2006 to November 2015 in Huashan Hospital were recruited in the current study. The PSA-AV scores were calculated and assessed together with PSA and PSA density (PSAD) retrospectively. Among 2133 patients included in the analysis, 947 (44.4%) were diagnosed with PCa. The mean age, PSA, and positive rates of digital rectal examination result and transrectal ultrasound result were statistically higher in men diagnosed with PCa (all P < 0.05). The values of area under the receiver operating characteristic curves (AUCs) of PSAD and PSA-AV were 0.864 and 0.851, respectively, in predicting PCa in the entire population, both performed better than PSA (AUC = 0.805; P < 0.05). The superiority of PSAD and PSA-AV was more obvious in subgroup with PSA ranging from 2.0 ng ml-1 to 20.0 ng ml-1. A PSA-AV score of 400 had a sensitivity and specificity of 93.7% and 40.0%, respectively. In conclusion, the PSA-AV score performed equally with PSAD and was better than PSA in predicting PCa. This indicated that PSA-AV score could be a useful tool for predicting PCa in Chinese population.

Resting state functional connectivity of the anterior striatum and prefrontal cortex predicts reading performance in school-age children.

PubMed

Alcauter, Sarael; García-Mondragón, Liliana; Gracia-Tabuenca, Zeus; Moreno, Martha B; Ortiz, Juan J; Barrios, Fernando A

2017-11-01

The current study investigated the neural basis of reading performance in 60 school-age Spanish-speaking children, aged 6 to 9years. By using a data-driven approach and an automated matching procedure, we identified a left-lateralized resting state network that included typical language regions (Wernicke's and Broca's regions), prefrontal cortex, pre- and post-central gyri, superior and middle temporal gyri, cerebellum, and subcortical regions, and explored its relevance for reading performance (accuracy, comprehension and speed). Functional connectivity of the left frontal and temporal cortices and subcortical regions predicted reading speed. These results extend previous findings on the relationship between functional connectivity and reading competence in children, providing new evidence about such relationships in previously unexplored regions in the resting brain, including the left caudate, putamen and thalamus. This work highlights the relevance of a broad network, functionally synchronized in the resting state, for the acquisition and perfecting of reading abilities in young children. Copyright © 2017 Elsevier Inc. All rights reserved.

Fine-pore aeration diffusers: accelerated membrane ageing studies.

PubMed

Kaliman, An; Rosso, Diego; Leu, Shao-Yuan; Stenstrom, Michael K

2008-01-01

Polymeric membranes are widely used in aeration systems for biological treatment. These membranes may degrade over time and are sensitive to fouling and scaling. Membrane degradation is reflected in a decline in operating performance and higher headloss, resulting in increased energy costs. Mechanical property parameters, such as membrane hardness, Young's modulus, and orifice creep, were used to characterize the performance of membranes over time in operation and to predict their failure. Used diffusers from municipal wastewater treatment plants were collected and tested for efficiency and headloss, and then dissected to facilitate measurements of Young's modulus, hardness, and orifice creep. Higher degree of membrane fouling corresponded consistently with larger orifice creep. A lab-scale membrane ageing simulation was performed with polyurethane and four different ethylene-propylene-diene (EPDM) membrane diffusers by subjecting them to chemical ageing cycles and periodic testing. The results confirmed full-scale plant results and showed the superiority of orifice creep over Young's modulus and hardness in predicting diffuser deterioration.

The effects of voice and manual control mode on dual task performance

NASA Technical Reports Server (NTRS)

Wickens, C. D.; Zenyuh, J.; Culp, V.; Marshak, W.

1986-01-01

Two fundamental principles of human performance, compatibility and resource competition, are combined with two structural dichotomies in the human information processing system, manual versus voice output, and left versus right cerebral hemisphere, in order to predict the optimum combination of voice and manual control with either hand, for time-sharing performance of a dicrete and continuous task. Eight right handed male subjected performed a discrete first-order tracking task, time-shared with an auditorily presented Sternberg Memory Search Task. Each task could be controlled by voice, or by the left or right hand, in all possible combinations except for a dual voice mode. When performance was analyzed in terms of a dual-task decrement from single task control conditions, the following variables influenced time-sharing efficiency in diminishing order of magnitude, (1) the modality of control, (discrete manual control of tracking was superior to discrete voice control of tracking and the converse was true with the memory search task), (2) response competition, (performance was degraded when both tasks were responded manually), (3) hemispheric competition, (performance degraded whenever two tasks were controlled by the left hemisphere) (i.e., voice or right handed control). The results confirm the value of predictive models invoice control implementation.

Vocational Interests and Performance: A Quantitative Summary of Over 60 Years of Research.

PubMed

Nye, Christopher D; Su, Rong; Rounds, James; Drasgow, Fritz

2012-07-01

Despite early claims that vocational interests could be used to distinguish successful workers and superior students from their peers, interest measures are generally ignored in the employee selection literature. Nevertheless, theoretical descriptions of vocational interests from vocational and educational psychology have proposed that interest constructs should be related to performance and persistence in work and academic settings. Moreover, on the basis of Holland's (1959, 1997) theoretical predictions, congruence indices, which quantify the degree of similarity or person-environment fit between individuals and their occupations, should be more strongly related to performance than interest scores alone. Using a comprehensive review of the interest literature that spans more than 60 years of research, a meta-analysis was conducted to examine the veracity of these claims. A literature search identified 60 studies and approximately 568 correlations that addressed the relationship between interests and performance. Results showed that interests are indeed related to performance and persistence in work and academic contexts. In addition, the correlations between congruence indices and performance were stronger than for interest scores alone. Thus, consistent with interest theory, the fit between individuals and their environment was more predictive of performance than interest alone. © The Author(s) 2012.

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

PubMed

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-12-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Genomic Prediction of Single Crosses in the Early Stages of a Maize Hybrid Breeding Pipeline.

PubMed

Kadam, Dnyaneshwar C; Potts, Sarah M; Bohn, Martin O; Lipka, Alexander E; Lorenz, Aaron J

2016-09-19

Prediction of single-cross performance has been a major goal of plant breeders since the beginning of hybrid breeding. Recently, genomic prediction has shown to be a promising approach, but only limited studies have examined the accuracy of predicting single-cross performance. Moreover, no studies have examined the potential of predicting single crosses among random inbreds derived from a series of biparental families, which resembles the structure of germplasm comprising the initial stages of a hybrid maize breeding pipeline. The main objectives of this study were to evaluate the potential of genomic prediction for identifying superior single crosses early in the hybrid breeding pipeline and optimize its application. To accomplish these objectives, we designed and analyzed a novel population of single crosses representing the Iowa Stiff Stalk Synthetic/Non-Stiff Stalk heterotic pattern commonly used in the development of North American commercial maize hybrids. The performance of single crosses was predicted using parental combining ability and covariance among single crosses. Prediction accuracies were estimated using cross-validation and ranged from 0.28 to 0.77 for grain yield, 0.53 to 0.91 for plant height, and 0.49 to 0.94 for staygreen, depending on the number of tested parents of the single cross and genomic prediction method used. The genomic estimated general and specific combining abilities showed an advantage over genomic covariances among single crosses when one or both parents of the single cross were untested. Overall, our results suggest that genomic prediction of single crosses in the early stages of a hybrid breeding pipeline holds great potential to re-design hybrid breeding and increase its efficiency. Copyright © 2016 Author et al.

Rubisco catalytic properties of wild and domesticated relatives provide scope for improving wheat photosynthesis

PubMed Central

Prins, Anneke; Orr, Douglas J.; Andralojc, P. John; Reynolds, Matthew P.; Carmo-Silva, Elizabete; Parry, Martin A. J.

2016-01-01

Rubisco is a major target for improving crop photosynthesis and yield, yet natural diversity in catalytic properties of this enzyme is poorly understood. Rubisco from 25 genotypes of the Triticeae tribe, including wild relatives of bread wheat (Triticum aestivum), were surveyed to identify superior enzymes for improving photosynthesis in this crop. In vitro Rubisco carboxylation velocity (V c), Michaelis–Menten constants for CO2 (K c) and O2 (K o) and specificity factor (S c/o) were measured at 25 and 35 °C. V c and K c correlated positively, while V c and S c/o were inversely related. Rubisco large subunit genes (rbcL) were sequenced, and predicted corresponding amino acid differences analysed in relation to the corresponding catalytic properties. The effect of replacing native wheat Rubisco with counterparts from closely related species was analysed by modelling the response of photosynthesis to varying CO2 concentrations. The model predicted that two Rubisco enzymes would increase photosynthetic performance at 25 °C while only one of these also increased photosynthesis at 35 °C. Thus, under otherwise identical conditions, catalytic variation in the Rubiscos analysed is predicted to improve photosynthetic rates at physiological CO2 concentrations. Naturally occurring Rubiscos with superior properties amongst the Triticeae tribe can be exploited to improve wheat photosynthesis and crop productivity. PMID:26798025

Ion exchange of several radionuclides on the hydrous crystalline silicotitanate, UOP IONSIV IE-911

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

Huckman, M.E.; Latheef, I.M.; Anthony, R.G.

1999-04-01

The crystalline silicotitanate, UOP IONSIV IE-911, is a proven material for removing radionuclides from a wide variety of waste streams. It is superior for removing several radionuclides from the highly alkaline solutions typical of DOE wastes. This laboratory previously developed an equilibrium model applicable to complex solutions for IE-910 (the power form of the granular IE-911), and more recently, the authors have developed several single component ion-exchange kinetic models for predicting column breakthrough curves and batch reactor concentration histories. In this paper, the authors model ion-exchange column performance using effective diffusivities determined from batch kinetic experiments. This technique is preferablemore » because the batch experiments are easier, faster, and cheaper to perform than column experiments. They also extend these ideas to multicomponent systems. Finally, they evaluate the ability of the equilibrium model to predict data for IE-911.« less

Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection.

PubMed

Lang, Congyan; Feng, Jiashi; Feng, Songhe; Wang, Jingdong; Yan, Shuicheng

2016-06-01

Saliency detection is an important procedure for machines to understand visual world as humans do. In this paper, we consider a specific saliency detection problem of predicting human eye fixations when they freely view natural images, and propose a novel dual low-rank pursuit (DLRP) method. DLRP learns saliency-aware feature transformations by utilizing available supervision information and constructs discriminative bases for effectively detecting human fixation points under the popular low-rank and sparsity-pursuit framework. Benefiting from the embedded high-level information in the supervised learning process, DLRP is able to predict fixations accurately without performing the expensive object segmentation as in the previous works. Comprehensive experiments clearly show the superiority of the proposed DLRP method over the established state-of-the-art methods. We also empirically demonstrate that DLRP provides stronger generalization performance across different data sets and inherits the advantages of both the bottom-up- and top-down-based saliency detection methods.

Event-related potential effects of superior action anticipation in professional badminton players.

PubMed

Jin, Hua; Xu, Guiping; Zhang, John X; Gao, Hongwei; Ye, Zuoer; Wang, Pin; Lin, Huiyan; Mo, Lei; Lin, Chong-De

2011-04-04

The ability to predict the trajectory of a ball based on the opponent's body kinematics has been shown to be critical to high-performing athletes in many sports. However, little is known about the neural correlates underlying such superior ability in action anticipation. The present event-related potential study compared brain responses from professional badminton players and non-player controls when they watched video clips of badminton games and predicted a ball's landing position. Replicating literature findings, the players made significantly more accurate judgments than the controls and showed better action anticipation. Correspondingly, they showed enlarged amplitudes of two ERP components, a P300 peaking around 350ms post-stimulus with a parietal scalp distribution and a P2 peaking around 250ms with a posterior-occipital distribution. The P300 effect was interpreted to reflect primed access and/or directing of attention to game-related memory representations in the players facilitating their online judgment of related actions. The P2 effect was suggested to reflect some generic learning effects. The results identify clear neural responses that differentiate between different levels of action anticipation associated with sports expertise. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

RRCRank: a fusion method using rank strategy for residue-residue contact prediction.

PubMed

Jing, Xiaoyang; Dong, Qiwen; Lu, Ruqian

2017-09-02

In structural biology area, protein residue-residue contacts play a crucial role in protein structure prediction. Some researchers have found that the predicted residue-residue contacts could effectively constrain the conformational search space, which is significant for de novo protein structure prediction. In the last few decades, related researchers have developed various methods to predict residue-residue contacts, especially, significant performance has been achieved by using fusion methods in recent years. In this work, a novel fusion method based on rank strategy has been proposed to predict contacts. Unlike the traditional regression or classification strategies, the contact prediction task is regarded as a ranking task. First, two kinds of features are extracted from correlated mutations methods and ensemble machine-learning classifiers, and then the proposed method uses the learning-to-rank algorithm to predict contact probability of each residue pair. First, we perform two benchmark tests for the proposed fusion method (RRCRank) on CASP11 dataset and CASP12 dataset respectively. The test results show that the RRCRank method outperforms other well-developed methods, especially for medium and short range contacts. Second, in order to verify the superiority of ranking strategy, we predict contacts by using the traditional regression and classification strategies based on the same features as ranking strategy. Compared with these two traditional strategies, the proposed ranking strategy shows better performance for three contact types, in particular for long range contacts. Third, the proposed RRCRank has been compared with several state-of-the-art methods in CASP11 and CASP12. The results show that the RRCRank could achieve comparable prediction precisions and is better than three methods in most assessment metrics. The learning-to-rank algorithm is introduced to develop a novel rank-based method for the residue-residue contact prediction of proteins, which achieves state-of-the-art performance based on the extensive assessment.

Chaotic time series prediction for prenatal exposure to polychlorinated biphenyls in umbilical cord blood using the least squares SEATR model

NASA Astrophysics Data System (ADS)

Xu, Xijin; Tang, Qian; Xia, Haiyue; Zhang, Yuling; Li, Weiqiu; Huo, Xia

2016-04-01

Chaotic time series prediction based on nonlinear systems showed a superior performance in prediction field. We studied prenatal exposure to polychlorinated biphenyls (PCBs) by chaotic time series prediction using the least squares self-exciting threshold autoregressive (SEATR) model in umbilical cord blood in an electronic waste (e-waste) contaminated area. The specific prediction steps basing on the proposal methods for prenatal PCB exposure were put forward, and the proposed scheme’s validity was further verified by numerical simulation experiments. Experiment results show: 1) seven kinds of PCB congeners negatively correlate with five different indices for birth status: newborn weight, height, gestational age, Apgar score and anogenital distance; 2) prenatal PCB exposed group at greater risks compared to the reference group; 3) PCBs increasingly accumulated with time in newborns; and 4) the possibility of newborns suffering from related diseases in the future was greater. The desirable numerical simulation experiments results demonstrated the feasibility of applying mathematical model in the environmental toxicology field.

Chaotic time series prediction for prenatal exposure to polychlorinated biphenyls in umbilical cord blood using the least squares SEATR model

PubMed Central

Xu, Xijin; Tang, Qian; Xia, Haiyue; Zhang, Yuling; Li, Weiqiu; Huo, Xia

2016-01-01

Chaotic time series prediction based on nonlinear systems showed a superior performance in prediction field. We studied prenatal exposure to polychlorinated biphenyls (PCBs) by chaotic time series prediction using the least squares self-exciting threshold autoregressive (SEATR) model in umbilical cord blood in an electronic waste (e-waste) contaminated area. The specific prediction steps basing on the proposal methods for prenatal PCB exposure were put forward, and the proposed scheme’s validity was further verified by numerical simulation experiments. Experiment results show: 1) seven kinds of PCB congeners negatively correlate with five different indices for birth status: newborn weight, height, gestational age, Apgar score and anogenital distance; 2) prenatal PCB exposed group at greater risks compared to the reference group; 3) PCBs increasingly accumulated with time in newborns; and 4) the possibility of newborns suffering from related diseases in the future was greater. The desirable numerical simulation experiments results demonstrated the feasibility of applying mathematical model in the environmental toxicology field. PMID:27118260

Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control.

PubMed

Aissa, Oualid; Moulahoum, Samir; Colak, Ilhami; Babes, Badreddine; Kabache, Nadir

2017-10-12

This paper discusses the use of the concept of classical and predictive direct power control for shunt active power filter function. These strategies are used to improve the active power filter performance by compensation of the reactive power and the elimination of the harmonic currents drawn by non-linear loads. A theoretical analysis followed by a simulation using MATLAB/Simulink software for the studied techniques has been established. Moreover, two test benches have been carried out using the dSPACE card 1104 for the classic and predictive DPC control to evaluate the studied methods in real time. Obtained results are presented and compared in this paper to confirm the superiority of the predictive technique. To overcome the pollution problems caused by the consumption of fossil fuels, renewable energies are the alternatives recommended to ensure green energy. In the same context, the tested predictive filter can easily be supplied by a renewable energy source that will give its impact to enhance the power quality.

Facility-level outcome performance measures for nursing homes.

PubMed

Porell, F; Caro, F G

1998-12-01

Risk-adjusted nursing home performance scores were developed for four health outcomes and five quality indicators from resident-level longitudinal case-mix reimbursement data for Medicaid residents of more than 500 nursing homes in Massachusetts. Facility performance was measured by comparing actual resident outcomes with expected outcomes derived from quarterly predictions of resident-level econometric models over a 3-year period (1991-1994). Performance measures were tightly distributed among facilities in the state. The intercorrelations among the nine outcome performance measures were relatively low and not uniformly positive. Performance measures were not highly associated with various structural facility attributes. For most outcomes, longitudinal analyses revealed only modest correlations between a facility's performance score from one time period to the next. Relatively few facilities exhibited consistent superior or inferior performance over time. The findings have implications toward the practical use of facility outcome performance measures for quality assurance and reimbursement purposes in the near future.

Inter-comparison of time series models of lake levels predicted by several modeling strategies

NASA Astrophysics Data System (ADS)

Khatibi, R.; Ghorbani, M. A.; Naghipour, L.; Jothiprakash, V.; Fathima, T. A.; Fazelifard, M. H.

2014-04-01

Five modeling strategies are employed to analyze water level time series of six lakes with different physical characteristics such as shape, size, altitude and range of variations. The models comprise chaos theory, Auto-Regressive Integrated Moving Average (ARIMA) - treated for seasonality and hence SARIMA, Artificial Neural Networks (ANN), Gene Expression Programming (GEP) and Multiple Linear Regression (MLR). Each is formulated on a different premise with different underlying assumptions. Chaos theory is elaborated in a greater detail as it is customary to identify the existence of chaotic signals by a number of techniques (e.g. average mutual information and false nearest neighbors) and future values are predicted using the Nonlinear Local Prediction (NLP) technique. This paper takes a critical view of past inter-comparison studies seeking a superior performance, against which it is reported that (i) the performances of all five modeling strategies vary from good to poor, hampering the recommendation of a clear-cut predictive model; (ii) the performances of the datasets of two cases are consistently better with all five modeling strategies; (iii) in other cases, their performances are poor but the results can still be fit-for-purpose; (iv) the simultaneous good performances of NLP and SARIMA pull their underlying assumptions to different ends, which cannot be reconciled. A number of arguments are presented including the culture of pluralism, according to which the various modeling strategies facilitate an insight into the data from different vantages.

A Hierarchical Model Predictive Tracking Control for Independent Four-Wheel Driving/Steering Vehicles with Coaxial Steering Mechanism

NASA Astrophysics Data System (ADS)

Itoh, Masato; Hagimori, Yuki; Nonaka, Kenichiro; Sekiguchi, Kazuma

2016-09-01

In this study, we apply a hierarchical model predictive control to omni-directional mobile vehicle, and improve the tracking performance. We deal with an independent four-wheel driving/steering vehicle (IFWDS) equipped with four coaxial steering mechanisms (CSM). The coaxial steering mechanism is a special one composed of two steering joints on the same axis. In our previous study with respect to IFWDS with ideal steering, we proposed a model predictive tracking control. However, this method did not consider constraints of the coaxial steering mechanism which causes delay of steering. We also proposed a model predictive steering control considering constraints of this mechanism. In this study, we propose a hierarchical system combining above two control methods for IFWDS. An upper controller, which deals with vehicle kinematics, runs a model predictive tracking control, and a lower controller, which considers constraints of coaxial steering mechanism, runs a model predictive steering control which tracks the predicted steering angle optimized an upper controller. We verify the superiority of this method by comparing this method with the previous method.

Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya

2013-03-01

This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.

Comparative evaluation of the impact of GRAPES and MM5 meteorology on CMAQ prediction over Pearl River Delta, China

NASA Astrophysics Data System (ADS)

Deng, T.; Chen, Y.; Wan, Q.

2017-12-01

The Community Multiscale Air Quality (CMAQ) model was utilized for forecasting air quality over the Pearl River Delta (PRD) region from December 2013 to January 2014. The pollution forecasting performance of CMAQ coupled with the two different meteorological models, the Global/Regional Assimilation and Prediction System (GRAPES) and the 5th-generation Mesoscale Model (MM5), was assessed by combining observational data. The effect of meteorological factors and physical-chemical processes on forecast results was discussed through process analysis. The results showed that both models have similar good performance with better performance by GRAPES-CMAQ. GRAPES was superior in predicting the overall meteorological element variation tendencies but showed large deviations in atmospheric pressure and wind speed. It contributed to higher correlation coefficients of the pollutants with GRAPES-CMAQ, but with greater deviation. The underestimations of nitrate and ammonium salt contributed to the underestimations of Particle Matter (PM) and extinction coefficients. Surface layer SO2, CO and NO source emissions made the sole positive contribution. O3 originated mainly from horizontal and vertical transport and chemical processes were the main consumption item. On the contrary, NO2 derived mainly from chemical production.

Classification of Multiple Chinese Liquors by Means of a QCM-based E-Nose and MDS-SVM Classifier.

PubMed

Li, Qiang; Gu, Yu; Jia, Jing

2017-01-30

Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, and production processes. Developing a novel, rapid, and reliable method to identify multiple Chinese liquors is of positive significance. This paper presents a pattern recognition system for classifying ten brands of Chinese liquors based on multidimensional scaling (MDS) and support vector machine (SVM) algorithms in a quartz crystal microbalance (QCM)-based electronic nose (e-nose) we designed. We evaluated the comprehensive performance of the MDS-SVM classifier that predicted all ten brands of Chinese liquors individually. The prediction accuracy (98.3%) showed superior performance of the MDS-SVM classifier over the back-propagation artificial neural network (BP-ANN) classifier (93.3%) and moving average-linear discriminant analysis (MA-LDA) classifier (87.6%). The MDS-SVM classifier has reasonable reliability, good fitting and prediction (generalization) performance in classification of the Chinese liquors. Taking both application of the e-nose and validation of the MDS-SVM classifier into account, we have thus created a useful method for the classification of multiple Chinese liquors.

Investigation of Neural Strategies of Visual Search

NASA Technical Reports Server (NTRS)

Krauzlis, Richard J.

2003-01-01

The goal of this project was to measure how neurons in the superior colliculus (SC) change their activity during a visual search task. Specifically, we proposed to measure how the activity of these neurons was altered by the discriminability of visual targets and to test how these changes might predict the changes in the subjects performance. The primary rationale for this study was that understanding how the information encoded by these neurons constrains overall search performance would foster the development of better models of human performance. Work performed during the period supported by this grant has achieved these aims. First, we have recorded from neurons in the superior colliculus (SC) during a visual search task in which the difficulty of the task and the performance of the subject was systematically varied. The results from these single-neuron physiology experiments shows that prior to eye movement onset, the difference in activity across the ensemble of neurons reaches a fixed threshold value, reflecting the operation of a winner-take-all mechanism. Second, we have developed a model of eye movement decisions based on the principle of winner-take-all . The model incorporates the idea that the overt saccade choice reflects only one of the multiple saccades prepared during visual discrimination, consistent with our physiological data. The value of the model is that, unlike previous models, it is able to account for both the latency and the percent correct of saccade choices.

Enhanced cortical connectivity in absolute pitch musicians: a model for local hyperconnectivity.

PubMed

Loui, Psyche; Li, H Charles; Hohmann, Anja; Schlaug, Gottfried

2011-04-01

Connectivity in the human brain has received increased scientific interest in recent years. Although connection disorders can affect perception, production, learning, and memory, few studies have associated brain connectivity with graded variations in human behavior, especially among normal individuals. One group of normal individuals who possess unique characteristics in both behavior and brain structure is absolute pitch (AP) musicians, who can name the appropriate pitch class of any given tone without a reference. Using diffusion tensor imaging and tractography, we observed hyperconnectivity in bilateral superior temporal lobe structures linked to AP possession. Furthermore, volume of tracts connecting left superior temporal gyrus to left middle temporal gyrus predicted AP performance. These findings extend previous reports of exaggerated temporal lobe asymmetry, may explain the higher incidence of AP in special populations, and may provide a model for understanding the heightened connectivity that is thought to underlie savant skills and cases of exceptional creativity.

Enhanced Cortical Connectivity in Absolute Pitch Musicians: A Model for Local Hyperconnectivity

PubMed Central

Loui, Psyche; Charles Li, Hui C.; Hohmann, Anja; Schlaug, Gottfried

2010-01-01

Connectivity in the human brain has received increased scientific interest in recent years. Although connection disorders can affect perception, production, learning, and memory, few studies have associated brain connectivity with graded variations in human behavior, especially among normal individuals. One group of normal individuals who possess unique characteristics in both behavior and brain structure is absolute pitch (AP) musicians, who can name the appropriate pitch class of any given tone without a reference. Using diffusion tensor imaging and tractography, we observed hyperconnectivity in bilateral superior temporal lobe structures linked to AP possession. Furthermore, volume of tracts connecting left superior temporal gyrus to left middle temporal gyrus predicted AP performance. These findings extend previous reports of exaggerated temporal lobe asymmetry, may explain the higher incidence of AP in developmental disorders, and may provide a model for understanding the heightened connectivity that is thought to underlie savant skills and cases of exceptional creativity. PMID:20515408

Perception-action coupling in complex game play: Exploring the quiet eye in contested basketball jump shots.

PubMed

Klostermann, André; Panchuk, Derek; Farrow, Damian

2018-05-01

The duration of the final fixation before movement initiation - a gaze strategy labelled quiet eye - has been found to explain differences in motor expertise and performance in precision tasks. To date, research only addressed this phenomenon in situations without adversarial constraints. In the present study, we compared the quiet-eye behaviour of intermediately-skilled and highly-skilled basketball players in defended vs. undefended game situations. We predicted differences in quiet-eye duration as a function of skill and performance particularly resulting from late quiet-eye offsets. Results indicated performance-enhancing effects of long quiet-eye durations in the defended but not in the undefended game situation. Furthermore, in line with our prediction, later quiet-eye offsets were associated with superior performance elucidating the phenomenon's relevance in online-demanding motor tasks. Further, earlier quiet-eye onsets were linked to successful performance supporting earlier suggestions that it is not only the duration but also the timing that matters. These findings not only extend the positive effects of the quiet eye in motor performance to dynamic game-play situations but also support the role of the quiet eye in response to programming and information processing respectively.

A high resolution computer tomography scoring system to predict culture-positive pulmonary tuberculosis in the emergency department.

PubMed

Yeh, Jun-Jun; Neoh, Choo-Aun; Chen, Cheng-Ren; Chou, Christine Yi-Ting; Wu, Ming-Ting

2014-01-01

This study evaluated the use of high-resolution computed tomography (HRCT) to predict the presence of culture-positive pulmonary tuberculosis (PTB) in adult patients with pulmonary lesions in the emergency department (ED). The study included a derivation phase and validation phase with a total of 8,245 patients with pulmonary disease. There were 132 patients with culture-positive PTB in the derivation phase and 147 patients with culture-positive PTB in the validation phase. Imaging evaluation of pulmonary lesions included morphology and segmental distribution. The post-test probability ratios between both phases in three prevalence areas were analyzed. In the derivation phase, a multivariate analysis model identified cavitation, consolidation, and clusters/nodules in right or left upper lobe (except anterior segment) and consolidation of the superior segment of the right or left lower lobe as independent positive factors for culture-positive PTB, while consolidation of the right or left lower lobe (except superior segment) were independent negative factors. An ideal cutoff point based on the receiver operating characteristic (ROC) curve analysis was obtained at a score of 1. The sensitivity, specificity, positivity predictive value, and negative predictive value from derivation phase were 98.5% (130/132), 99.7% (3997/4008), 92.2% (130/141), and 99.9% (3997/3999). Based on the predicted positive likelihood ratio value of 328.33 in derivation phase, the post-test probability was observed to be 91.5% in the derivation phase, 92.5% in the validation phase, 94.5% in a high TB prevalence area, 91.0% in a moderate prevalence area, and 76.8% in moderate-to-low prevalence area. Our model using HRCT, which is feasible to perform in the ED, can promptly diagnose culture-positive PTB in moderate and moderate-to-low prevalence areas.

Biomarker-based risk prediction in the community.

PubMed

AbouEzzeddine, Omar F; McKie, Paul M; Scott, Christopher G; Rodeheffer, Richard J; Chen, Horng H; Michael Felker, G; Jaffe, Allan S; Burnett, John C; Redfield, Margaret M

2016-11-01

Guided by predictive characteristics of cardiovascular biomarkers, we explored the clinical implications of a simulated biomarker-guided heart failure (HF) and major adverse cardiovascular events (MACE) prevention strategy in the community. In a community cohort (n = 1824), the predictive characteristics for HF and MACE of galectin-3 (Gal-3), ST2, high-sensitivity cardiac troponin I (hscTnI), high-sensitivity C-reactive protein (hsCRP), N-terminal pro-brain natriuretic peptide (NT-proBNP) and B-type natriuretic peptide (BNP) were established. We performed number needed to screen (NNS) and treat (NNT) with the intervention analyses according to biomarker screening strategy and intervention efficacy in persons with at least one cardiovascular risk factor. In the entire cohort, for both HF and MACE, the predictive characteristics of NT-proBNP and hscTnI were superior to other biomarkers; alone, in a multimarker model, and adjusting for clinical risk factors. An NT-proBNP-guided preventative intervention with an intervention effect size (4-year hazard ratio for intervention in biomarker positive cohort) of ≤0.7 would reduce the global burden of HF by ≥20% and MACE by ≥15%. From this simulation, the NNS to prevent one HF event or MACE in 4 years would be ≤100 with a NNT to prevent one HF event of ≤20 and one MACE of ≤10. The predictive characteristics of NT-proBNP and hscTnI for HF or MACE in the community are superior to other biomarkers. Biomarker-guided preventative interventions with reasonable efficacy would compare favourably to established preventative interventions. This data provides a framework for biomarker selection which may inform design of biomarker-guided preventative intervention trials. © 2016 The Authors. European Journal of Heart Failure © 2016 European Society of Cardiology.

Comparative Analysis of 2-D Versus 3-D Ultrasound Estimation of the Fetal Adrenal Gland Volume and Prediction of Preterm Birth

PubMed Central

Turan, Ozhan M.; Turan, Sifa; Buhimschi, Irina A.; Funai, Edmund F.; Campbell, Katherine H.; Bahtiyar, Ozan M.; Harman, Chris R.; Copel, Joshua A.; Baschat, Ahmet A; Buhimschi, Catalin S.

2013-01-01

Objective We aim to test the hypothesis that 2D fetal AGV measurements offer similar volume estimates as volume calculations based on 3D technique Methods Fetal AGV was estimated by 3D ultrasound (VOCAL) in 93 women with signs/symptoms of preterm labor and 73 controls. Fetal AGV was calculated using an ellipsoid formula derived from 2D measurements of the same blocks (0.523× length × width × depth). Comparisons were performed by intra-class correlation coefficient (ICC), coefficient of repeatability, and Bland-Altman method. The cAGV (AGV/fetal weight) was calculated for both methods and compared for prediction of PTB within 7 days. Results Among 168 volumes, there was a significant correlation between 3D and 2D methods (ICC=0.979[95%CI: 0.971-0.984]). The coefficient of repeatability for the 3D was superior to the 2D method (Intra-observer 3D: 30.8, 2D:57.6; inter-observer 3D: 12.2, 2D: 15.6). Based on 2D calculations, a cAGV≥433mm3/kg, was best for prediction of PTB (sensitivity: 75%(95%CI=59-87); specificity: 89%(95%CI=82-94). Sensitivity and specificity for the 3D cAGV (cut-off ≥420mm3/kg) was 85%(95%CI=70-94) and 95%(95%CI=90-98), respectively. In receiver-operating-curve curve analysis, 3D cAGV was superior to 2D cAGV for prediction of PTB (z=1.99, p=0.047). Conclusion 2D volume estimation of fetal adrenal gland using ellipsoid formula cannot replace 3D AGV calculations for prediction of PTB. PMID:22644825

Genomic selection of agronomic traits in hybrid rice using an NCII population.

PubMed

Xu, Yang; Wang, Xin; Ding, Xiaowen; Zheng, Xingfei; Yang, Zefeng; Xu, Chenwu; Hu, Zhongli

2018-05-10

Hybrid breeding is an effective tool to improve yield in rice, while parental selection remains the key and difficult issue. Genomic selection (GS) provides opportunities to predict the performance of hybrids before phenotypes are measured. However, the application of GS is influenced by several genetic and statistical factors. Here, we used a rice North Carolina II (NC II) population constructed by crossing 115 rice varieties with five male sterile lines as a model to evaluate effects of statistical methods, heritability, marker density and training population size on prediction for hybrid performance. From the comparison of six GS methods, we found that predictabilities for different methods are significantly different, with genomic best linear unbiased prediction (GBLUP) and least absolute shrinkage and selection operation (LASSO) being the best, support vector machine (SVM) and partial least square (PLS) being the worst. The marker density has lower influence on predicting rice hybrid performance compared with the size of training population. Additionally, we used the 575 (115 × 5) hybrid rice as a training population to predict eight agronomic traits of all hybrids derived from 120 (115 + 5) rice varieties each mating with 3023 rice accessions from the 3000 rice genomes project (3 K RGP). Of the 362,760 potential hybrids, selection of the top 100 predicted hybrids would lead to 35.5%, 23.25%, 30.21%, 42.87%, 61.80%, 75.83%, 19.24% and 36.12% increase in grain yield per plant, thousand-grain weight, panicle number per plant, plant height, secondary branch number, grain number per panicle, panicle length and primary branch number, respectively. This study evaluated the factors affecting predictabilities for hybrid prediction and demonstrated the implementation of GS to predict hybrid performance of rice. Our results suggest that GS could enable the rapid selection of superior hybrids, thus increasing the efficiency of rice hybrid breeding.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

The prognostic performance of the complement system in septic patients in emergency department: a cohort study.

PubMed

Zhao, Xin; Chen, Yun-Xia; Li, Chun-Sheng

2015-01-01

To investigate the prognostic performance of complement components in septic patients, complement 3, membrane attack complex (MAC) and mannose-binding lectin were measured and compared among adult patients with sepsis, severe sepsis and septic shock, as well as between in-hospital nonsurvivors and survivors. The prognostic value of complement components was compared with mortality in emergency department sepsis (MEDS) score. Median complement 3, MAC and mannose-binding lectin increased directly with the sepsis, severe sepsis and septic shock groups, and were significantly higher in nonsurvivors than in survivors. MEDS and MAC independently predicted in-hospital mortality. The prognostic performance of MAC was superior to MEDS as analyzed by receiver operating characteristic curve and area under the curve.

Predicting geomorphic stability in low-order streams of the western Lake Superior basin

EPA Science Inventory

Width:depth ratios, entrenchment ratios, gradients, and median substrate particle sizes (D50s) were measured in 32 second and third order stream reaches in the western Lake Superior basin, and stream reaches were assigned a Rosgen geomorphic classification. Over 700 measurements ...

Enhancing Flood Prediction Reliability Using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Liu, Z.; Merwade, V.

2017-12-01

Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

Predictive local receptive fields based respiratory motion tracking for motion-adaptive radiotherapy.

PubMed

Yubo Wang; Tatinati, Sivanagaraja; Liyu Huang; Kim Jeong Hong; Shafiq, Ghufran; Veluvolu, Kalyana C; Khong, Andy W H

2017-07-01

Extracranial robotic radiotherapy employs external markers and a correlation model to trace the tumor motion caused by the respiration. The real-time tracking of tumor motion however requires a prediction model to compensate the latencies induced by the software (image data acquisition and processing) and hardware (mechanical and kinematic) limitations of the treatment system. A new prediction algorithm based on local receptive fields extreme learning machines (pLRF-ELM) is proposed for respiratory motion prediction. All the existing respiratory motion prediction methods model the non-stationary respiratory motion traces directly to predict the future values. Unlike these existing methods, the pLRF-ELM performs prediction by modeling the higher-level features obtained by mapping the raw respiratory motion into the random feature space of ELM instead of directly modeling the raw respiratory motion. The developed method is evaluated using the dataset acquired from 31 patients for two horizons in-line with the latencies of treatment systems like CyberKnife. Results showed that pLRF-ELM is superior to that of existing prediction methods. Results further highlight that the abstracted higher-level features are suitable to approximate the nonlinear and non-stationary characteristics of respiratory motion for accurate prediction.

An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

PubMed Central

Safa Sadiq, Ali; Fisal, Norsheila Binti; Ghafoor, Kayhan Zrar; Lloret, Jaime

2014-01-01

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches. PMID:25574490

An adaptive handover prediction scheme for seamless mobility based wireless networks.

PubMed

Sadiq, Ali Safa; Fisal, Norsheila Binti; Ghafoor, Kayhan Zrar; Lloret, Jaime

2014-01-01

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine

NASA Astrophysics Data System (ADS)

Imani, Moslem; Kao, Huan-Chin; Lan, Wen-Hau; Kuo, Chung-Yen

2018-02-01

The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

Longitudinal association of anthropometric measures of adiposity with cardiometabolic risk factors in postmenopausal women

PubMed Central

Kabat, Geoffrey C.; Heo, Moonseong; Van Horn, Linda V.; Kazlauskaite, Rasa; Getaneh, Asqual; Ard, Jamy; Vitolins, Mara Z.; Waring, Molly E.; Zaslavsky, Oleg; Smoller, Sylvia Wassertheil; Rohan, Thomas E.

2015-01-01

Purpose Some studies suggest that anthropometric measures of abdominal obesity may be superior to body mass index for the prediction of cardiometabolic risk factors; however, most studies have been cross-sectional. Our aim was to prospectively examine the association of change in body mass index (BMI), waist-hip ratio (WHR), waist circumference (WC), and waist circumference-height ratio (WCHtR) with change in markers of cardiometabolic risk in a population of postmenopausal women. Methods We used a subsample of participants in the Women’s Health Initiative aged 50 to 79 at entry with available fasting blood samples and anthropometric measurements obtained at multiple time points over 12.8 years of follow-up (N = 2,672). The blood samples were used to measure blood glucose, insulin, total cholesterol, LDL-C, HDL-C, and triglycerides at baseline, and at years 1, 3, and 6. We conducted mixed-effects linear regression analyses to examine associations at baseline and longitudinal associations between change in anthropometric measures and change in cardiometabolic risk factors, adjusting for covariates. Results In longitudinal analyses, change in BMI, WC, and WCHtR robustly predicted change in cardiometabolic risk, whereas change in WHR did not. The strongest associations were seen for change in triglycerides, glucose, and HDL-C (inverse association). Conclusion Increase in BMI, WC, and WCHtR strongly predicted increases in serum triglycerides and glucose, and reduced HDL-C. WC and WCHtR were superior to BMI in predicting serum glucose, HDL-C, and triglycerides. WCHtR was superior to WC only in predicting serum glucose. BMI, WC, and WCHtR were all superior to WHR. PMID:25453348

Longitudinal association of anthropometric measures of adiposity with cardiometabolic risk factors in postmenopausal women.

PubMed

Kabat, Geoffrey C; Heo, Moonseong; Van Horn, Linda V; Kazlauskaite, Rasa; Getaneh, Asqual; Ard, Jamy; Vitolins, Mara Z; Waring, Molly E; Zaslavsky, Oleg; Wassertheil-Smoller, Sylvia; Rohan, Thomas E

2014-12-01

Some studies suggest that anthropometric measures of abdominal obesity may be superior to body mass index (BMI) for the prediction of cardiometabolic risk factors; however, most studies have been cross-sectional. Our aim was to prospectively examine the association of change in BMI, waist-to-hip ratio (WHR), waist circumference (WC), and waist circumference-to-height ratio (WCHtR) with change in markers of cardiometabolic risk in a population of postmenopausal women. We used a subsample of participants in the Women's Health Initiative aged 50 to 79 years at entry with available fasting blood samples and anthropometric measurements obtained at multiple time points over 12.8 years of follow-up (n = 2672). The blood samples were used to measure blood glucose, insulin, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), and triglycerides at baseline, and at years 1, 3, and 6. We conducted mixed-effects linear regression analyses to examine associations at baseline and longitudinal associations between change in anthropometric measures and change in cardiometabolic risk factors, adjusting for covariates. In longitudinal analyses, change in BMI, WC, and WCHtR robustly predicted change in cardiometabolic risk, whereas change in WHR did not. The strongest associations were seen for change in triglycerides, glucose, and HDL-C (inverse association). Increase in BMI, WC, and WCHtR strongly predicted increases in serum triglycerides and glucose, and reduced HDL-C. WC and WCHtR were superior to BMI in predicting serum glucose, HDL-C, and triglycerides. WCHtR was superior to WC only in predicting serum glucose. BMI, WC, and WCHtR were all superior to WHR.

Does human capital matter? A meta-analysis of the relationship between human capital and firm performance.

PubMed

Crook, T Russell; Todd, Samuel Y; Combs, James G; Woehr, David J; Ketchen, David J

2011-05-01

Theory at both the micro and macro level predicts that investments in superior human capital generate better firm-level performance. However, human capital takes time and money to develop or acquire, which potentially offsets its positive benefits. Indeed, extant tests appear equivocal regarding its impact. To clarify what is known, we meta-analyzed effects drawn from 66 studies of the human capital-firm performance relationship and investigated 3 moderators suggested by resource-based theory. We found that human capital relates strongly to performance, especially when the human capital in question is not readily tradable in labor markets and when researchers use operational performance measures that are not subject to profit appropriation. Our results suggest that managers should invest in programs that increase and retain firm-specific human capital.

Dual Energy X-Ray Absorptiometry Compared with Anthropometry in Relation to Cardio-Metabolic Risk Factors in a Young Adult Population: Is the 'Gold Standard' Tarnished?

PubMed

Demmer, Denise L; Beilin, Lawrence J; Hands, Beth; Burrows, Sally; Pennell, Craig E; Lye, Stephen J; Mountain, Jennifer A; Mori, Trevor A

2016-01-01

Assessment of adiposity using dual energy x-ray absorptiometry (DXA) has been considered more advantageous in comparison to anthropometry for predicting cardio-metabolic risk in the older population, by virtue of its ability to distinguish total and regional fat. Nonetheless, there is increasing uncertainty regarding the relative superiority of DXA and little comparative data exist in young adults. This study aimed to identify which measure of adiposity determined by either DXA or anthropometry is optimal within a range of cardio-metabolic risk factors in young adults. 1138 adults aged 20 years were assessed by DXA and standard anthropometry from the Western Australian Pregnancy Cohort (Raine) Study. Cross-sectional linear regression analyses were performed. Waist to height ratio was superior to any DXA measure with HDL-C. BMI was the superior model in relation to blood pressure than any DXA measure. Midriff fat mass (DXA) and waist circumference were comparable in relation to glucose. For all the other cardio-metabolic variables, anthropometric and DXA measures were comparable. DXA midriff fat mass compared with BMI or waist hip ratio was the superior measure for triglycerides, insulin and HOMA-IR. Although midriff fat mass (measured by DXA) was the superior measure with insulin sensitivity and triglycerides, the anthropometric measures were better or equal with various DXA measures for majority of the cardio-metabolic risk factors. Our findings suggest, clinical anthropometry is generally as useful as DXA in the evaluation of the individual cardio-metabolic risk factors in young adults.

Use of a Tantalum Liner to Reduce Bore Erosion and Increase Muzzle Velocity in Two-Stage Light Gas Guns

NASA Technical Reports Server (NTRS)

Bogdanoff, David W.

2015-01-01

Muzzle velocities and gun erosion predicted by earlier numerical simulations of two stage light gas guns with steel gun tubes were in good agreement with experimental values. In a subsequent study, simulations of high performance shots were repeated with rhenium (Re) gun tubes. Large increases in muzzle velocity (2 - 4 km/sec) were predicted for Re tubes. In addition, the hydrogen-produced gun tube erosion was, in general, predicted to be zero with Re tubes. Tantalum (Ta) has some mechanical properties superior to those of Re. Tantalum has a lower modulus of elasticity than Re for better force transmission from the refractory metal liner to an underlying thick wall steel tube. Tantalum also has greater ductility than Re for better survivability during severe stress/strain cycles. Also, tantalum has been used as a coating or liner in military powder guns with encouraging results. Tantalum has, however, somewhat inferior thermal properties to those of rhenium, with a lower melting point and lower density and thermal conductivity. The present study was undertaken to see to what degree the muzzle velocity gains of rhenium gun tubes (over steel tubes) could be achieved with tantalum gun tubes. Nine high performance shots were modeled with a new version of our CFD gun code for steel, rhenium and tantalum gun tubes. For all except the highest velocity shot, the results with Ta tubes were nearly identical with those for Re tubes. Even for the highest velocity shot, the muzzle velocity gain over a steel tube using Ta was 82% of the gain obtained using Re. Thus, the somewhat inferior thermal properties of Ta (when compared to those of Re) translate into only very slightly poorer overall muzzle velocity performance. When this fact is combined with the superior mechanical properties of Ta and the encouraging performance of Ta liners/coatings in military powder guns, tantalum is to be preferred over Re as a liner/coating material for two stage light gas guns to increase muzzle velocity and reduce bore erosion.

Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.

PubMed

Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong

2018-06-15

Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S EP ) and charge distribution area (S σ-profile ) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R 2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. Copyright © 2018 Elsevier B.V. All rights reserved.

Value of Egy-Score in diagnosis of significant, advanced hepatic fibrosis and cirrhosis compared to aspartate aminotransferase-to-platelet ratio index, FIB-4 and Forns' index in chronic hepatitis C virus.

PubMed

Alboraie, Mohamed; Khairy, Marwa; Elsharkawy, Marwa; Asem, Noha; Elsharkawy, Aisha; Esmat, Gamal

2015-05-01

Serum markers and developed scores are of rising importance in non-invasive diagnosis of hepatic fibrosis. Aspartate aminotransferase-to-platelet ratio index (APRI), FIB-4 and Forns' index are validated scores used for diagnosis of liver fibrosis. The Egy-Score is a newly developed score for detection of hepatic fibrosis with promising results. We aimed to assess the accuracy of the Egy-Score in the diagnosis of significant fibrosis, advanced fibrosis and cirrhosis compared to APRI, FIB-4 and Forns' in chronic hepatitis C virus (HCV) patients. A retrospective study including 100 chronic hepatitis C naïve Egyptian patients was performed. Patients were classified according to stages of fibrosis into three groups: significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4). Egy-Score, APRI, FIB-4 and Forns' index were calculated. Regression analysis and receiver-operator curves were plotted to assess the sensitivity, specificity and predictive values for the significant scores with the best cut-off for diagnosis. An Egy-Score of 3.28 or more was superior to APRI, FIB-4 and Forns' index for detecting advanced fibrosis with a sensitivity of 91% and specificity of 78%. An Egy-Score of 3.67 or more was superior to APRI, FIB-4 and Forns' index for detecting cirrhosis with a sensitivity of 82% and specificity of 87%. Forns' index was superior to Egy-Score, FIB-4 and APRI for detecting significant fibrosis. The Egy-Score is a promising, accurate, easily calculated, cost-effective score in the prediction of hepatic fibrosis in chronic HCV patients with superiority over APRI, FIB-4 and Forns' index in advanced hepatic fibrosis and cirrhosis. © 2014 The Japan Society of Hepatology.

State orientation and memory load impair prospective memory performance in older compared to younger persons.

PubMed

Kaschel, Reiner; Kazén, Miguel; Kuhl, Julius

2017-07-01

A modified event-based paradigm of prospective memory was applied to investigate intention initiation in older and younger participants under high versus low memory load (subsequent episodic word recall vs. recognition). State versus action orientation, a personality dimension related to intention enactment, was also measured. State-oriented persons show a superiority effect for the storage of intentions in an explicit format but have a paradoxical deficit in their actual enactment. We predicted an interaction between aging, personality, and memory load, with longer intention-initiation latencies and higher omission rates for older state-oriented participants under high memory load. Results were consistent with predictions and are interpreted according to current personality and prospective memory models of aging.

Plasticity models of material variability based on uncertainty quantification techniques

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

Jones, Reese E.; Rizzi, Francesco; Boyce, Brad

The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQmore » techniques can be used in model selection and assessing the quality of calibrated physical parameters.« less

Resting-State fMRI Activity Predicts Unsupervised Learning and Memory in an Immersive Virtual Reality Environment

PubMed Central

Wong, Chi Wah; Olafsson, Valur; Plank, Markus; Snider, Joseph; Halgren, Eric; Poizner, Howard; Liu, Thomas T.

2014-01-01

In the real world, learning often proceeds in an unsupervised manner without explicit instructions or feedback. In this study, we employed an experimental paradigm in which subjects explored an immersive virtual reality environment on each of two days. On day 1, subjects implicitly learned the location of 39 objects in an unsupervised fashion. On day 2, the locations of some of the objects were changed, and object location recall performance was assessed and found to vary across subjects. As prior work had shown that functional magnetic resonance imaging (fMRI) measures of resting-state brain activity can predict various measures of brain performance across individuals, we examined whether resting-state fMRI measures could be used to predict object location recall performance. We found a significant correlation between performance and the variability of the resting-state fMRI signal in the basal ganglia, hippocampus, amygdala, thalamus, insula, and regions in the frontal and temporal lobes, regions important for spatial exploration, learning, memory, and decision making. In addition, performance was significantly correlated with resting-state fMRI connectivity between the left caudate and the right fusiform gyrus, lateral occipital complex, and superior temporal gyrus. Given the basal ganglia's role in exploration, these findings suggest that tighter integration of the brain systems responsible for exploration and visuospatial processing may be critical for learning in a complex environment. PMID:25286145

Predicting geomorphic stability in low-order streams of the western Lake Superior basin - Poster

EPA Science Inventory

Width:depth ratios, entrenchment ratios, gradients, and median substrate particle sizes (D50s) were measured in 32 second- and third-order stream reaches in the western Lake Superior basin in 1997-1998. More than 700 measurements of suspended sediment concentration during snowmel...

Glasgow Prognostic Score is superior to ECOG PS as a prognostic factor in patients with gastric cancer with peritoneal seeding.

PubMed

Yuan, Shu-Qiang; Nie, Run-Cong; Chen, Yong-Ming; Qiu, Hai-Bo; Li, Xiao-Ping; Chen, Xiao-Jiang; Xu, Li-Pu; Yang, Li-Fang; Sun, Xiao-Wei; Li, Yuan-Fang; Zhou, Zhi-Wei; Chen, Shi; Chen, Ying-Bo

2018-04-01

The Glasgow Prognostic Score (GPS) has been shown to be associated with survival rates in patients with advanced cancer. The present study aimed to compare the GPS with the Eastern Cooperative Oncology Group Performance Status (ECOG PS) in patients with gastric cancer with peritoneal seeding. For the investigation, a total of 384 gastric patients with peritoneal metastasis were retrospectively analyzed. Patients with elevated C-reactive protein (CRP; >10 mg/l) and hypoalbuminemia (<35 mg/l) were assigned a score of 2. Patients were assigned a score of 1 if presenting with only one of these abnormalities, and a score of 0 if neither of these abnormalities were present. The clinicopathologic characteristics and clinical outcomes of patients with peritoneal seeding were analyzed. The results showed that the median overall survival (OS) of patients in the GPS 0 group was longer, compared with that in the GPS 1 and GPS 2 groups (15.50, vs. 10.07 and 7.97 months, respectively; P<0.001). No significant difference was found between the median OS of patients with a good performance status (ECOG <2) and those with a poor (ECOG ≥2) performance status (13.67, vs. 11.80 months; P=0.076). In the subgroup analysis, the median OS in the GPS 0 group was significantly longer, compared with that in the GPS 1 and GPS 2 groups, for the patients receiving palliative chemotherapy and patients without palliative chemotherapy. Multivariate survival analysis demonstrated that CA19-9, palliative gastrectomy, first-line chemotherapy and GPS were the prognostic factors predicting OS. In conclusion, the GPS was superior to the subjective assessment of ECOG PS as a prognostic factor in predicting the outcome of gastric cancer with peritoneal seeding.

Application of a Novel Grey Self-Memory Coupling Model to Forecast the Incidence Rates of Two Notifiable Diseases in China: Dysentery and Gonorrhea

PubMed Central

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

Objective In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China. Methods The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates. Results Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China. Conclusion The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control. PMID:25546054

Application of a novel grey self-memory coupling model to forecast the incidence rates of two notifiable diseases in China: dysentery and gonorrhea.

PubMed

Guo, Xiaojun; Liu, Sifeng; Wu, Lifeng; Tang, Lingling

2014-01-01

In this study, a novel grey self-memory coupling model was developed to forecast the incidence rates of two notifiable infectious diseases (dysentery and gonorrhea); the effectiveness and applicability of this model was assessed based on its ability to predict the epidemiological trend of infectious diseases in China. The linear model, the conventional GM(1,1) model and the GM(1,1) model with self-memory principle (SMGM(1,1) model) were used to predict the incidence rates of the two notifiable infectious diseases based on statistical incidence data. Both simulation accuracy and prediction accuracy were assessed to compare the predictive performances of the three models. The best-fit model was applied to predict future incidence rates. Simulation results show that the SMGM(1,1) model can take full advantage of the systematic multi-time historical data and possesses superior predictive performance compared with the linear model and the conventional GM(1,1) model. By applying the novel SMGM(1,1) model, we obtained the possible incidence rates of the two representative notifiable infectious diseases in China. The disadvantages of the conventional grey prediction model, such as sensitivity to initial value, can be overcome by the self-memory principle. The novel grey self-memory coupling model can predict the incidence rates of infectious diseases more accurately than the conventional model, and may provide useful references for making decisions involving infectious disease prevention and control.

[Determination of soluble solids content in Nanfeng Mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM].

PubMed

Sun, Tong; Xu, Wen-Li; Hu, Tian; Liu, Mu-Hua

2013-12-01

The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350-1000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate t hat Vis/NIR spectroscopy combinedwith UVE-ICA-LS-SVM is suitable for assessing SSC o f Nanfeng mandarin, and t he precision o f prediction ishigh. UVE--ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.

Interactions of cognitive and auditory abilities in congenitally blind individuals.

PubMed

Rokem, Ariel; Ahissar, Merav

2009-02-01

Congenitally blind individuals have been found to show superior performance in perceptual and memory tasks. In the present study, we asked whether superior stimulus encoding could account for performance in memory tasks. We characterized the performance of a group of congenitally blind individuals on a series of auditory, memory and executive cognitive tasks and compared their performance to that of sighted controls matched for age, education and musical training. As expected, we found superior verbal spans among congenitally blind individuals. Moreover, we found superior speech perception, measured by resilience to noise, and superior auditory frequency discrimination. However, when memory span was measured under conditions of equivalent speech perception, by adjusting the signal to noise ratio for each individual to the same level of perceptual difficulty (80% correct), the advantage in memory span was completely eliminated. Moreover, blind individuals did not possess any advantage in cognitive executive functions, such as manipulation of items in memory and math abilities. We propose that the short-term memory advantage of blind individuals results from better stimulus encoding, rather than from superiority at subsequent processing stages.

The performance of PI-RADSv2 and quantitative apparent diffusion coefficient for predicting confirmatory prostate biopsy findings in patients considered for active surveillance of prostate cancer.

PubMed

Nougaret, Stephanie; Robertson, Nicola; Golia Pernicka, Jennifer; Molinari, Nicolas; Hötker, Andreas M; Ehdaie, Behfar; Sala, Evis; Hricak, Hedvig; Vargas, Hebert Alberto

2017-07-01

To assess the performance of the updated Prostate Imaging Reporting and Data System (PI-RADSv2) and the apparent diffusion coefficient (ADC) for predicting confirmatory biopsy results in patients considered for active surveillance of prostate cancer (PCA). IRB-approved, retrospective study of 371 consecutive men with clinically low-risk PCA (initial biopsy Gleason score ≤6, prostate-specific antigen <10 ng/ml, clinical stage ≤T2a) who underwent 3T-prostate MRI before confirmatory biopsy. Two independent radiologists recorded the PI-RADSv2 scores and measured the corresponding ADC values in each patient. A composite score was generated to assess the performance of combining PI-RADSv2 + ADC. PCA was upgraded on confirmatory biopsy in 107/371 (29%) patients. Inter-reader agreement was substantial (PI-RADSv2: k = 0.73; 95% CI [0.66-0.80]; ADC: r = 0.74; 95% CI [0.69-0.79]). Accuracies, sensitivities, specificities, positive predicted value and negative predicted value of PI-RADSv2 were 85, 89, 83, 68, 95 and 78, 82, 76, 58, 91% for ADC. PI-RADSv2 accuracy was significantly higher than that of ADC for predicting biopsy upgrade (p = 0.014). The combined PI-RADSv2 + ADC composite score did not perform better than PI-RADSv2 alone. Obviating biopsy in patients with PI-RADSv2 score ≤3 would have missed Gleason Score upgrade in 12/232 (5%) of patients. PI-RADSv2 was superior to ADC measurements for predicting PCA upgrading on confirmatory biopsy.

Picture superiority in free recall: the effects of normal aging and primary degenerative dementia.

PubMed

Rissenberg, M; Glanzer, M

1986-01-01

A key factor in the decline of memory with age may be a breakdown of communication in the information network involved in memory and cognitive processing. A special case of this communication is assumed to underlie the picture superiority effect in recall. From this hypothesis it follows that the picture superiority effect should lessen with age. In Experiment 1, three groups of adults (young, old normal, and old memory-impaired) were tested in free recall of pictures and word lists. As predicted, the picture superiority effect declined with age. Experiment 2 replicated these findings and showed, moreover, that the picture superiority effect can be reestablished in normal old adults by instructing them to verbalize overtly during item presentation.

Prediction of brain-computer interface aptitude from individual brain structure.

PubMed

Halder, S; Varkuti, B; Bogdan, M; Kübler, A; Rosenstiel, W; Sitaram, R; Birbaumer, N

2013-01-01

Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. This confirms that structural brain traits contribute to individual performance in BCI use.

Prediction of brain-computer interface aptitude from individual brain structure

PubMed Central

Halder, S.; Varkuti, B.; Bogdan, M.; Kübler, A.; Rosenstiel, W.; Sitaram, R.; Birbaumer, N.

2013-01-01

Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use. PMID:23565083

Paramedic self-efficacy and skill retention in pediatric airway management.

PubMed

Youngquist, Scott T; Henderson, Deborah P; Gausche-Hill, Marianne; Goodrich, Suzanne M; Poore, Pamela D; Lewis, Roger J

2008-12-01

The objectives were to determine the effect of pediatric airway management training on paramedic self-efficacy and skill performance and to determine which of several retraining methods is superior. A total of 2,520 paramedics were trained to proficiency in pediatric bag-mask ventilation (BMV) and endotracheal intubation (ETI) on mannequins. Subjects were a convenience sample of 245 (10% of original cohort) presenting for voluntary retraining. A total of 212 of 245 (87%) completed skills testing. Self-efficacy was measured prior to and following initial training and retraining events. Paramedics were assigned to control (no retraining), videotape presentation, self-directed learning, or instructor-facilitated lecture and demonstration retraining. Following retraining, BMV and ETI skills were tested. Paramedics from low-call-volume areas reported lower baseline self-efficacy and derived larger increases with training, but also experienced the most decline between training events. Pass rates for BMV and ETI were 66% (139/211) and 42% (88/212), respectively. However, overall cohort self-efficacy was maintained over the study period. In ordinal regression modeling, only the lecture and demonstration method was superior to control, with an odds ratio (OR) of achieving higher scores of 2.5 (95% confidence interval [CI] = 1.2 to 5.2) for BMV and 5.2 (95% CI = 2.4 to 11.2) for ETI. Poor performance with ETI but not BMV was associated with time elapsed since training (p = 0.01). Self-efficacy ratings were not predictive of skill performance. Training provides increases in self-efficacy, particularly among paramedics from low-call-volume areas. A gap exists between self-efficacy and skill performance, in that self-efficacy may be maintained even when skill performance declines. Pediatric airway skills decay quickly, ETI skills drop off more significantly than BMV skills, and a lecture and demonstration format seems superior to other retraining methods investigated.

PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine.

PubMed

Eyal-Altman, Noah; Last, Mark; Rubin, Eitan

2017-01-17

Numerous publications attempt to predict cancer survival outcome from gene expression data using machine-learning methods. A direct comparison of these works is challenging for the following reasons: (1) inconsistent measures used to evaluate the performance of different models, and (2) incomplete specification of critical stages in the process of knowledge discovery. There is a need for a platform that would allow researchers to replicate previous works and to test the impact of changes in the knowledge discovery process on the accuracy of the induced models. We developed the PCM-SABRE platform, which supports the entire knowledge discovery process for cancer outcome analysis. PCM-SABRE was developed using KNIME. By using PCM-SABRE to reproduce the results of previously published works on breast cancer survival, we define a baseline for evaluating future attempts to predict cancer outcome with machine learning. We used PCM-SABRE to replicate previous work that describe predictive models of breast cancer recurrence, and tested the performance of all possible combinations of feature selection methods and data mining algorithms that was used in either of the works. We reconstructed the work of Chou et al. observing similar trends - superior performance of Probabilistic Neural Network (PNN) and logistic regression (LR) algorithms and inconclusive impact of feature pre-selection with the decision tree algorithm on subsequent analysis. PCM-SABRE is a software tool that provides an intuitive environment for rapid development of predictive models in cancer precision medicine.

On the same wavelength: predictable language enhances speaker-listener brain-to-brain synchrony in posterior superior temporal gyrus.

PubMed

Dikker, Suzanne; Silbert, Lauren J; Hasson, Uri; Zevin, Jason D

2014-04-30

Recent research has shown that the degree to which speakers and listeners exhibit similar brain activity patterns during human linguistic interaction is correlated with communicative success. Here, we used an intersubject correlation approach in fMRI to test the hypothesis that a listener's ability to predict a speaker's utterance increases such neural coupling between speakers and listeners. Nine subjects listened to recordings of a speaker describing visual scenes that varied in the degree to which they permitted specific linguistic predictions. In line with our hypothesis, the temporal profile of listeners' brain activity was significantly more synchronous with the speaker's brain activity for highly predictive contexts in left posterior superior temporal gyrus (pSTG), an area previously associated with predictive auditory language processing. In this region, predictability differentially affected the temporal profiles of brain responses in the speaker and listeners respectively, in turn affecting correlated activity between the two: whereas pSTG activation increased with predictability in the speaker, listeners' pSTG activity instead decreased for more predictable sentences. Listeners additionally showed stronger BOLD responses for predictive images before sentence onset, suggesting that highly predictable contexts lead comprehenders to preactivate predicted words.

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

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

He, Yupeng; Gorkin, David U.; Dickel, Diane E.

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared withmore » existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.« less

Constructing and predicting solitary pattern solutions for nonlinear time-fractional dispersive partial differential equations

NASA Astrophysics Data System (ADS)

Arqub, Omar Abu; El-Ajou, Ahmad; Momani, Shaher

2015-07-01

Building fractional mathematical models for specific phenomena and developing numerical or analytical solutions for these fractional mathematical models are crucial issues in mathematics, physics, and engineering. In this work, a new analytical technique for constructing and predicting solitary pattern solutions of time-fractional dispersive partial differential equations is proposed based on the generalized Taylor series formula and residual error function. The new approach provides solutions in the form of a rapidly convergent series with easily computable components using symbolic computation software. For method evaluation and validation, the proposed technique was applied to three different models and compared with some of the well-known methods. The resultant simulations clearly demonstrate the superiority and potentiality of the proposed technique in terms of the quality performance and accuracy of substructure preservation in the construct, as well as the prediction of solitary pattern solutions for time-fractional dispersive partial differential equations.

Testing by artificial intelligence: computational alternatives to the determination of mutagenicity.

PubMed

Klopman, G; Rosenkranz, H S

1992-08-01

In order to develop methods for evaluating the predictive performance of computer-driven structure-activity methods (SAR) as well as to determine the limits of predictivity, we investigated the behavior of two Salmonella mutagenicity data bases: (a) a subset from the Genetox Program and (b) one from the U.S. National Toxicology Program (NTP). For molecules common to the two data bases, the experimental concordance was 76% when "marginals" were included and 81% when they were excluded. Three SAR methods were evaluated: CASE, MULTICASE and CASE/Graph Indices (CASE/GI). The programs "learned" the Genetox data base and used it to predict NTP molecules that were not present in the Genetox compilation. The concordances were 72, 80 and 47% respectively. Obviously, the MULTICASE version is superior and approaches the 85% interlaboratory variability observed for the Salmonella mutagenicity assays when the latter was carried out under carefully controlled conditions.

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

DOE PAGES

He, Yupeng; Gorkin, David U.; Dickel, Diane E.; ...

2017-02-13

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulator y element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared withmore » existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types.« less

Visual search performance in infants associates with later ASD diagnosis.

PubMed

Cheung, C H M; Bedford, R; Johnson, M H; Charman, T; Gliga, T

2018-01-01

An enhanced ability to detect visual targets amongst distractors, known as visual search (VS), has often been documented in Autism Spectrum Disorders (ASD). Yet, it is unclear when this behaviour emerges in development and if it is specific to ASD. We followed up infants at high and low familial risk for ASD to investigate how early VS abilities links to later ASD diagnosis, the potential underlying mechanisms of this association and the specificity of superior VS to ASD. Clinical diagnosis of ASD as well as dimensional measures of ASD, attention-deficit/hyperactivity disorder (ADHD) and anxiety symptoms were ascertained at 3 years. At 9 and 15 months, but not at age 2 years, high-risk children who later met clinical criteria for ASD (HR-ASD) had better VS performance than those without later diagnosis and low-risk controls. Although HR-ASD children were also more attentive to the task at 9 months, this did not explain search performance. Superior VS specifically predicted 3 year-old ASD but not ADHD or anxiety symptoms. Our results demonstrate that atypical perception and core ASD symptoms of social interaction and communication are closely and selectively associated during early development, and suggest causal links between perceptual and social features of ASD. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato.

PubMed

Stich, Benjamin; Van Inghelandt, Delphine

2018-01-01

Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i) examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii) investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii) assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP), BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY), and tuber yield (TY) of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs.

Delirium prediction in the intensive care unit: comparison of two delirium prediction models.

PubMed

Wassenaar, Annelies; Schoonhoven, Lisette; Devlin, John W; van Haren, Frank M P; Slooter, Arjen J C; Jorens, Philippe G; van der Jagt, Mathieu; Simons, Koen S; Egerod, Ingrid; Burry, Lisa D; Beishuizen, Albertus; Matos, Joaquim; Donders, A Rogier T; Pickkers, Peter; van den Boogaard, Mark

2018-05-05

Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction  model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of - 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible. While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

Prospects and Potential Uses of Genomic Prediction of Key Performance Traits in Tetraploid Potato

PubMed Central

Stich, Benjamin; Van Inghelandt, Delphine

2018-01-01

Genomic prediction is a routine tool in breeding programs of most major animal and plant species. However, its usefulness for potato breeding has not yet been evaluated in detail. The objectives of this study were to (i) examine the prospects of genomic prediction of key performance traits in a diversity panel of tetraploid potato modeling additive, dominance, and epistatic effects, (ii) investigate the effects of size and make up of training set, number of test environments and molecular markers on prediction accuracy, and (iii) assess the effect of including markers from candidate genes on the prediction accuracy. With genomic best linear unbiased prediction (GBLUP), BayesA, BayesCπ, and Bayesian LASSO, four different prediction methods were used for genomic prediction of relative area under disease progress curve after a Phytophthora infestans infection, plant maturity, maturity corrected resistance, tuber starch content, tuber starch yield (TSY), and tuber yield (TY) of 184 tetraploid potato clones or subsets thereof genotyped with the SolCAP 8.3k SNP array. The cross-validated prediction accuracies with GBLUP and the three Bayesian approaches for the six evaluated traits ranged from about 0.5 to about 0.8. For traits with a high expected genetic complexity, such as TSY and TY, we observed an 8% higher prediction accuracy using a model with additive and dominance effects compared with a model with additive effects only. Our results suggest that for oligogenic traits in general and when diagnostic markers are available in particular, the use of Bayesian methods for genomic prediction is highly recommended and that the diagnostic markers should be modeled as fixed effects. The evaluation of the relative performance of genomic prediction vs. phenotypic selection indicated that the former is superior, assuming cycle lengths and selection intensities that are possible to realize in commercial potato breeding programs. PMID:29563919

Deciphering RNA Regulatory Elements Involved in the Developmental and Environmental Gene Regulation of Trypanosoma brucei.

PubMed

Gazestani, Vahid H; Salavati, Reza

2015-01-01

Trypanosoma brucei is a vector-borne parasite with intricate life cycle that can cause serious diseases in humans and animals. This pathogen relies on fine regulation of gene expression to respond and adapt to variable environments, with implications in transmission and infectivity. However, the involved regulatory elements and their mechanisms of actions are largely unknown. Here, benefiting from a new graph-based approach for finding functional regulatory elements in RNA (GRAFFER), we have predicted 88 new RNA regulatory elements that are potentially involved in the gene regulatory network of T. brucei. We show that many of these newly predicted elements are responsive to both transcriptomic and proteomic changes during the life cycle of the parasite. Moreover, we found that 11 of predicted elements strikingly resemble previously identified regulatory elements for the parasite. Additionally, comparison with previously predicted motifs on T. brucei suggested the superior performance of our approach based on the current limited knowledge of regulatory elements in T. brucei.

Predicting Older Driver On-Road Performance by Means of the Useful Field of View and Trail Making Test Part B

PubMed Central

Wang, Yanning; Crizzle, Alexander M.; Winter, Sandra M.; Lanford, Desiree N.

2013-01-01

The Useful Field of View® (UFOV) and Trail Making Test Part B (Trails B) are measures of divided attention. We determined which measure was more accurate in predicting on-road outcomes among drivers (N = 198, mean age = 73.86, standard deviation = 6.05). Receiver operating characteristic curves for the UFOV (Risk Index [RI] and Subtests 1–3) and Trails B significantly predicted on-road outcomes. Contrasting Trails B with the UFOV RI and subtests, the only difference was found between the UFOV RI and Trails B, indicating the UFOV RI was the best predictor of on-road outcomes. Misclassifications of drivers totaled 28 for the UFOV RI, 62 for Trails B, and 58 for UFOV Subtest 2. The UFOV RI is a superior test in predicting on-road outcomes, but the Trails B has acceptable accuracy and is comparable to the other UFOV subtests. PMID:23968796

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

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

Aircraft Engine Systems

NASA Technical Reports Server (NTRS)

Veres, Joseph P.

2003-01-01

The objective is to develop the capability to numerically model the performance of gas turbine engines used for aircraft propulsion. This capability will provide turbine engine designers with a means of accurately predicting the performance of new engines in a system environment prior to building and testing. The 'numerical test cell' developed under this project will reduce the number of component and engine tests required during development. As a result, the project will help to reduce the design cycle time and cost of gas turbine engines. This capability will be distributed to U.S. turbine engine manufacturers and air framers. This project focuses on goals of maintaining U.S. superiority in commercial gas turbine engine development for the aeronautics industry.

Magnetostrictive direct drive motors

NASA Technical Reports Server (NTRS)

Naik, Dipak; Dehoff, P. H.

1990-01-01

Developing magnetostrictive direct drive research motors to power robot joints is discussed. These type motors are expected to produce extraordinary torque density, to be able to perform microradian incremental steps and to be self-braking and safe with the power off. Several types of motor designs have been attempted using magnetostrictive materials. One of the candidate approaches (the magnetostrictive roller drive) is described. The method in which the design will function is described as is the reason why this approach is inherently superior to the other approaches. Following this, the design will be modelled and its expected performance predicted. This particular candidate design is currently undergoing detailed engineering with prototype construction and testing scheduled for mid 1991.

Prediction of Human Activity by Discovering Temporal Sequence Patterns.

PubMed

Li, Kang; Fu, Yun

2014-08-01

Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units.

Optimal control theory for non-scalar-valued performance criteria. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Gerring, H. P.

1971-01-01

The theory of optimal control for nonscalar-valued performance criteria is discussed. In the space, where the performance criterion attains its value, the relations better than, worse than, not better than, and not worse than are defined by a partial order relation. The notion of optimality splits up into superiority and non-inferiority, because worse than is not the complement of better than, in general. A superior solution is better than every other solution. A noninferior solution is not worse than any other solution. Noninferior solutions have been investigated particularly for vector-valued performance criteria. Superior solutions for non-scalar-valued performance criteria attaining their values in abstract partially ordered spaces are emphasized. The main result is the infimum principle which constitutes necessary conditions for a control to be a superior solution to an optimal control problem.

Superior performance of N-terminal pro brain natriuretic peptide for diagnosis of acute decompensated heart failure in an Asian compared with a Western setting.

PubMed

Ibrahim, Irwani; Kuan, Win Sen; Frampton, Chris; Troughton, Richard; Liew, Oi Wah; Chong, Jenny Pek Ching; Chan, Siew Pang; Tan, Li Ling; Lin, Wei Qin; Pemberton, Chris J; Ooi, Shirley Beng Suat; Richards, A Mark

2017-02-01

This study was conducted to test the diagnostic performance of NT-proBNP for discrimination of acute decompensated heart failure (ADHF) among breathless patients presenting in an Asian compared with a Western centre. Patients with breathlessness were prospectively and contemporaneously recruited in Emergency Departments in Singapore and New Zealand (NZ). The diagnosis of ADHF was adjudicated by two clinician specialists. A total of 606 patients were recruited in Singapore and 500 in NZ. The discriminative power of NT-proBNP for ADHF was superior in Singapore compared with NZ [area under the curve (AUC) 0.926 vs. 0.866; P = 0.012] both overall and among selected subgroups stratified according to age, renal function, body mass index, and presence or absence of AF or diabetes. Previously established cut-off point values of plasma NT-proBNP yielded comparable sensitivity and negative predictive values, but superior specificity and accuracy in Singapore compared with NZ. The difference in test performance was driven by the younger age (median age 56 years vs. 73 years; P < 0.001), associated with better renal function (estimated glomerular filtration rate 89 vs. 62 mL/min/1.73 m 2 ; P < 0.001), and lower prevalence of AF (9.7% vs. 25.7%; P < 0.001) in acutely breathless patients in Singapore. Considering emerging evidence of a lower average age of presentation with ADHF over most of Asia compared with Western countries, NT-proBNP is likely to be more accurate when applied in Asian centres than in the West. © 2016 The Authors. European Journal of Heart Failure © 2016 European Society of Cardiology.

Rubisco catalytic properties of wild and domesticated relatives provide scope for improving wheat photosynthesis.

PubMed

Prins, Anneke; Orr, Douglas J; Andralojc, P John; Reynolds, Matthew P; Carmo-Silva, Elizabete; Parry, Martin A J

2016-03-01

Rubisco is a major target for improving crop photosynthesis and yield, yet natural diversity in catalytic properties of this enzyme is poorly understood. Rubisco from 25 genotypes of the Triticeae tribe, including wild relatives of bread wheat (Triticum aestivum), were surveyed to identify superior enzymes for improving photosynthesis in this crop. In vitro Rubisco carboxylation velocity (V c), Michaelis-Menten constants for CO2 (K c) and O2 (K o) and specificity factor (S c/o) were measured at 25 and 35 °C. V c and K c correlated positively, while V c and S c/o were inversely related. Rubisco large subunit genes (rbcL) were sequenced, and predicted corresponding amino acid differences analysed in relation to the corresponding catalytic properties. The effect of replacing native wheat Rubisco with counterparts from closely related species was analysed by modelling the response of photosynthesis to varying CO2 concentrations. The model predicted that two Rubisco enzymes would increase photosynthetic performance at 25 °C while only one of these also increased photosynthesis at 35 °C. Thus, under otherwise identical conditions, catalytic variation in the Rubiscos analysed is predicted to improve photosynthetic rates at physiological CO2 concentrations. Naturally occurring Rubiscos with superior properties amongst the Triticeae tribe can be exploited to improve wheat photosynthesis and crop productivity. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology.

ROSGREN STREAM TYPES AS A TOOL FOR PREDICTING BEDLOAD AND SUSPENDED SEDIMENT EXPORT IN LOW-ORDER LAKE SUPERIOR WATERSHEDS

EPA Science Inventory

Bedload samples were collected from 48 second and third order Lake Superior tributaries during snowmelt in 1998 and 1999. Suspended sediment samples were collected over a three-year period during baseflow, rain events, and snowmelt. This work was part of a comparative watershed...

Predicting Improvement in Writer's Cramp Symptoms following Botulinum Neurotoxin Injection Therapy.

PubMed

Jackman, Mallory; Delrobaei, Mehdi; Rahimi, Fariborz; Atashzar, S Farokh; Shahbazi, Mahya; Patel, Rajni; Jog, Mandar

2016-01-01

Writer's cramp is a specific focal hand dystonia causing abnormal posturing and tremor in the upper limb. The most popular medical intervention, botulinum neurotoxin type A (BoNT-A) therapy, is variably effective for 50-70% of patients. BoNT-A non-responders undergo ineffective treatment and may experience significant side effects. Various assessments have been used to determine response prediction to BoNT-A, but not in the same population of patients. A comprehensive assessment was employed to measure various symptom aspects. Clinical scales, full upper-limb kinematic measures, self-report, and task performance measures were assessed for nine writer's cramp patients at baseline. Patients received two BoNT-A injections then were classified as responders or non-responders based on a quantified self-report measure. Baseline scores were compared between groups, across all measures, to determine which scores predicted a positive BoNT-A response. Five of nine patients were responders. No kinematic measures were predictably different between groups. Analyses revealed three features that predicted a favorable response and separated the two groups: higher than average cramp severity and cramp frequency, and below average cramp latency. Non-kinematic measures appear to be superior in making such predictions. Specifically, measures of cramp severity, frequency, and latency during performance of a specific set of writing and drawing tasks were predictive factors. Since kinematic was not used to determine the injection pattern and the injections were visually guided, it may still be possible to use individual patient kinematics for better outcomes.

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.

Running biomechanics: shorter heels, better economy.

PubMed

Scholz, M N; Bobbert, M F; van Soest, A J; Clark, J R; van Heerden, J

2008-10-01

Better running economy (i.e. a lower rate of energy consumption at a given speed) is correlated with superior distance running performance. There is substantial variation in running economy, even among elite runners. This variation might be due to variation in the storage and reutilization of elastic energy in tendons. Using a simple musculoskeletal model, it was predicted that the amount of energy stored in a tendon during a given movement depends more critically on moment arm than on mechanical properties of the tendon, with the amount of stored energy increasing as the moment arm gets smaller. Assuming a link between elastic energy reutilization and overall metabolic cost of running, a smaller moment arm should therefore be associated with superior running economy. This prediction was confirmed experimentally in a group of 15 highly trained runners. The moment arm of the Achilles tendon was determined from standardized photographs of the ankle, using the position of anatomical landmarks. Running economy was measured as the rate of metabolic energy consumption during level treadmill running at a speed of 16 km h(-1). A strong correlation was found between the moment arm of the Achilles tendon and running economy. Smaller muscle moment arms correlated with lower rates of metabolic energy consumption (r(2)=0.75, P<0.001).

Short-arc measurement and fitting based on the bidirectional prediction of observed data

NASA Astrophysics Data System (ADS)

Fei, Zhigen; Xu, Xiaojie; Georgiadis, Anthimos

2016-02-01

To measure a short arc is a notoriously difficult problem. In this study, the bidirectional prediction method based on the Radial Basis Function Neural Network (RBFNN) to the observed data distributed along a short arc is proposed to increase the corresponding arc length, and thus improve its fitting accuracy. Firstly, the rationality of regarding observed data as a time series is discussed in accordance with the definition of a time series. Secondly, the RBFNN is constructed to predict the observed data where the interpolation method is used for enlarging the size of training examples in order to improve the learning accuracy of the RBFNN’s parameters. Finally, in the numerical simulation section, we focus on simulating how the size of the training sample and noise level influence the learning error and prediction error of the built RBFNN. Typically, the observed data coming from a 5{}^\\circ short arc are used to evaluate the performance of the Hyper method known as the ‘unbiased fitting method of circle’ with a different noise level before and after prediction. A number of simulation experiments reveal that the fitting stability and accuracy of the Hyper method after prediction are far superior to the ones before prediction.

Experimental Study on the Flexural Performance of Parallel Strand Bamboo Beams

PubMed Central

Zhou, Aiping; Bian, Yuling

2014-01-01

Searching for materials to provide proper housing with less emission and low energy becomes an urgent demand with the ever-growing population. Bamboo has gained a reputation as an ecofriendly, highly renewable source of material. Parallel Strand Bamboo (PSB) is a new biocomposite made of bamboo strips which has superiority performances than wood products. It has attracted considerable interests as a sustainable alternative for more traditional building materials. But the mechanical performance study of PSB as construction materials is still inadequate. Also, the structural behavior of PSB is not quite understood as conventional construction materials, which results in the difficulties to predict the performances of PSB structural members. To achieve this purpose, 4-point bending experiments for PSB beams were carried out. The flexural performances, mode of failure in bending, and the damage mechanism of PSB beams were investigated in this paper. PMID:24701141

Investigating enhanced thermoelectric performance of graphene-based nano-structures.

PubMed

Hossain, Md Sharafat; Huynh, Duc Hau; Jiang, Liming; Rahman, Sharmin; Nguyen, Phuong Duc; Al-Dirini, Feras; Hossain, Faruque; Bahk, Je-Hyeong; Skafidas, Efstratios

2018-03-08

Recently, it has been demonstrated that graphene nano-ribbons (GNRs) exhibit superior thermoelectric performance compared to graphene sheets. However, the underlying mechanism behind this enhancement has not been systematically investigated and significant opportunity remains for further enhancement of the thermoelectric performance of GNRs by optimizing their charge carrier concentration. In this work, we modulate the carrier concentration of graphene-based nano-structures using a gate voltage and investigate the resulting carrier-concentration-dependent thermoelectric parameters using the Boltzmann transport equations. We investigate the effect of energy dependent scattering time and the role of substrate-induced charge carrier fluctuation in optimizing the Seebeck coefficient and power factor. Our approach predicts the scattering mechanism and the extent of the charge carrier fluctuation in different samples and explains the enhancement of thermoelectric performance of GNR samples. Subsequently, we propose a route towards the enhancement of thermoelectric performance of graphene-based devices which can also be applied to other two-dimensional materials.

Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

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

Miltiadis Alamaniotis; Vivek Agarwal

This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are thenmore » inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.« less

Evaluation of image features and classification methods for Barrett's cancer detection using VLE imaging

NASA Astrophysics Data System (ADS)

Klomp, Sander; van der Sommen, Fons; Swager, Anne-Fré; Zinger, Svitlana; Schoon, Erik J.; Curvers, Wouter L.; Bergman, Jacques J.; de With, Peter H. N.

2017-03-01

Volumetric Laser Endomicroscopy (VLE) is a promising technique for the detection of early neoplasia in Barrett's Esophagus (BE). VLE generates hundreds of high resolution, grayscale, cross-sectional images of the esophagus. However, at present, classifying these images is a time consuming and cumbersome effort performed by an expert using a clinical prediction model. This paper explores the feasibility of using computer vision techniques to accurately predict the presence of dysplastic tissue in VLE BE images. Our contribution is threefold. First, a benchmarking is performed for widely applied machine learning techniques and feature extraction methods. Second, three new features based on the clinical detection model are proposed, having superior classification accuracy and speed, compared to earlier work. Third, we evaluate automated parameter tuning by applying simple grid search and feature selection methods. The results are evaluated on a clinically validated dataset of 30 dysplastic and 30 non-dysplastic VLE images. Optimal classification accuracy is obtained by applying a support vector machine and using our modified Haralick features and optimal image cropping, obtaining an area under the receiver operating characteristic of 0.95 compared to the clinical prediction model at 0.81. Optimal execution time is achieved using a proposed mean and median feature, which is extracted at least factor 2.5 faster than alternative features with comparable performance.

Language, aging, and cognition: frontal aslant tract and superior longitudinal fasciculus contribute toward working memory performance in older adults.

PubMed

Rizio, Avery A; Diaz, Michele T

2016-06-15

Previous research has documented change in white matter tract integrity with increasing age. Both interhemispheric and intrahemispheric tracts that underlie language processing are susceptible to these age-related changes. The aim of the current study was to explore age and white matter integrity in language-related tracts as predictors of cognitive task performance in younger and older adults. To this end, we carried out principal component analyses of white matter tracts and confirmatory factor analysis of neuropsychological measures. We next carried out a series of regression analyses that used white matter components to predict scores on each of the neuropsychological components. For both younger and older adults, age was a significant predictor of processing speed and working memory. However, white matter integrity did not contribute independently toward these models. In older adults only, both age and a white matter component that included the bilateral frontal aslant tract and left superior longitudinal fasciculus were significant predictors of working memory. Taken together, these results extend our understanding of the contributions of language-related white matter structure to cognitive processing and highlight the effects of age-related differences in both frontal and dorsal tracts.

Comparison of continuous and discontinuous collisional bumpers: Dimensionally scaled impact experiments into single wire meshes

NASA Technical Reports Server (NTRS)

Hoerz, Friedrich; Cintala, Mark; See, Thomas; Bernhard, Ronald; Cardenas, Frank; Davidson, William; Haynes, Jerry

1992-01-01

An experimental inquiry into the utility of discontinuous bumpers was conducted to investigate the collisional outcomes of impacts into single grid-like targets and to compare the results with more traditional bumper designs that employ continuous sheet stock. We performed some 35 experiments using 6.3 and 3.2 mm diameter spherical soda-lime glass projectiles at low velocities (less than 2.5 km/s) and 13 at velocities between 5 and 6 km/s, using 3.2 mm spheres only. The thrust of the experiments related to the characterization of collisional fragments as a function of target thickness or areal shield mass of both bumper designs. The primary product of these experiments was witness plates that record the resulting population of collisional fragments. Substantial interpretive and predictive insights into bumper performance were obtained. All qualitative observations (on the witness plates) and detailed measurements of displaced masses seem simply and consistently related only to bumper mass available for interaction with the impactor. This renders the grid bumper into the superior shield design. These findings present evidence that discontinuous bumpers are a viable concept for collisional shields, possibly superior to continuous geometries.

The EPOS-CC Score: An Integration of Independent, Tumor- and Patient-Associated Risk Factors to Predict 5-years Overall Survival Following Colorectal Cancer Surgery.

PubMed

Haga, Yoshio; Ikejiri, Koji; Wada, Yasuo; Ikenaga, Masakazu; Koike, Shoichiro; Nakamura, Seiji; Koseki, Masato

2015-06-01

Surgical audit is an essential task for the estimation of postoperative outcome and comparison of quality of care. Previous studies on surgical audits focused on short-term outcomes, such as postoperative mortality. We propose a surgical audit evaluating long-term outcome following colorectal cancer surgery. The predictive model for this audit is designated as 'Estimation of Postoperative Overall Survival for Colorectal Cancer (EPOS-CC)'. Thirty-one tumor-related and physiological variables were prospectively collected in 889 patients undergoing elective resection for colorectal cancer between April 2005 and April 2007 in 16 Japanese hospitals. Postoperative overall survival was assessed over a 5-years period. The EPOS-CC score was established by selecting significant variables in a uni- and multivariate analysis and allocating a risk-adjusted multiplication factor to each variable using Cox regression analysis. For validation, the EPOS-CC score was compared to the predictive power of UICC stage. Inter-hospital variability of the observed-to-estimated 5-years survival was assessed to estimate quality of care. Among the 889 patients, 804 (90%) completed the 5-years follow-up. Univariate analysis displayed a significant correlation with 5-years survival for 14 physiological and nine tumor-related variables (p < 0.005). Highly significant p-values below 0.0001 were found for age, ASA score, severe pulmonary disease, respiratory history, performance status, hypoalbuminemia, alteration of hemoglobin, serum sodium level, and for all histological variables except tumor location. Age, TNM stage, lymphatic invasion, performance status, and serum sodium level were independent variables in the multivariate analysis and were entered the EPOS-CC model for the prediction of survival. Risk-adjusted multiplication factors between 1.5 (distant metastasis) and 0.16 (serum sodium level) were accorded to the different variables. The predictive power of EPOS-CC was superior to the one of UICC stage; area under the curve 0.87, 95% CI 0.85-0.90 for EPOS-CC, and 0.80, 0.76-0.83 for UICC stage, p < 0.001. Quality of care did not differ between hospitals. The EPOS-CC score including the independent variables age, performance status, serum sodium level, TNM stage, and lymphatic invasion is superior to the UICC stage in the prediction of 5-years overall survival. This higher accuracy might be explained by the inclusion of physiological factors, thus also taking non-tumor-associated deaths into account. Furthermore, EPOS-CC score may compare quality of care among different institutions. Future studies are necessary to further evaluate this score and help improving the prediction of long-term survival following colorectal cancer surgery.

Multi-model ensemble hydrologic prediction using Bayesian model averaging

NASA Astrophysics Data System (ADS)

Duan, Qingyun; Ajami, Newsha K.; Gao, Xiaogang; Sorooshian, Soroosh

2007-05-01

Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box-Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of daily root mean square error (DRMS) and daily absolute mean error (DABS) is generally superior to that of the best individual predictions. Furthermore, the BMA predictions employing multiple sets of weights are generally better than those using single set of weights.

Dual Energy X-Ray Absorptiometry Compared with Anthropometry in Relation to Cardio-Metabolic Risk Factors in a Young Adult Population: Is the ‘Gold Standard’ Tarnished?

PubMed Central

Hands, Beth; Pennell, Craig E.; Lye, Stephen J.; Mountain, Jennifer A.

2016-01-01

Background and Aims Assessment of adiposity using dual energy x-ray absorptiometry (DXA) has been considered more advantageous in comparison to anthropometry for predicting cardio-metabolic risk in the older population, by virtue of its ability to distinguish total and regional fat. Nonetheless, there is increasing uncertainty regarding the relative superiority of DXA and little comparative data exist in young adults. This study aimed to identify which measure of adiposity determined by either DXA or anthropometry is optimal within a range of cardio-metabolic risk factors in young adults. Methods and Results 1138 adults aged 20 years were assessed by DXA and standard anthropometry from the Western Australian Pregnancy Cohort (Raine) Study. Cross-sectional linear regression analyses were performed. Waist to height ratio was superior to any DXA measure with HDL-C. BMI was the superior model in relation to blood pressure than any DXA measure. Midriff fat mass (DXA) and waist circumference were comparable in relation to glucose. For all the other cardio-metabolic variables, anthropometric and DXA measures were comparable. DXA midriff fat mass compared with BMI or waist hip ratio was the superior measure for triglycerides, insulin and HOMA-IR. Conclusion Although midriff fat mass (measured by DXA) was the superior measure with insulin sensitivity and triglycerides, the anthropometric measures were better or equal with various DXA measures for majority of the cardio-metabolic risk factors. Our findings suggest, clinical anthropometry is generally as useful as DXA in the evaluation of the individual cardio-metabolic risk factors in young adults. PMID:27622523

Scanning laser polarimetry, but not optical coherence tomography predicts permanent visual field loss in acute nonarteritic anterior ischemic optic neuropathy.

PubMed

Kupersmith, Mark J; Anderson, Susan; Durbin, Mary; Kardon, Randy

2013-08-15

Scanning laser polarimetry (SLP) reveals abnormal retardance of birefringence in locations of the edematous peripapillary retinal nerve fiber layer (RNFL), which appear thickened by optical coherence tomography (OCT), in nonarteritic anterior ischemic optic neuropathy (NAION). We hypothesize initial sector SLP RNFL abnormalities will correlate with long-term regional visual field loss due to ischemic injury. We prospectively performed automated perimetry, SLP, and high definition OCT (HD-OCT) of the RNFL in 25 eyes with acute NAION. We grouped visual field threshold and RNFL values into Garway-Heath inferior/superior disc sectors and corresponding superior/inferior field regions. We compared sector SLP RNFL thickness with corresponding visual field values at presentation and at >3 months. At presentation, 12 eyes had superior sector SLP reduction, 11 of which had inferior field loss. Six eyes, all with superior field loss, had inferior sector SLP reduction. No eyes had reduced OCT-derived RNFL acutely. Eyes with abnormal field regions had corresponding SLP sectors thinner (P = 0.003) than for sectors with normal field regions. During the acute phase, the SLP-derived sector correlated with presentation (r = 0.59, P = 0.02) and with >3-month after presentation (r = 0.44, P = 0.02) corresponding superior and inferior field thresholds. Abnormal RNFL birefringence occurs in sectors corresponding to regional visual field loss during acute NAION when OCT-derived RNFL shows thickening. Since the visual field deficits show no significant recovery, SLP can be an early marker for axonal injury, which may be used to assess recovery potential at RNFL locations with respect to new treatments for acute NAION.

Evaluation of hot forming effects mapping for CAE analyses

NASA Astrophysics Data System (ADS)

Knoerr, L.; Faath, T.; Dykeman, J.; Malcolm, S.

2016-08-01

Hot forming has grown significantly in the manufacturing of structural components within the vehicle Body-In-White construction. The superior strength of press hardened steels not only guarantee high resistance to deformation, it also brings a significant weight saving compared to conventional cold formed products. However, the benefit of achieving ultrahigh strength with hot stamping, comes with a reduction in ductility of the press hardened part. This will require advanced material modeling to capture the predicted performances accurately. A technique to optically measure and map the thinning distribution after hot stamping has shown to improve numerical analysis for fracture prediction. The proposed method to determine the forming effects and mapping to CAE models can be integrated into the Vehicle Development Process to shorten the time to production.

Prediction of solvation enthalpy of gaseous organic compounds in propanol

NASA Astrophysics Data System (ADS)

Golmohammadi, Hassan; Dashtbozorgi, Zahra

2016-09-01

The purpose of this paper is to present a novel way for developing quantitative structure-property relationship (QSPR) models to predict the gas-to-propanol solvation enthalpy (Δ H solv) of 95 organic compounds. Different kinds of descriptors were calculated for each compound using the Dragon software package. The variable selection technique of replacement method (RM) was employed to select the optimal subset of solute descriptors. Our investigation reveals that the dependence of physical chemistry properties of solution on solvation enthalpy is nonlinear and that the RM method is unable to model the solvation enthalpy accurately. The results established that the calculated Δ H solv values by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by RM model.

Impact of donor- and collection-related variables on product quality in ex utero cord blood banking.

PubMed

Askari, Sabeen; Miller, John; Chrysler, Gayl; McCullough, Jeffrey

2005-02-01

Optimizing product quality is a current focus in cord blood banking. This study evaluates the role of selected donor- and collection-related variables. Retrospective review was performed of cord blood units (CBUs) collected ex utero between February 1, 2000, and February 28, 2002. Preprocessing volume and total nucleated cell (TNC) counts and postprocessing CD34 cell counts were used as product quality indicators. Of 2084 CBUs, volume determinations and TNC counts were performed on 1628 and CD34+ counts on 1124 CBUs. Mean volume and TNC and CD34+ counts were 85.2 mL, 118.9 x 10(7), and 5.2 x 10(6), respectively. In univariate analysis, placental weight of greater than 500 g and meconium in amniotic fluid correlated with better volume and TNC and CD34+ counts. Greater than 40 weeks' gestation predicted enhanced volume and TNC count. Cesarean section, two- versus one-person collection, and not greater than 5 minutes between placental delivery and collection produced superior volume. Increased TNC count was also seen in Caucasian women, primigravidae, female newborns, and collection duration of more than 5 minutes. A time between delivery of newborn and placenta of not greater than 10 minutes predicted better volume and CD34+ count. By regression analysis, collection within not greater than 5 minutes of placental delivery produced superior volume and TNC count. Donor selection and collection technique modifications may improve product quality. TNC count appears to be more affected by different variables than CD34+ count.

Superior Prognostic Value of Cumulative Intracranial Tumor Volume Relative to Largest Intracranial Tumor Volume for Stereotactic Radiosurgery-Treated Brain Metastasis Patients.

PubMed

Hirshman, Brian R; Wilson, Bayard; Ali, Mir Amaan; Proudfoot, James A; Koiso, Takao; Nagano, Osamu; Carter, Bob S; Serizawa, Toru; Yamamoto, Masaaki; Chen, Clark C

2018-04-01

Two intracranial tumor volume variables have been shown to prognosticate survival of stereotactic-radiosurgery-treated brain metastasis patients: the largest intracranial tumor volume (LITV) and the cumulative intracranial tumor volume (CITV). To determine whether the prognostic value of the Scored Index for Radiosurgery (SIR) model can be improved by replacing one of its components-LITV-with CITV. We compared LITV and CITV in terms of their survival prognostication using a series of multivariable models that included known components of the SIR: age, Karnofsky Performance Score, status of extracranial disease, and the number of brain metastases. Models were compared using established statistical measures, including the net reclassification improvement (NRI > 0) and integrated discrimination improvement (IDI). The analysis was performed in 2 independent cohorts, each consisting of ∼3000 patients. In both cohorts, CITV was shown to be independently predictive of patient survival. Replacement of LITV with CITV in the SIR model improved the model's ability to predict 1-yr survival. In the first cohort, the CITV model showed an NRI > 0 improvement of 0.2574 (95% confidence interval [CI] 0.1890-0.3257) and IDI of 0.0088 (95% CI 0.0057-0.0119) relative to the LITV model. In the second cohort, the CITV model showed a NRI > 0 of 0.2604 (95% CI 0.1796-0.3411) and IDI of 0.0051 (95% CI 0.0029-0.0073) relative to the LITV model. After accounting for covariates within the SIR model, CITV offers superior prognostic value relative to LITV for stereotactic radiosurgery-treated brain metastasis patients.

Minimally invasive mitral valve surgery expands the surgical options for high-risks patients.

PubMed

Petracek, Michael R; Leacche, Marzia; Solenkova, Natalia; Umakanthan, Ramanan; Ahmad, Rashid M; Ball, Stephen K; Hoff, Steven J; Absi, Tarek S; Balaguer, Jorge M; Byrne, John G

2011-10-01

A simplified minimally invasive mitral valve surgery (MIMVS) approach avoiding cross-clamping and cardioplegic myocardial arrest using a small (5 cm) right antero-lateral incision was developed. We hypothesized that, in high-risk patients and in patients with prior sternotomy, this approach would yield superior results compared to those predicted by the Society of Thoracic Surgeons (STS) algorithm for standard median sternotomy mitral valve surgery. Five hundred and four consecutive patients (249 males/255 females), median age 65 years (range 20-92 years) underwent MIMVS between 1/06 and 8/09. Median preoperative New York Heart Association function class was 3 (range 1-4). Eighty-two (16%) patients had an ejection fraction ≤35%. Forty-seven (9%) had a STS predicted mortality ≥10%. Under cold fibrillatory arrest (median temperature 28°C) without aortic cross-clamp, mitral valve repair (224/504, 44%) or replacement (280/504, 56%) was performed. Thirty-day mortality for the entire cohort was 2.2% (11/504). In patients with a STS predicted mortality ≥ 10% (range 10%-67%), the observed 30-day mortality was 4% (2/47), lower than the mean STS predicted mortality of 20%. Morbidity in this high-risk group was equally low: 1 of 47 (2%) patients underwent reexploration for bleeding, 1 of 47 (2%) patients suffered a permanent neurologic deficit, none had wound infection. The median length of stay was 8 days (range 1-68 days). This study demonstrates that MIMVS without aortic cross-clamp is reproducible with low mortality and morbidity rates. This approach expands the surgical options for high-risk patients and yields to superior results than the conventional median sternotomy approach.

Liver transplantation for hepatocellular carcinoma: evaluation of the alpha-fetoprotein model in a multicenter cohort from Latin America.

PubMed

Piñero, Federico; Tisi Baña, Matías; de Ataide, Elaine Cristina; Hoyos Duque, Sergio; Marciano, Sebastian; Varón, Adriana; Anders, Margarita; Zerega, Alina; Menéndez, Josemaría; Zapata, Rodrigo; Muñoz, Linda; Padilla Machaca, Martín; Soza, Alejandro; McCormack, Lucas; Poniachik, Jaime; Podestá, Luis G; Gadano, Adrian; Boin, Ilka S F Fatima; Duvoux, Christophe; Silva, Marcelo

2016-11-01

The French alpha-fetoprotein (AFP) model has recently shown superior results compared to Milan criteria (MC) for prediction of hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) in European populations. The aim of this study was to explore the predictive capacity of the AFP model for HCC recurrence in a Latin-American cohort. Three hundred twenty-seven patients with HCC were included from a total of 2018 patients transplanted at 15 centres. Serum AFP and imaging data were both recorded at listing. Predictability was assessed by the Net Reclassification Improvement (NRI) method. Overall, 82 and 79% of the patients were within MC and the AFP model respectively. NRI showed a superior predictability of the AFP model against MC. Patients with an AFP score >2 points had higher risk of recurrence at 5 years Hazard Ratio (HR) of 3.15 (P = 0.0001) and lower patient survival (HR = 1.51; P = 0.03). Among patients exceeding MC, a score ≤2 points identified a subgroup of patients with lower recurrence (5% vs 42%; P = 0.013) and higher survival rates (84% vs 45%; P = 0.038). In cases treated with bridging procedures, following restaging, a score >2 points identified a higher recurrence (HR 2.2, P = 0.12) and lower survival rate (HR 2.25, P = 0.03). A comparative analysis between HBV and non-HBV patients showed that the AFP model performed better in non-HBV patients. The AFP model could be useful in Latin-American countries to better select patients for LT in subgroups presenting with extended criteria. However, particular attention should be focused on patients with HBV. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Prognostic and Pathogenetic Value of Combining Clinical and Biochemical Indices in Patients With Acute Lung Injury

PubMed Central

Koyama, Tatsuki; Billheimer, D. Dean; Wu, William; Bernard, Gordon R.; Thompson, B. Taylor; Brower, Roy G.; Standiford, Theodore J.; Martin, Thomas R.; Matthay, Michael A.

2010-01-01

Background: No single clinical or biologic marker reliably predicts clinical outcomes in acute lung injury (ALI)/ARDS. We hypothesized that a combination of biologic and clinical markers would be superior to either biomarkers or clinical factors alone in predicting ALI/ARDS mortality and would provide insight into the pathogenesis of clinical ALI/ARDS. Methods: Eight biologic markers that reflect endothelial and epithelial injury, inflammation, and coagulation (von Willebrand factor antigen, surfactant protein D [SP-D]), tumor necrosis factor receptor-1, interleukin [IL]-6, IL-8, intercellular adhesion molecule-1, protein C, plasminogen activator inhibitor-1) were measured in baseline plasma from 549 patients in the ARDSNet trial of low vs high positive end-expiratory pressure. Mortality was modeled with multivariable logistic regression. Predictors were selected using backward elimination. Comparisons between candidate models were based on the receiver operating characteristics (ROC) and tests of integrated discrimination improvement. Results: Clinical predictors (Acute Physiology And Chronic Health Evaluation III [APACHE III], organ failures, age, underlying cause, alveolar-arterial oxygen gradient, plateau pressure) predicted mortality with an area under the ROC curve (AUC) of 0.82; a combination of eight biomarkers and the clinical predictors had an AUC of 0.85. The best performing biomarkers were the neutrophil chemotactic factor, IL-8, and SP-D, a product of alveolar type 2 cells, supporting the concept that acute inflammation and alveolar epithelial injury are important pathogenetic pathways in human ALI/ARDS. Conclusions: A combination of biomarkers and clinical predictors is superior to clinical predictors or biomarkers alone for predicting mortality in ALI/ARDS and may be useful for stratifying patients in clinical trials. From a pathogenesis perspective, the degree of acute inflammation and alveolar epithelial injury are highly associated with the outcome of human ALI/ARDS. PMID:19858233

SCREENING FOR PERSONALITY DISORDERS

PubMed Central

Morse, Jennifer Q.; Pilkonis, Paul A.

2010-01-01

A brief but valid self-report measure to screen for personality disorders (PDs) would be a valuable tool in making decisions about further assessment and in planning optimal treatments. In psychiatric and nonpsychiatric samples, we compared the validity of three screening measures: the PD scales from the Inventory of Interpersonal Problems, a self-report version of the Iowa Personality Disorder Screen, and the self-directedness scale of the Temperament and Character Inventory. Despite their different theoretical origins, the screeners were highly correlated in a range from .71 to .77. As a result, the use of multiple screeners was not a significant improvement over any individual screener, and no single screener stood out as clearly superior to the others. Each performed modestly in predicting the presence of any PD diagnosis in both the psychiatric and nonpsychiatric groups. Performance was best when predicting a more severe PD diagnosis in the psychiatric sample. The results also highlight the potential value of multiple assessments when relying on self-reports. PMID:17492920

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

Shimizu, Yu; Yoshimoto, Junichiro; Takamura, Masahiro; Okada, Go; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

Sonographic findings predictive of central lymph node metastasis in patients with papillary thyroid carcinoma: influence of associated chronic lymphocytic thyroiditis on the diagnostic performance of sonography.

PubMed

Yoo, Yeon Hwa; Kim, Jeong-Ah; Son, Eun Ju; Youk, Ji Hyun; Kwak, Jin Young; Kim, Eun-Kyung; Park, Cheong Soo

2013-12-01

To analyze sonographic findings suggesting central lymph node metastasis of papillary thyroid carcinoma and to evaluate the influence of associated chronic lymphocytic thyroiditis on the diagnostic performance of sonography for predicting central lymph node metastasis. A total of 124 patients (101 female and 23 male; mean age, 47.5 years; range, 21-74 years) underwent sonographically guided fine-needle aspiration in central lymph nodes from January 2008 to July 2011. Sonographic features of size, shape, margin, thickening of the cortex, cortical echogenicity, presence of a hilum, cystic changes, calcification, and vascularity of enlarged lymph nodes were analyzed before fine-needle aspiration and classified into 2 categories (probably benign and suspicious). Sonographic findings were correlated with the pathologic diagnosis and associated chronic lymphocytic thyroiditis. Receiver operating characteristic curve analysis was performed to assess the diagnostic performance of sonography for predicting central lymph node metastasis according to the associated thyroiditis. Fifty-one lymph nodes (39.5%) were malignant, and 73 (60.5%) were benign. On univariate analysis, size, shape, margin, cortical thickening, cortical echogenicity, cystic changes, calcification, and vascularity were significantly different between the benign and metastatic nodes (P < .05). On multivariate analysis, eccentric cortical thickening (odds ratio, 26.59; 95% confidence interval [CI], 3.26-216.66) and hyper echogenicity of the cortex (odds ratio, 18.46; 95% CI, 2.44-139.64) were significantly associated with malignant nodes (P < .05). The area under the curve values for sonography for predicting metastasis were 0.756 (95% CI, 0.618-0.894) in chronic lymphocytic thyroiditis-positive patients and 0.971 (95% CI, 0.938-1.000) in negative patients. Eccentric cortical thickening and cortical hyperechogenicity were the sonographic findings predictive of central lymph node metastasis from papillary thyroid carcinoma. The diagnostic performance of sonography for predicting metastasis was superior in chronic lymphocytic thyroiditis-negative patients than in positive patients.

Intelligent Prediction of Fan Rotation Stall in Power Plants Based on Pressure Sensor Data Measured In-Situ

PubMed Central

Xu, Xiaogang; Wang, Songling; Liu, Jinlian; Liu, Xinyu

2014-01-01

Blower and exhaust fans consume over 30% of electricity in a thermal power plant, and faults of these fans due to rotation stalls are one of the most frequent reasons for power plant outage failures. To accurately predict the occurrence of fan rotation stalls, we propose a support vector regression machine (SVRM) model that predicts the fan internal pressures during operation, leaving ample time for rotation stall detection. We train the SVRM model using experimental data samples, and perform pressure data prediction using the trained SVRM model. To prove the feasibility of using the SVRM model for rotation stall prediction, we further process the predicted pressure data via wavelet-transform-based stall detection. By comparison of the detection results from the predicted and measured pressure data, we demonstrate that the SVRM model can accurately predict the fan pressure and guarantee reliable stall detection with a time advance of up to 0.0625 s. This superior pressure data prediction capability leaves significant time for effective control and prevention of fan rotation stall faults. This model has great potential for use in intelligent fan systems with stall prevention capability, which will ensure safe operation and improve the energy efficiency of power plants. PMID:24854057

Biomechanical Effects of Acromioplasty on Superior Capsule Reconstruction for Irreparable Supraspinatus Tendon Tears.

PubMed

Mihata, Teruhisa; McGarry, Michelle H; Kahn, Timothy; Goldberg, Iliya; Neo, Masashi; Lee, Thay Q

2016-01-01

Acromioplasty is increasingly being performed for both reparable and irreparable rotator cuff tears. However, acromioplasty may destroy the coracoacromial arch, including the coracoacromial ligament, consequently causing a deterioration in superior stability even after superior capsule reconstruction. The purpose of this study was to investigate the effects of acromioplasty on shoulder biomechanics after superior capsule reconstruction for irreparable supraspinatus tendon tears. The hypothesis was that acromioplasty with superior capsule reconstruction would decrease the area of subacromial impingement without increasing superior translation and subacromial contact pressure. Controlled laboratory study. Seven fresh-frozen cadaveric shoulders were evaluated using a custom shoulder testing system. Glenohumeral superior translation, the location of the humeral head relative to the glenoid, and subacromial contact pressure and area were compared among 4 conditions: (1) intact shoulder, (2) irreparable supraspinatus tendon tear, (3) superior capsule reconstruction without acromioplasty, and (4) superior capsule reconstruction with acromioplasty. Superior capsule reconstruction was performed using the fascia lata. Compared with the intact shoulder, the creation of an irreparable supraspinatus tear significantly shifted the humeral head superiorly in the balanced muscle loading condition (without superior force applied) (0° of abduction: 2.8-mm superior shift [P = .0005]; 30° of abduction: 1.9-mm superior shift [P = .003]) and increased both superior translation (0° of abduction: 239% of intact [P = .04]; 30° of abduction: 199% of intact [P = .02]) and subacromial peak contact pressure (0° of abduction: 308% of intact [P = .0002]; 30° of abduction: 252% of intact [P = .001]) by applying superior force. Superior capsule reconstruction without acromioplasty significantly decreased superior translation (0° of abduction: 86% of intact [P = .02]; 30° of abduction: 75% of intact [P = .002]) and subacromial peak contact pressure (0° of abduction: 47% of intact [P = .0002]; 30° of abduction: 83% of intact [P = .0005]; 60° of abduction: 38% of intact [P = .04]) compared with after the creation of a supraspinatus tear. Adding acromioplasty significantly decreased the subacromial contact area compared with superior capsule reconstruction without acromioplasty (0° of abduction: 26% decrease [P = .01]; 30° of abduction: 21% decrease [P = .009]; 60° of abduction: 61% decrease [P = .003]) and did not alter humeral head position, superior translation, or subacromial peak contact pressure. Superior capsule reconstruction repositioned the superiorly migrated humeral head and restored superior stability in the shoulder joint. Adding acromioplasty decreased the subacromial contact area without increasing the subacromial contact pressure. When superior capsule reconstruction is performed for irreparable rotator cuff tears, acromioplasty may help to decrease the postoperative risk of abrasion and tearing of the graft beneath the acromion. © 2015 The Author(s).

Genetic prediction of type 2 diabetes using deep neural network.

PubMed

Kim, J; Kim, J; Kwak, M J; Bajaj, M

2018-04-01

Type 2 diabetes (T2DM) has strong heritability but genetic models to explain heritability have been challenging. We tested deep neural network (DNN) to predict T2DM using the nested case-control study of Nurses' Health Study (3326 females, 45.6% T2DM) and Health Professionals Follow-up Study (2502 males, 46.5% T2DM). We selected 96, 214, 399, and 678 single-nucleotide polymorphism (SNPs) through Fisher's exact test and L1-penalized logistic regression. We split each dataset randomly in 4:1 to train prediction models and test their performance. DNN and logistic regressions showed better area under the curve (AUC) of ROC curves than the clinical model when 399 or more SNPs included. DNN was superior than logistic regressions in AUC with 399 or more SNPs in male and 678 SNPs in female. Addition of clinical factors consistently increased AUC of DNN but failed to improve logistic regressions with 214 or more SNPs. In conclusion, we show that DNN can be a versatile tool to predict T2DM incorporating large numbers of SNPs and clinical information. Limitations include a relatively small number of the subjects mostly of European ethnicity. Further studies are warranted to confirm and improve performance of genetic prediction models using DNN in different ethnic groups. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

PubMed

Cai, Binghuang; Jiang, Xia

2014-04-01

Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well. Copyright © 2013 Elsevier Inc. All rights reserved.

PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection

PubMed Central

2013-01-01

Background Assessment of potential allergenicity of protein is necessary whenever transgenic proteins are introduced into the food chain. Bioinformatics approaches in allergen prediction have evolved appreciably in recent years to increase sophistication and performance. However, what are the critical features for protein's allergenicity have been not fully investigated yet. Results We presented a more comprehensive model in 128 features space for allergenic proteins prediction by integrating various properties of proteins, such as biochemical and physicochemical properties, sequential features and subcellular locations. The overall accuracy in the cross-validation reached 93.42% to 100% with our new method. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) procedure were applied to obtain which features are essential for allergenicity. Results of the performance comparisons showed the superior of our method to the existing methods used widely. More importantly, it was observed that the features of subcellular locations and amino acid composition played major roles in determining the allergenicity of proteins, particularly extracellular/cell surface and vacuole of the subcellular locations for wheat and soybean. To facilitate the allergen prediction, we implemented our computational method in a web application, which can be available at http://gmobl.sjtu.edu.cn/PREAL/index.php. Conclusions Our new approach could improve the accuracy of allergen prediction. And the findings may provide novel insights for the mechanism of allergies. PMID:24565053

A grammar inference approach for predicting kinase specific phosphorylation sites.

PubMed

Datta, Sutapa; Mukhopadhyay, Subhasis

2015-01-01

Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner.

A two-model hydrologic ensemble prediction of hydrograph: case study from the upper Nysa Klodzka river basin (SW Poland)

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej

2016-04-01

The HydroProg system has been elaborated in frame of the research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland and is steadily producing multimodel ensemble predictions of hydrograph in real time. Although there are six ensemble members available at present, the longest record of predictions and their statistics is available for two data-based models (uni- and multivariate autoregressive models). Thus, we consider 3-hour predictions of water levels, with lead times ranging from 15 to 180 minutes, computed every 15 minutes since August 2013 for the Nysa Klodzka basin (SW Poland) using the two approaches and their two-model ensemble. Since the launch of the HydroProg system there have been 12 high flow episodes, and the objective of this work is to present the performance of the two-model ensemble in the process of forecasting these events. For a sake of brevity, we limit our investigation to a single gauge located at the Nysa Klodzka river in the town of Klodzko, which is centrally located in the studied basin. We identified certain regular scenarios of how the models perform in predicting the high flows in Klodzko. At the initial phase of the high flow, well before the rising limb of hydrograph, the two-model ensemble is found to provide the most skilful prognoses of water levels. However, while forecasting the rising limb of hydrograph, either the two-model solution or the vector autoregressive model offers the best predictive performance. In addition, it is hypothesized that along with the development of the rising limb phase, the vector autoregression becomes the most skilful approach amongst the scrutinized ones. Our simple two-model exercise confirms that multimodel hydrologic ensemble predictions cannot be treated as universal solutions suitable for forecasting the entire high flow event, but their superior performance may hold only for certain phases of a high flow.

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

PubMed

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

2017-03-01

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

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

PubMed Central

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

2017-01-01

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

Comparison of three scoring systems for risk stratification in elderly patients wıth acute upper gastrointestinal bleeding.

PubMed

Kalkan, Çağdaş; Soykan, Irfan; Karakaya, Fatih; Tüzün, Ali; Gençtürk, Zeynep Bıyıklı

2017-04-01

Acute gastrointestinal bleeding is a potentially life-threatening condition that requires rapid assessment and dynamic management. Several scoring systems are used to predict mortality and rebleeding in such cases. The aim of the present study was to compare three scoring systems for predicting short-term mortality, rebleeding, duration of hospitalization and the need for blood transfusion in elderly patients with upper gastrointestinal bleeding. The present study included 335 elderly patients with upper gastrointestinal bleeding. Pre- and post-endoscopic Rockall, Glasgow-Blatchford and AIMS65 scores were calculated. The ability of these scores to predict rebleeding, mortality, duration of hospitalization and the need for blood transfusion was determined. Pre- (4.5) and post-endoscopic (7.5) Rockall scores were superior to the Glasgow-Blatchford (12.5) score for predicting mortality (P = 0.006 and P = 0.015). Likewise, pre- (4.5) and post-endoscopic Rockall scores were superior to the respective Glasgow-Blatchford scores for predicting rebleeding (P = 0.013 and P = 0.03). There was an association between duration of hospitalization and mortality; as the duration of hospitalization increased the mortality rate increased. In all, 94% of patients hospitalized for a mean of 5 days were alive versus 56.1% of those hospitalized for 20 days, and 20.2% of those hospitalized for 40 days. In elderly patients with upper gastrointestinal bleeding, the Rockall score is clinically more useful for predicting mortality and rebleeding than the Glasgow-Blatchford and AIMS65 scores; however, for predicting duration of hospitalization and the need for blood transfusion, the Glasgow-Blatchford score is superior to the Rockall and AIMS65 scores. Geriatr Gerontol Int 2017; 17: 575-583. © 2016 Japan Geriatrics Society.

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

The Science and Practice of Self-Control.

PubMed

Duckworth, Angela L; Seligman, Martin E P

2017-09-01

In 2005, we discovered that self-control "outdoes" talent in predicting academic success during adolescence. Since then, a surfeit of longitudinal evidence has affirmed the importance of self-control to achieving everyday goals that conflict with momentary temptations. In parallel, research that has "lumped" self-control with other facets of Big Five conscientiousness has shown the superior predictive power of this broad family of individual differences for diverse life outcomes. Self-control can also be "split" from related traits that in certain contexts demonstrate superior predictive power for achievement. Most important, both the "lumping" and "splitting" traditions have enhanced our understanding of the underlying mechanisms and antecedents of self-control. Collectively, progress over the past decade and a half suggests a bright future for the science and practice of self-control.

PhyloGibbs-MP: Module Prediction and Discriminative Motif-Finding by Gibbs Sampling

PubMed Central

Siddharthan, Rahul

2008-01-01

PhyloGibbs, our recent Gibbs-sampling motif-finder, takes phylogeny into account in detecting binding sites for transcription factors in DNA and assigns posterior probabilities to its predictions obtained by sampling the entire configuration space. Here, in an extension called PhyloGibbs-MP, we widen the scope of the program, addressing two major problems in computational regulatory genomics. First, PhyloGibbs-MP can localise predictions to small, undetermined regions of a large input sequence, thus effectively predicting cis-regulatory modules (CRMs) ab initio while simultaneously predicting binding sites in those modules—tasks that are usually done by two separate programs. PhyloGibbs-MP's performance at such ab initio CRM prediction is comparable with or superior to dedicated module-prediction software that use prior knowledge of previously characterised transcription factors. Second, PhyloGibbs-MP can predict motifs that differentiate between two (or more) different groups of regulatory regions, that is, motifs that occur preferentially in one group over the others. While other “discriminative motif-finders” have been published in the literature, PhyloGibbs-MP's implementation has some unique features and flexibility. Benchmarks on synthetic and actual genomic data show that this algorithm is successful at enhancing predictions of differentiating sites and suppressing predictions of common sites and compares with or outperforms other discriminative motif-finders on actual genomic data. Additional enhancements include significant performance and speed improvements, the ability to use “informative priors” on known transcription factors, and the ability to output annotations in a format that can be visualised with the Generic Genome Browser. In stand-alone motif-finding, PhyloGibbs-MP remains competitive, outperforming PhyloGibbs-1.0 and other programs on benchmark data. PMID:18769735

Differential contributions of the superior and inferior parietal cortex to feedback versus feedforward control of tools.

PubMed

Macuga, Kristen L; Frey, Scott H

2014-05-15

Damage to the superior and/or inferior parietal lobules (SPL, IPL) (Sirigu et al., 1996) or cerebellum (Grealy and Lee, 2011) can selectively disrupt motor imagery, motivating the hypothesis that these regions participate in predictive (i.e., feedforward) control. If so, then the SPL, IPL, and cerebellum should show greater activity as the demands on feedforward control increase from visually-guided execution (closed-loop) to execution without visual feedback (open-loop) to motor imagery. Using fMRI and a Fitts' reciprocal aiming task with tools directed at targets in far space, we found that the SPL and cerebellum exhibited greater activity during closed-loop control. Conversely, open-loop and imagery conditions were associated with increased activity within the IPL and prefrontal areas. These results are consistent with a superior-to-inferior gradient in the representation of feedback-to-feedforward control within the posterior parietal cortex. Additionally, the anterior SPL displayed greater activity when aiming movements were performed with a stick vs. laser pointer. This may suggest that it is involved in the remapping of far into near (reachable) space (Maravita and Iriki, 2004), or in distalization of the end-effector from hand to stick (Arbib et al., 2009). Copyright © 2014 Elsevier Inc. All rights reserved.

A link prediction method for heterogeneous networks based on BP neural network

NASA Astrophysics Data System (ADS)

Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

2018-04-01

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

IMHOTEP—a composite score integrating popular tools for predicting the functional consequences of non-synonymous sequence variants

PubMed Central

Knecht, Carolin; Mort, Matthew; Junge, Olaf; Cooper, David N.; Krawczak, Michael

2017-01-01

Abstract The in silico prediction of the functional consequences of mutations is an important goal of human pathogenetics. However, bioinformatic tools that classify mutations according to their functionality employ different algorithms so that predictions may vary markedly between tools. We therefore integrated nine popular prediction tools (PolyPhen-2, SNPs&GO, MutPred, SIFT, MutationTaster2, Mutation Assessor and FATHMM as well as conservation-based Grantham Score and PhyloP) into a single predictor. The optimal combination of these tools was selected by means of a wide range of statistical modeling techniques, drawing upon 10 029 disease-causing single nucleotide variants (SNVs) from Human Gene Mutation Database and 10 002 putatively ‘benign’ non-synonymous SNVs from UCSC. Predictive performance was found to be markedly improved by model-based integration, whilst maximum predictive capability was obtained with either random forest, decision tree or logistic regression analysis. A combination of PolyPhen-2, SNPs&GO, MutPred, MutationTaster2 and FATHMM was found to perform as well as all tools combined. Comparison of our approach with other integrative approaches such as Condel, CoVEC, CAROL, CADD, MetaSVM and MetaLR using an independent validation dataset, revealed the superiority of our newly proposed integrative approach. An online implementation of this approach, IMHOTEP (‘Integrating Molecular Heuristics and Other Tools for Effect Prediction’), is provided at http://www.uni-kiel.de/medinfo/cgi-bin/predictor/. PMID:28180317

Closed-loop control of artificial pancreatic Beta -cell in type 1 diabetes mellitus using model predictive iterative learning control.

PubMed

Wang, Youqing; Dassau, Eyal; Doyle, Francis J

2010-02-01

A novel combination of iterative learning control (ILC) and model predictive control (MPC), referred to here as model predictive iterative learning control (MPILC), is proposed for glycemic control in type 1 diabetes mellitus. MPILC exploits two key factors: frequent glucose readings made possible by continuous glucose monitoring technology; and the repetitive nature of glucose-meal-insulin dynamics with a 24-h cycle. The proposed algorithm can learn from an individual's lifestyle, allowing the control performance to be improved from day to day. After less than 10 days, the blood glucose concentrations can be kept within a range of 90-170 mg/dL. Generally, control performance under MPILC is better than that under MPC. The proposed methodology is robust to random variations in meal timings within +/-60 min or meal amounts within +/-75% of the nominal value, which validates MPILC's superior robustness compared to run-to-run control. Moreover, to further improve the algorithm's robustness, an automatic scheme for setpoint update that ensures safe convergence is proposed. Furthermore, the proposed method does not require user intervention; hence, the algorithm should be of particular interest for glycemic control in children and adolescents.

On Bi-Grid Local Mode Analysis of Solution Techniques for 3-D Euler and Navier-Stokes Equations

NASA Technical Reports Server (NTRS)

Ibraheem, S. O.; Demuren, A. O.

1994-01-01

A procedure is presented for utilizing a bi-grid stability analysis as a practical tool for predicting multigrid performance in a range of numerical methods for solving Euler and Navier-Stokes equations. Model problems based on the convection, diffusion and Burger's equation are used to illustrate the superiority of the bi-grid analysis as a predictive tool for multigrid performance in comparison to the smoothing factor derived from conventional von Neumann analysis. For the Euler equations, bi-grid analysis is presented for three upwind difference based factorizations, namely Spatial, Eigenvalue and Combination splits, and two central difference based factorizations, namely LU and ADI methods. In the former, both the Steger-Warming and van Leer flux-vector splitting methods are considered. For the Navier-Stokes equations, only the Beam-Warming (ADI) central difference scheme is considered. In each case, estimates of multigrid convergence rates from the bi-grid analysis are compared to smoothing factors obtained from single-grid stability analysis. Effects of grid aspect ratio and flow skewness are examined. Both predictions are compared with practical multigrid convergence rates for 2-D Euler and Navier-Stokes solutions based on the Beam-Warming central scheme.

Douglas-fir plantations in Europe: a retrospective test of assisted migration to address climate change.

PubMed

Isaac-Renton, Miriam G; Roberts, David R; Hamann, Andreas; Spiecker, Heinrich

2014-08-01

We evaluate genetic test plantations of North American Douglas-fir provenances in Europe to quantify how tree populations respond when subjected to climate regime shifts, and we examined whether bioclimate envelope models developed for North America to guide assisted migration under climate change can retrospectively predict the success of these provenance transfers to Europe. The meta-analysis is based on long-term growth data of 2800 provenances transferred to 120 European test sites. The model was generally well suited to predict the best performing provenances along north-south gradients in Western Europe, but failed to predict superior performance of coastal North American populations under continental climate conditions in Eastern Europe. However, model projections appear appropriate when considering additional information regarding adaptation of Douglas-fir provenances to withstand frost and drought, even though the model partially fails in a validation against growth traits alone. We conclude by applying the partially validated model to climate change scenarios for Europe, demonstrating that climate trends observed over the last three decades warrant changes to current use of Douglas-fir provenances in plantation forestry throughout Western and Central Europe. © 2014 John Wiley & Sons Ltd.

Robust face alignment under occlusion via regional predictive power estimation.

PubMed

Heng Yang; Xuming He; Xuhui Jia; Patras, Ioannis

2015-08-01

Face alignment has been well studied in recent years, however, when a face alignment model is applied on facial images with heavy partial occlusion, the performance deteriorates significantly. In this paper, instead of training an occlusion-aware model with visibility annotation, we address this issue via a model adaptation scheme that uses the result of a local regression forest (RF) voting method. In the proposed scheme, the consistency of the votes of the local RF in each of several oversegmented regions is used to determine the reliability of predicting the location of the facial landmarks. The latter is what we call regional predictive power (RPP). Subsequently, we adapt a holistic voting method (cascaded pose regression based on random ferns) by putting weights on the votes of each fern according to the RPP of the regions used in the fern tests. The proposed method shows superior performance over existing face alignment models in the most challenging data sets (COFW and 300-W). Moreover, it can also estimate with high accuracy (72.4% overlap ratio) which image areas belong to the face or nonface objects, on the heavily occluded images of the COFW data set, without explicit occlusion modeling.

Emotional Intelligence and Mismatching Expressive and Verbal Messages: A Contribution to Detection of Deception

PubMed Central

Wojciechowski, Jerzy; Stolarski, Maciej; Matthews, Gerald

2014-01-01

Processing facial emotion, especially mismatches between facial and verbal messages, is believed to be important in the detection of deception. For example, emotional leakage may accompany lying. Individuals with superior emotion perception abilities may then be more adept in detecting deception by identifying mismatch between facial and verbal messages. Two personal factors that may predict such abilities are female gender and high emotional intelligence (EI). However, evidence on the role of gender and EI in detection of deception is mixed. A key issue is that the facial processing skills required to detect deception may not be the same as those required to identify facial emotion. To test this possibility, we developed a novel facial processing task, the FDT (Face Decoding Test) that requires detection of inconsistencies between facial and verbal cues to emotion. We hypothesized that gender and ability EI would be related to performance when cues were inconsistent. We also hypothesized that gender effects would be mediated by EI, because women tend to score as more emotionally intelligent on ability tests. Data were collected from 210 participants. Analyses of the FDT suggested that EI was correlated with superior face decoding in all conditions. We also confirmed the expected gender difference, the superiority of high EI individuals, and the mediation hypothesis. Also, EI was more strongly associated with facial decoding performance in women than in men, implying there may be gender differences in strategies for processing affective cues. It is concluded that integration of emotional and cognitive cues may be a core attribute of EI that contributes to the detection of deception. PMID:24658500

Revising superior planning performance in chess players: the impact of time restriction and motivation aspects.

PubMed

Unterrainer, Josef Martin; Kaller, Christoph Philipp; Leonhart, Rainer; Rahm, Benjamin

2011-01-01

In a previous study (Unterrainer, Kaller, Halsband, & Rahm, 2006), chess players outperformed non-chess players in the Tower of London planning task but exhibited disproportionately longer processing times. This pattern of results raises the question of whether chess players' planning capabilities are superior or whether the results reflect differences in the speed-accuracy trade-off between the groups, possibly attributable to sports motivation. The present study was designed to disambiguate these alternative suggestions by implementing various constraints on planning time and by assessing self-reported motivation. In contrast to the previous study, chess players' performance was not superior, independently of whether problems had to be solved with (Experiment 1) or without (Experiment 2) time limits. As expected, chess players reported higher overall trait and state motivation scores across both experiments. These findings revise the notion of superior planning performance in chess players. In consequence, they do not conform with the assumption of a general transfer of chess-related planning expertise to other cognitive domains, instead suggesting that superior performance may be possible only under specific circumstances such as receiving competitive instructions.

DOE-DARPA High-Performance Corrosion-Resistant Materials (HPCRM), Annual HPCRM Team Meeting & Technical Review

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

Farmer, J; Brown, B; Bayles, B

The overall goal is to develop high-performance corrosion-resistant iron-based amorphous-metal coatings for prolonged trouble-free use in very aggressive environments: seawater & hot geothermal brines. The specific technical objectives are: (1) Synthesize Fe-based amorphous-metal coating with corrosion resistance comparable/superior to Ni-based Alloy C-22; (2) Establish processing parameter windows for applying and controlling coating attributes (porosity, density, bonding); (3) Assess possible cost savings through substitution of Fe-based material for more expensive Ni-based Alloy C-22; (4) Demonstrate practical fabrication processes; (5) Produce quality materials and data with complete traceability for nuclear applications; and (6) Develop, validate and calibrate computational models to enable lifemore » prediction and process design.« less

The Tri-Trophic Interactions Hypothesis: Interactive Effects of Host Plant Quality, Diet Breadth and Natural Enemies on Herbivores

PubMed Central

Mooney, Kailen A.; Pratt, Riley T.; Singer, Michael S.

2012-01-01

Several influential hypotheses in plant-herbivore and herbivore-predator interactions consider the interactive effects of plant quality, herbivore diet breadth, and predation on herbivore performance. Yet individually and collectively, these hypotheses fail to address the simultaneous influence of all three factors. Here we review existing hypotheses, and propose the tri-trophic interactions (TTI) hypothesis to consolidate and integrate their predictions. The TTI hypothesis predicts that dietary specialist herbivores (as compared to generalists) should escape predators and be competitively dominant due to faster growth rates, and that such differences should be greater on low quality (as compared to high quality) host plants. To provide a preliminary test of these predictions, we conducted an empirical study comparing the effects of plant (Baccharis salicifolia) quality and predators between a specialist (Uroleucon macolai) and a generalist (Aphis gossypii) aphid herbivore. Consistent with predictions, these three factors interactively determine herbivore performance in ways not addressed by existing hypotheses. Compared to the specialist, the generalist was less fecund, competitively inferior, and more sensitive to low plant quality. Correspondingly, predator effects were contingent upon plant quality only for the generalist. Contrary to predictions, predator effects were weaker for the generalist and on low-quality plants, likely due to density-dependent benefits provided to the generalist by mutualist ants. Because the TTI hypothesis predicts the superior performance of specialists, mutualist ants may be critical to A. gossypii persistence under competition from U. macolai. In summary, the integrative nature of the TTI hypothesis offers novel insight into the determinants of plant-herbivore and herbivore-predator interactions and the coexistence of specialist and generalist herbivores. PMID:22509298

White Matter Integrity, Substance Use, and Risk Taking in Adolescence

PubMed Central

Jacobus, Joanna; Thayer, Rachel E.; Trim, Ryan S.; Bava, Sunita; Frank, Lawrence R.; Tapert, Susan F.

2012-01-01

White matter development is important for efficient communication between brain regions, higher order cognitive functioning, and complex behaviors. Adolescents have a higher propensity for engaging in risky behaviors, yet few studies have explored associations between white matter integrity and risk taking directly. Altered white matter integrity in mid-adolescence was hypothesized to predict subsequent risk taking behaviors 1.5 years later. Adolescent substance users (predominantly alcohol and marijuana, n=47) and demographically similar non-users (n=49) received diffusion tensor imaging at baseline (ages 16–19), and risk taking measures at both baseline and an 18-month follow-up (i.e., at ages 17–20). Brain regions of interest were: fornix, superior corona radiata, superior longitudinal fasciculus, and superior fronto-occipital fasciculus. In substance using youth (n=47), lower white matter integrity at baseline in the fornix and superior corona radiata predicted follow-up substance use (ΔR2 =10–12%, ps < .01), and baseline fornix integrity predicted follow-up delinquent behaviors (ΔR2 = 10%, p < .01) 1.5 years later. Poorer fronto-limbic white matter integrity was linked to a greater propensity for future risk taking behaviors among youth who initiated heavy substance use by mid-adolescence. Most notable were relationships between projection and limbic system fibers and future substance use frequency. Subcortical white matter coherence along with an imbalance between the maturation levels in cognitive control and reward systems may disadvantage the resistance to engage in risk taking behaviors during adolescence. PMID:22564204

White matter integrity, substance use, and risk taking in adolescence.

PubMed

Jacobus, Joanna; Thayer, Rachel E; Trim, Ryan S; Bava, Sunita; Frank, Lawrence R; Tapert, Susan F

2013-06-01

White matter development is important for efficient communication between brain regions, higher order cognitive functioning, and complex behaviors. Adolescents have a higher propensity for engaging in risky behaviors, yet few studies have explored associations between white matter integrity and risk taking directly. Altered white matter integrity in mid-adolescence was hypothesized to predict subsequent risk taking behaviors 1.5 years later. Adolescent substance users (predominantly alcohol and marijuana, n = 47) and demographically similar nonusers (n = 49) received diffusion tensor imaging at baseline (ages 16-19), and risk taking measures at both baseline and an 18-month follow-up (i.e., at ages 17-20). Brain regions of interest were the fornix, superior corona radiata, superior longitudinal fasciculus, and superior fronto-occipital fasciculus. In substance-using youth (n = 47), lower white matter integrity at baseline in the fornix and superior corona radiata predicted follow-up substance use (ΔR2 = 10-12%, ps < .01), and baseline fornix integrity predicted follow-up delinquent behaviors (ΔR2 = 10%, p < .01) 1.5 years later. Poorer fronto-limbic white matter integrity was linked to a greater propensity for future risk taking behaviors among youth who initiated heavy substance use by mid-adolescence. Most notable were relationships between projection and limbic-system fibers and future substance-use frequency. Subcortical white matter coherence, along with an imbalance between the maturation levels in cognitive control and reward systems, may disadvantage the resistance to engage in risk taking behaviors during adolescence. 2013 APA, all rights reserved

Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility

NASA Astrophysics Data System (ADS)

Ma, Feng; Wei, Yu; Huang, Dengshi; Chen, Yixiang

2014-07-01

In this paper, by taking the 5-min high frequency data of the Shanghai Composite Index as example, we compare the forecasting performance of HAR-RV and Multifractal volatility, Realized volatility, Realized Bipower Variation and their corresponding short memory model with rolling windows forecasting method and the Model Confidence Set which is proved superior to SPA test. The empirical results show that, for six loss functions, HAR-RV outperforms other models. Moreover, to make the conclusions more precise and robust, we use the MCS test to compare the performance of their logarithms form models, and find that the HAR-log(RV) has a better performance in predicting future volatility. Furthermore, by comparing the two models of HAR-RV and HAR-log(RV), we conclude that, in terms of performance forecasting, the HAR-log(RV) model is the best model among models we have discussed in this paper.

Performance of Swashplateless Ultralight Helicopter Rotor with Trailing-edge Flaps for Primary Flight Control

NASA Technical Reports Server (NTRS)

Shen, Jin-Wei; Chopra, Inderjit

2003-01-01

The objective of present study is to evaluate the rotor performance, trailing-edge deflections and actuation requirement of a helicopter rotor with trailing-edge flap system for primary flight control. The swashplateless design is implemented by modifying a two-bladed teetering rotor of an production ultralight helicopter through the use of plain flaps on the blades, and by replacing the pitch link to fixed system control system assembly with a root spring. A comprehensive rotorcraft analysis based on UMARC is carried out to obtain the results for both the swashplateless and a conventional baseline rotor configuration. The predictions show swashplateless configuration achieve superior performance than the conventional rotor attributed from reduction of parasite drag by eliminating swashplate mechanic system. It is indicated that optimal selection of blade pitch index angle, flap location, length, and chord ratio reduces flap deflections and actuation requirements, however, has virtually no effect on rotor performance.

Species distribution models: A comparison of statistical approaches for livestock and disease epidemics.

PubMed

Hollings, Tracey; Robinson, Andrew; van Andel, Mary; Jewell, Chris; Burgman, Mark

2017-01-01

In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning.

Species distribution models: A comparison of statistical approaches for livestock and disease epidemics

PubMed Central

Robinson, Andrew; van Andel, Mary; Jewell, Chris; Burgman, Mark

2017-01-01

In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning. PMID:28837685

High Ability and Learner Characteristics

ERIC Educational Resources Information Center

Hindal, Huda; Reid, Norman; Whitehead, Rex

2013-01-01

The outstandingly able learner has been conceptualised, in terms of test and examination performance, as the learner showing superior academic performance which is markedly better than that of peers and in ways regarded as of value by wider society. In Kuwait, such superior examination performance leads to a classification regarded as being…

40 CFR 63.176 - Quality improvement program for pumps.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., operating or maintenance practices, and pump or pump seal designs or technologies that have poorer than... shall also be used to determine if there are superior performing pump or pump seal technologies that are... average emission performance. A superior performing pump or pump seal technology is one with a leak...

40 CFR 63.176 - Quality improvement program for pumps.

Code of Federal Regulations, 2011 CFR

2011-07-01

..., operating or maintenance practices, and pump or pump seal designs or technologies that have poorer than... shall also be used to determine if there are superior performing pump or pump seal technologies that are... average emission performance. A superior performing pump or pump seal technology is one with a leak...

40 CFR 63.1035 - Quality improvement program for pumps.

Code of Federal Regulations, 2013 CFR

2013-07-01

... determine the services, operating or maintenance practices, and pump or pump seal designs or technologies... analysis shall also be used to determine if there are superior performing pump or pump seal technologies... with poorer than average emission performance. A superior performing pump or pump seal technology is...

40 CFR 63.1035 - Quality improvement program for pumps.

Code of Federal Regulations, 2012 CFR

2012-07-01

... determine the services, operating or maintenance practices, and pump or pump seal designs or technologies... analysis shall also be used to determine if there are superior performing pump or pump seal technologies... with poorer than average emission performance. A superior performing pump or pump seal technology is...

40 CFR 63.1035 - Quality improvement program for pumps.

Code of Federal Regulations, 2011 CFR

2011-07-01

... determine the services, operating or maintenance practices, and pump or pump seal designs or technologies... analysis shall also be used to determine if there are superior performing pump or pump seal technologies... with poorer than average emission performance. A superior performing pump or pump seal technology is...

40 CFR 63.1035 - Quality improvement program for pumps.

Code of Federal Regulations, 2014 CFR

2014-07-01

... determine the services, operating or maintenance practices, and pump or pump seal designs or technologies... analysis shall also be used to determine if there are superior performing pump or pump seal technologies... with poorer than average emission performance. A superior performing pump or pump seal technology is...

40 CFR 63.176 - Quality improvement program for pumps.

Code of Federal Regulations, 2012 CFR

2012-07-01

..., operating or maintenance practices, and pump or pump seal designs or technologies that have poorer than... shall also be used to determine if there are superior performing pump or pump seal technologies that are... average emission performance. A superior performing pump or pump seal technology is one with a leak...

Cognitive Development of Intellectually Gifted: A Piagetian Perspective.

ERIC Educational Resources Information Center

Carter, Kyle R.

1985-01-01

Relationships between intellectual giftedness and performance within Piagetian stages of 673 gifted students (10-16 years old) were investigated. Results showed that intellectually superior children out- performed children of normal ability at all age levels. Intellectually superior Ss out-performed bright-normal Ss at lower ages, but no…

A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network.

PubMed

Luo, Jiawei; Xiao, Qiu

2017-02-01

MicroRNAs (miRNAs) play a critical role by regulating their targets in post-transcriptional level. Identification of potential miRNA-disease associations will aid in deciphering the pathogenesis of human polygenic diseases. Several computational models have been developed to uncover novel miRNA-disease associations based on the predicted target genes. However, due to the insufficient number of experimentally validated miRNA-target interactions as well as the relatively high false-positive and false-negative rates of predicted target genes, it is still challenging for these prediction models to obtain remarkable performances. The purpose of this study is to prioritize miRNA candidates for diseases. We first construct a heterogeneous network, which consists of a disease similarity network, a miRNA functional similarity network and a known miRNA-disease association network. Then, an unbalanced bi-random walk-based algorithm on the heterogeneous network (BRWH) is adopted to discover potential associations by exploiting bipartite subgraphs. Based on 5-fold cross validation, the proposed network-based method achieves AUC values ranging from 0.782 to 0.907 for the 22 human diseases and an average AUC of almost 0.846. The experiments indicated that BRWH can achieve better performances compared with several popular methods. In addition, case studies of some common diseases further demonstrated the superior performance of our proposed method on prioritizing disease-related miRNA candidates. Copyright © 2017 Elsevier Inc. All rights reserved.

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian

2005-04-01

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

PubMed

Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

Whoever Doesn't HOP Must Be Superior: The Russian Left-Periphery and the Emergence of Superiority

ERIC Educational Resources Information Center

Scott, Tatiana V.

2012-01-01

This dissertation maps the left-periphery of the Russian language, presenting a new geometry of Russian main and subordinate clauses in order to account for a number of phenomena: single and multiple wh-constructions, sluicing constructions, and coordinate multiple wh-constructions (CMW), as well as to predict various occurring word-orders.…

In Search of ''Master Mindreaders'': Are Psychics Superior in Reading the Language of the Eyes?

ERIC Educational Resources Information Center

Dziobek, I.; Rogers, K.; Fleck, S.; Hassenstab, J.; Gold, S.; Wolf, O.T.; Convit, A.

2005-01-01

Much of the effort to understand the brain substrate of theory of mind and empathy has involved the study of individuals with deficits in that domain, such as those on the autism spectrum. Studying individuals with presumed superior abilities in picking up social signals may yield important additional information. We predicted that psychic readers…

DETERMINING THE INFLUENCE OF LANDSCAPE AND RESEARCH-SPECIFIC HABITAT VARIABLES ON VARIATION OF THERMAL CHARACTERISTICS OF STREAMS WITHIN WESTERN LAKE SUPERIOR WATERSHEDS

EPA Science Inventory

As part of a study to develop and test a framework for predicting sensitivity of watersheds to land-use activities, temperatures were monitored in 48 second- and third- order streams on the north and south shores of western Lake Superior. Maximun 21-day average temperatures, whic...

Improved numerical methods for turbulent viscous flows aerothermal modeling program, phase 2

NASA Technical Reports Server (NTRS)

Karki, K. C.; Patankar, S. V.; Runchal, A. K.; Mongia, H. C.

1988-01-01

The details of a study to develop accurate and efficient numerical schemes to predict complex flows are described. In this program, several discretization schemes were evaluated using simple test cases. This assessment led to the selection of three schemes for an in-depth evaluation based on two-dimensional flows. The scheme with the superior overall performance was incorporated in a computer program for three-dimensional flows. To improve the computational efficiency, the selected discretization scheme was combined with a direct solution approach in which the fluid flow equations are solved simultaneously rather than sequentially.

Nonlinear autoregressive neural networks with external inputs for forecasting of typhoon inundation level.

PubMed

Ouyang, Huei-Tau

2017-08-01

Accurate inundation level forecasting during typhoon invasion is crucial for organizing response actions such as the evacuation of people from areas that could potentially flood. This paper explores the ability of nonlinear autoregressive neural networks with exogenous inputs (NARX) to predict inundation levels induced by typhoons. Two types of NARX architecture were employed: series-parallel (NARX-S) and parallel (NARX-P). Based on cross-correlation analysis of rainfall and water-level data from historical typhoon records, 10 NARX models (five of each architecture type) were constructed. The forecasting ability of each model was assessed by considering coefficient of efficiency (CE), relative time shift error (RTS), and peak water-level error (PE). The results revealed that high CE performance could be achieved by employing more model input variables. Comparisons of the two types of model demonstrated that the NARX-S models outperformed the NARX-P models in terms of CE and RTS, whereas both performed exceptionally in terms of PE and without significant difference. The NARX-S and NARX-P models with the highest overall performance were identified and their predictions were compared with those of traditional ARX-based models. The NARX-S model outperformed the ARX-based models in all three indexes, whereas the NARX-P model exhibited comparable CE performance and superior RTS and PE performance.

3D printed cellular solid outperforms traditional stochastic foam in long-term mechanical response

DOE PAGES

Maiti, A.; Small, W.; Lewicki, J.; ...

2016-04-27

3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curvesmore » predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. As a result, this indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter’s improved long-term stability and mechanical performance.« less

3D printed cellular solid outperforms traditional stochastic foam in long-term mechanical response

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

Maiti, A.; Small, W.; Lewicki, J.

3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curvesmore » predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. As a result, this indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter’s improved long-term stability and mechanical performance.« less

Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.

PubMed

Liu, Yang; Traskin, Mikhail; Lorch, Scott A; George, Edward I; Small, Dylan

2015-03-01

A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.

3D printed cellular solid outperforms traditional stochastic foam in long-term mechanical response

NASA Astrophysics Data System (ADS)

Maiti, A.; Small, W.; Lewicki, J. P.; Weisgraber, T. H.; Duoss, E. B.; Chinn, S. C.; Pearson, M. A.; Spadaccini, C. M.; Maxwell, R. S.; Wilson, T. S.

2016-04-01

3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curves predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. This indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter’s improved long-term stability and mechanical performance.

3D printed cellular solid outperforms traditional stochastic foam in long-term mechanical response

PubMed Central

Maiti, A.; Small, W.; Lewicki, J. P.; Weisgraber, T. H.; Duoss, E. B.; Chinn, S. C.; Pearson, M. A.; Spadaccini, C. M.; Maxwell, R. S.; Wilson, T. S.

2016-01-01

3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curves predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. This indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter’s improved long-term stability and mechanical performance. PMID:27117858

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

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

Yahya, Noorazrul, E-mail: noorazrul.yahya@research.uwa.edu.au; Ebert, Martin A.; Bulsara, Max

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥more » 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.« less

An improved method to detect correct protein folds using partial clustering.

PubMed

Zhou, Jianjun; Wishart, David S

2013-01-16

Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient "partial" clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either C(α) RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance.

An improved method to detect correct protein folds using partial clustering

PubMed Central

2013-01-01

Background Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient “partial“ clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods. Results We propose a new scheme that performs rapid but incomplete clustering on protein decoys. Our method detects structurally similar decoys (measured using either Cα RMSD or GDT-TS score) and extracts representatives from them without assigning every decoy to a cluster. We integrated our new clustering strategy with several different scoring functions to assess both the performance and speed in identifying correct or near-correct folds. Experimental results on 35 Rosetta decoy sets and 40 I-TASSER decoy sets show that our method can improve the correct fold detection rate as assessed by two different quality criteria. This improvement is significantly better than two recently published clustering methods, Durandal and Calibur-lite. Speed and efficiency testing shows that our method can handle much larger decoy sets and is up to 22 times faster than Durandal and Calibur-lite. Conclusions The new method, named HS-Forest, avoids the computationally expensive task of clustering every decoy, yet still allows superior correct-fold selection. Its improved speed, efficiency and decoy-selection performance should enable structure prediction researchers to work with larger decoy sets and significantly improve their ab initio structure prediction performance. PMID:23323835

Characterizing Tumor Heterogeneity With Functional Imaging and Quantifying High-Risk Tumor Volume for Early Prediction of Treatment Outcome: Cervical Cancer as a Model

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

Mayr, Nina A., E-mail: Nina.Mayr@osumc.edu; Huang Zhibin; Wang, Jian Z.

2012-07-01

Purpose: Treatment response in cancer has been monitored by measuring anatomic tumor volume (ATV) at various times without considering the inherent functional tumor heterogeneity known to critically influence ultimate treatment outcome: primary tumor control and survival. This study applied dynamic contrast-enhanced (DCE) functional MRI to characterize tumors' heterogeneous subregions with low DCE values, at risk for treatment failure, and to quantify the functional risk volume (FRV) for personalized early prediction of treatment outcome. Methods and Materials: DCE-MRI was performed in 102 stage IB{sub 2}-IVA cervical cancer patients to assess tumor perfusion heterogeneity before and during radiation/chemotherapy. FRV represents the totalmore » volume of tumor voxels with critically low DCE signal intensity (<2.1 compared with precontrast image, determined by previous receiver operator characteristic analysis). FRVs were correlated with treatment outcome (follow-up: 0.2-9.4, mean 6.8 years) and compared with ATVs (Mann-Whitney, Kaplan-Meier, and multivariate analyses). Results: Before and during therapy at 2-2.5 and 4-5 weeks of RT, FRVs >20, >13, and >5 cm{sup 3}, respectively, significantly predicted unfavorable 6-year primary tumor control (p = 0.003, 7.3 Multiplication-Sign 10{sup -8}, 2.0 Multiplication-Sign 10{sup -8}) and disease-specific survival (p = 1.9 Multiplication-Sign 10{sup -4}, 2.1 Multiplication-Sign 10{sup -6}, 2.5 Multiplication-Sign 10{sup -7}, respectively). The FRVs were superior to the ATVs as early predictors of outcome, and the differentiating power of FRVs increased during treatment. Discussion: Our preliminary results suggest that functional tumor heterogeneity can be characterized by DCE-MRI to quantify FRV for predicting ultimate long-term treatment outcome. FRV is a novel functional imaging heterogeneity parameter, superior to ATV, and can be clinically translated for personalized early outcome prediction before or as early as 2-5 weeks into treatment.« less

Predicting couple therapy outcomes based on speech acoustic features

PubMed Central

Nasir, Md; Baucom, Brian Robert; Narayanan, Shrikanth

2017-01-01

Automated assessment and prediction of marital outcome in couples therapy is a challenging task but promises to be a potentially useful tool for clinical psychologists. Computational approaches for inferring therapy outcomes using observable behavioral information obtained from conversations between spouses offer objective means for understanding relationship dynamics. In this work, we explore whether the acoustics of the spoken interactions of clinically distressed spouses provide information towards assessment of therapy outcomes. The therapy outcome prediction task in this work includes detecting whether there was a relationship improvement or not (posed as a binary classification) as well as discerning varying levels of improvement or decline in the relationship status (posed as a multiclass recognition task). We use each interlocutor’s acoustic speech signal characteristics such as vocal intonation and intensity, both independently and in relation to one another, as cues for predicting the therapy outcome. We also compare prediction performance with one obtained via standardized behavioral codes characterizing the relationship dynamics provided by human experts as features for automated classification. Our experiments, using data from a longitudinal clinical study of couples in distressed relations, showed that predictions of relationship outcomes obtained directly from vocal acoustics are comparable or superior to those obtained using human-rated behavioral codes as prediction features. In addition, combining direct signal-derived features with manually coded behavioral features improved the prediction performance in most cases, indicating the complementarity of relevant information captured by humans and machine algorithms. Additionally, considering the vocal properties of the interlocutors in relation to one another, rather than in isolation, showed to be important for improving the automatic prediction. This finding supports the notion that behavioral outcome, like many other behavioral aspects, is closely related to the dynamics and mutual influence of the interlocutors during their interaction and their resulting behavioral patterns. PMID:28934302

Effects of low-level sarin and cyclosarin exposure on white matter integrity in Gulf War Veterans.

PubMed

Chao, Linda L; Zhang, Yu; Buckley, Shannon

2015-05-01

We previously found evidence of reduced gray and white matter volume in Gulf War (GW) veterans with predicted low-level exposure to sarin (GB) and cyclosarin (GF). Because loss of white matter tissue integrity has been linked to both gray and white matter atrophy, the current study sought to test the hypothesis that GW veterans with predicted GB/GF exposure have evidence of disrupted white matter microstructural integrity. Measures of fractional anisotropy and directional (i.e., axial and radial) diffusivity were assessed from the 4T diffusion tensor images (DTI) of 59 GW veterans with predicted GB/GF exposure and 59 "matched" unexposed GW veterans (mean age: 48 ± 7 years). The DTI data were analyzed using regions of interest (ROI) analyses that accounted for age, sex, total brain gray and white matter volume, trauma exposure, posttraumatic stress disorder, current major depression, and chronic multisymptom illness status. There were no significant group differences in fractional anisotropy or radial diffusivity. However, there was increased axial diffusivity in GW veterans with predicted GB/GF exposure compared to matched, unexposed veterans throughout the brain, including the temporal stem, corona radiata, superior and inferior (hippocampal) cingulum, inferior and superior fronto-occipital fasciculus, internal and external capsule, and superficial cortical white matter blades. Post hoc analysis revealed significant correlations between higher fractional anisotropy and lower radial diffusivity with better neurobehavioral performance in unexposed GW veterans. In contrast, only increased axial diffusivity in posterior limb of the internal capsule was associated with better psychomotor function in GW veterans with predicted GB/GF exposure. The finding that increased axial diffusivity in a region of the brain that contains descending corticospinal fibers was associated with better psychomotor function and the lack of significant neurobehavioral deficits in veterans with predicted GB/GF exposure hint at the possibility that the widespread increases in axial diffusivity that we observed in GW veterans with predicted GB/GF exposure relative to unexposed controls may reflect white matter reorganization after brain injury (i.e., exposure to GB/GF). Published by Elsevier B.V.

Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.

PubMed

Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan

2016-11-01

In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects. Copyright © 2016 Crop Science Society of America.

Is performance related to marketing research in the health care industry?

PubMed

Naidu, G M; Kleimenhagen, A; Pillari, G D

1994-01-01

Marketing research has grown to become indispensable for superior performance in packaged goods industries. While health care institutions are spending large amounts on marketing research, few studies focus upon the relationship of marketing research to health care organizational performance. Utilizing a national sample of U.S. hospitals, this article points out that marketing research and superior performance are positively associated.

Giftedness and Evidence for Reproducibly Superior Performance: An Account Based on the Expert Performance Framework

ERIC Educational Resources Information Center

Ericsson, K. Anders; Roring, Roy W.; Nandagopal, Kiruthiga

2007-01-01

Giftedness researchers have long debated whether there is empirical evidence to support a distinction between giftedness and attained level of achievement. In this paper we propose a general theoretical framework that establishes scientific criteria for acceptable evidence of superior reproducible performance, which any theory of exceptional…

Combustion of Nitramine Propellants

DTIC Science & Technology

1983-03-01

through development of a comprehensive analytical model. The ultimate goals are to enable prediction of deflagration rate over a wide pressure range...superior in burn rate prediction , both simple models fail in correlating existing temperature- sensitivity data. (2) In the second part, a...auxiliary condition to enable independent burn rate prediction ; improved melt phase model including decomposition-gas bubbles; model for far-field

An experimental/computational study of sharp fin induced shock wave/turbulent boundary layer interactions at Mach 5 - Experimental results

NASA Technical Reports Server (NTRS)

Rodi, Patrick E.; Dolling, David S.

1992-01-01

A combined experimental/computational study has been performed of sharp fin induced shock wave/turbulent boundary layer interactions at Mach 5. The current paper focuses on the experiments and analysis of the results. The experimental data include mean surface heat transfer, mean surface pressure distributions and surface flow visualization for fin angles of attack of 6, 8, 10, 12, 14 and 16-degrees at Mach 5 under a moderately cooled wall condition. Comparisons between the results and correlations developed earlier show that Scuderi's correlation for the upstream influence angle (recast in a conical form) is superior to other such correlations in predicting the current results, that normal Mach number based correlations for peak pressure heat transfer are adequate and that the initial heat transfer peak can be predicted using pressure-interaction theory.

Acquisition with partial and continuous reinforcement in pigeon autoshaping.

PubMed

Gottlieb, Daniel A

2004-08-01

Contemporary time accumulation models make the unique prediction that acquisition of a conditioned response will be equally rapid with partial and continuous reinforcement, if the time between conditioned stimuli is held constant. To investigate this, acquisition of conditioned responding was examined in pigeon autoshaping under conditions of 100% and 25% reinforcement, holding intertrial interval constant. Contrary to what was predicted, evidence for slowed acquisition in partially reinforced animals was observed with several response measures. However, asymptotic performance was superior with 25% reinforcement. A switching of reinforcement contingencies after initial acquisition did not immediately affect responding. After further sessions, partial reinforcement augmented responding, whereas continuous reinforcement did not, irrespective of an animal's reinforcement history. Subsequent training with a novel stimulus maintained the response patterns. These acquisition results generally support associative, rather than time accumulation, accounts of conditioning.

Eighteen-month-olds' ability to make gaze predictions following distraction or a long delay.

PubMed

Forssman, Linda; Bohlin, Gunilla; von Hofsten, Claes

2014-05-01

The abilities to flexibly allocate attention, select between conflicting stimuli, and make anticipatory gaze movements are important for young children's exploration and learning about their environment. These abilities constitute voluntary control of attention and show marked improvements in the second year of a child's life. Here we investigate the effects of visual distraction and delay on 18-month-olds' ability to predict the location of an occluded target in an experiment that requires switching of attention, and compare their performance to that of adults. Our results demonstrate that by 18 months of age children can readily overcome a previously learned response, even under a condition that involves visual distraction, but have difficulties with correctly updating their prediction when presented with a longer time delay. Further, the experiment shows that, overall, the 18-month-olds' allocation of visual attention is similar to that of adults, the primary difference being that adults demonstrate a superior ability to maintain attention on task and update their predictions over a longer time period. Copyright © 2014 Elsevier Inc. All rights reserved.

Usefulness and limitations of various guinea-pig test methods in detecting human skin sensitizers-validation of guinea-pig tests for skin hypersensitivity.

PubMed

Marzulli, F; Maguire, H C

1982-02-01

Several guinea-pig predictive test methods were evaluated by comparison of results with those obtained with human predictive tests, using ten compounds that have been used in cosmetics. The method involves the statistical analysis of the frequency with which guinea-pig tests agree with the findings of tests in humans. In addition, the frequencies of false positive and false negative predictive findings are considered and statistically analysed. The results clearly demonstrate the superiority of adjuvant tests (complete Freund's adjuvant) in determining skin sensitizers and the overall superiority of the guinea-pig maximization test in providing results similar to those obtained by human testing. A procedure is suggested for utilizing adjuvant and non-adjuvant test methods for characterizing compounds as of weak, moderate or strong sensitizing potential.

Improved regulatory element prediction based on tissue-specific local epigenomic signatures

PubMed Central

He, Yupeng; Gorkin, David U.; Dickel, Diane E.; Nery, Joseph R.; Castanon, Rosa G.; Lee, Ah Young; Shen, Yin; Visel, Axel; Pennacchio, Len A.; Ren, Bing; Ecker, Joseph R.

2017-01-01

Accurate enhancer identification is critical for understanding the spatiotemporal transcriptional regulation during development as well as the functional impact of disease-related noncoding genetic variants. Computational methods have been developed to predict the genomic locations of active enhancers based on histone modifications, but the accuracy and resolution of these methods remain limited. Here, we present an algorithm, regulatory element prediction based on tissue-specific local epigenetic marks (REPTILE), which integrates histone modification and whole-genome cytosine DNA methylation profiles to identify the precise location of enhancers. We tested the ability of REPTILE to identify enhancers previously validated in reporter assays. Compared with existing methods, REPTILE shows consistently superior performance across diverse cell and tissue types, and the enhancer locations are significantly more refined. We show that, by incorporating base-resolution methylation data, REPTILE greatly improves upon current methods for annotation of enhancers across a variety of cell and tissue types. REPTILE is available at https://github.com/yupenghe/REPTILE/. PMID:28193886

Dispositional envy revisited: unraveling the motivational dynamics of benign and malicious envy.

PubMed

Lange, Jens; Crusius, Jan

2015-02-01

Previous research has conceptualized dispositional envy as a unitary construct. Recently however, episodic envy has been shown to emerge in two qualitatively different forms. Benign envy is related to the motivation to move upward, whereas malicious envy is related to pulling superior others down. In four studies (N = 1,094)--using the newly developed Benign and Malicious Envy Scale (BeMaS)--we show that dispositional envy is also characterized by two independent dimensions related to distinct motivational dynamics and behavioral consequences. Dispositional benign and malicious envy uniquely predict envious responding following upward social comparisons. Furthermore, they are differentially connected to hope for success and fear of failure. Corresponding to these links, dispositional benign envy predicted faster race performance of marathon runners mediated via higher goal setting. In contrast, dispositional malicious envy predicted race goal disengagement. The findings highlight that disentangling the two sides of envy opens up numerous research avenues. © 2014 by the Society for Personality and Social Psychology, Inc.

An empirical evaluation of three vibrational spectroscopic methods for detection of aflatoxins in maize.

PubMed

Lee, Kyung-Min; Davis, Jessica; Herrman, Timothy J; Murray, Seth C; Deng, Youjun

2015-04-15

Three commercially available vibrational spectroscopic techniques, including Raman, Fourier transform near infrared reflectance (FT-NIR), and Fourier transform infrared (FTIR) were evaluated to help users determine the spectroscopic method best suitable for aflatoxin analysis in maize (Zea mays L.) grain based on their relative efficiency and predictive ability. Spectral differences of Raman and FTIR spectra were more marked and pronounced among aflatoxin contamination groups than those of FT-NIR spectra. From the observations and findings in our current and previous studies, Raman and FTIR spectroscopic methods are superior to FT-NIR method in terms of predictive power and model performance for aflatoxin analysis and they are equally effective and accurate in predicting aflatoxin concentration in maize. The present study is considered as the first attempt to assess how spectroscopic techniques with different physical processes can influence and improve accuracy and reliability for rapid screening of aflatoxin contaminated maize samples. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prognostic nomogram for previously untreated adult patients with acute myeloid leukemia

PubMed Central

Zheng, Zhuojun; Li, Xiaodong; Zhu, Yuandong; Gu, Weiying; Xie, Xiaobao; Jiang, Jingting

2016-01-01

This study was designed to perform an acceptable prognostic nomogram for acute myeloid leukemia. The clinical data from 311 patients from our institution and 165 patients generated with Cancer Genome Atlas Research Network were reviewed. A prognostic nomogram was designed according to the Cox's proportional hazard model to predict overall survival (OS). To compare the capacity of the nomogram with that of the current prognostic system, the concordance index (C-index) was used to validate the accuracy as well as the calibration curve. The nomogram included 6 valuable variables: age, risk stratifications based on cytogenetic abnormalities, status of FLT3-ITD mutation, status of NPM1 mutation, expression of CD34, and expression of HLA-DR. The C-indexes were 0.71 and 0.68 in the primary and validation cohort respectively, which were superior to the predictive capacity of the current prognostic systems in both cohorts. The nomogram allowed both patients with acute myeloid leukemia and physicians to make prediction of OS individually prior to treatment. PMID:27689396

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

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

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

1994-09-01

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

Multi-label learning with fuzzy hypergraph regularization for protein subcellular location prediction.

PubMed

Chen, Jing; Tang, Yuan Yan; Chen, C L Philip; Fang, Bin; Lin, Yuewei; Shang, Zhaowei

2014-12-01

Protein subcellular location prediction aims to predict the location where a protein resides within a cell using computational methods. Considering the main limitations of the existing methods, we propose a hierarchical multi-label learning model FHML for both single-location proteins and multi-location proteins. The latent concepts are extracted through feature space decomposition and label space decomposition under the nonnegative data factorization framework. The extracted latent concepts are used as the codebook to indirectly connect the protein features to their annotations. We construct dual fuzzy hypergraphs to capture the intrinsic high-order relations embedded in not only feature space, but also label space. Finally, the subcellular location annotation information is propagated from the labeled proteins to the unlabeled proteins by performing dual fuzzy hypergraph Laplacian regularization. The experimental results on the six protein benchmark datasets demonstrate the superiority of our proposed method by comparing it with the state-of-the-art methods, and illustrate the benefit of exploiting both feature correlations and label correlations.

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response.

PubMed

Djuričić, Goran J; Radulovic, Marko; Sopta, Jelena P; Nikitović, Marina; Milošević, Nebojša T

2017-01-01

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

ProTox: a web server for the in silico prediction of rodent oral toxicity

PubMed Central

Drwal, Malgorzata N.; Banerjee, Priyanka; Dunkel, Mathias; Wettig, Martin R.; Preissner, Robert

2014-01-01

Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein–ligand-based pharmacophore models (‘toxicophores’) for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes. PMID:24838562

Application of neural networks to prediction of fish diversity and salmonid production in the Lake Ontario basin

USGS Publications Warehouse

McKenna, James E.

2005-01-01

Diversity and fish productivity are important measures of the health and status of aquatic systems. Being able to predict the values of these indices as a function of environmental variables would be valuable to management. Diversity and productivity have been related to environmental conditions by multiple linear regression and discriminant analysis, but such methods have several shortcomings. In an effort to predict fish species diversity and estimate salmonid production for streams in the eastern basin of Lake Ontario, I constructed neural networks and trained them on a data set containing abiotic information and either fish diversity or juvenile salmonid abundance. Twenty percent of the original data were retained as a test data set and used in the training. The ability to extend these neural networks to conditions throughout the streams was tested with data not involved in the network training. The resulting neural networks were able to predict the number of salmonids with more than 84% accuracy and diversity with more than 73% accuracy, which was far superior to the performance of multiple regression. The networks also identified the environmental variables with the greatest predictive power, namely, those describing water movement, stream size, and water chemistry. Thirteen input variables were used to predict diversity and 17 to predict salmonid abundance.

Functional Connectivity Between Superior Parietal Lobule and Primary Visual Cortex "at Rest" Predicts Visual Search Efficiency.

PubMed

Bueichekú, Elisenda; Ventura-Campos, Noelia; Palomar-García, María-Ángeles; Miró-Padilla, Anna; Parcet, María-Antonia; Ávila, César

2015-10-01

Spatiotemporal activity that emerges spontaneously "at rest" has been proposed to reflect individual a priori biases in cognitive processing. This research focused on testing neurocognitive models of visual attention by studying the functional connectivity (FC) of the superior parietal lobule (SPL), given its central role in establishing priority maps during visual search tasks. Twenty-three human participants completed a functional magnetic resonance imaging session that featured a resting-state scan, followed by a visual search task based on the alphanumeric category effect. As expected, the behavioral results showed longer reaction times and more errors for the within-category (i.e., searching a target letter among letters) than the between-category search (i.e., searching a target letter among numbers). The within-category condition was related to greater activation of the superior and inferior parietal lobules, occipital cortex, inferior frontal cortex, dorsal anterior cingulate cortex, and the superior colliculus than the between-category search. The resting-state FC analysis of the SPL revealed a broad network that included connections with the inferotemporal cortex, dorsolateral prefrontal cortex, and dorsal frontal areas like the supplementary motor area and frontal eye field. Noteworthy, the regression analysis revealed that the more efficient participants in the visual search showed stronger FC between the SPL and areas of primary visual cortex (V1) related to the search task. We shed some light on how the SPL establishes a priority map of the environment during visual attention tasks and how FC is a valuable tool for assessing individual differences while performing cognitive tasks.

A comparison of hydrologic models for ecological flows and water availability

USGS Publications Warehouse

Caldwell, Peter V; Kennen, Jonathan G.; Sun, Ge; Kiang, Julie E.; Butcher, John B; Eddy, Michelle C; Hay, Lauren E.; LaFontaine, Jacob H.; Hain, Ernie F.; Nelson, Stacy C; McNulty, Steve G

2015-01-01

Robust hydrologic models are needed to help manage water resources for healthy aquatic ecosystems and reliable water supplies for people, but there is a lack of comprehensive model comparison studies that quantify differences in streamflow predictions among model applications developed to answer management questions. We assessed differences in daily streamflow predictions by four fine-scale models and two regional-scale monthly time step models by comparing model fit statistics and bias in ecologically relevant flow statistics (ERFSs) at five sites in the Southeastern USA. Models were calibrated to different extents, including uncalibrated (level A), calibrated to a downstream site (level B), calibrated specifically for the site (level C) and calibrated for the site with adjusted precipitation and temperature inputs (level D). All models generally captured the magnitude and variability of observed streamflows at the five study sites, and increasing level of model calibration generally improved performance. All models had at least 1 of 14 ERFSs falling outside a +/−30% range of hydrologic uncertainty at every site, and ERFSs related to low flows were frequently over-predicted. Our results do not indicate that any specific hydrologic model is superior to the others evaluated at all sites and for all measures of model performance. Instead, we provide evidence that (1) model performance is as likely to be related to calibration strategy as it is to model structure and (2) simple, regional-scale models have comparable performance to the more complex, fine-scale models at a monthly time step.

Logistic regression model can reduce unnecessary artificial liver support in hepatitis B virus-associated acute-on-chronic liver failure: decision curve analysis.

PubMed

Qin, Gang; Bian, Zhao-Lian; Shen, Yi; Zhang, Lei; Zhu, Xiao-Hong; Liu, Yan-Mei; Shao, Jian-Guo

2016-06-04

Several models have been proposed to predict the short-term outcome of acute-on-chronic liver failure (ACLF) after treatment. We aimed to determine whether better decisions for artificial liver support system (ALSS) treatment could be made with a model than without, through decision curve analysis (DCA). The medical profiles of a cohort of 232 patients with hepatitis B virus (HBV)-associated ACLF were retrospectively analyzed to explore the role of plasma prothrombin activity (PTA), model for end-stage liver disease (MELD) and logistic regression model (LRM) in identifying patients who could benefit from ALSS. The accuracy and reliability of PTA, MELD and LRM were evaluated with previously reported cutoffs. DCA was performed to evaluate the clinical role of these models in predicting the treatment outcome. With the cut-off value of 0.2, LRM had sensitivity of 92.6 %, specificity of 42.3 % and an area under the receiving operating characteristic curve (AUC) of 0.68, which showed superior discrimination over PTA and MELD. DCA revealed that the LRM-guided ALSS treatment was superior over other strategies including "treating all" and MELD-guided therapy, for the midrange threshold probabilities of 16 to 64 %. The use of LRM-guided ALSS treatment could increase both the accuracy and efficiency of this procedure, allowing the avoidance of unnecessary ALSS.

Estimation of hydraulic jump characteristics of channels with sudden diverging side walls via SVM.

PubMed

Roushangar, Kiyoumars; Valizadeh, Reyhaneh; Ghasempour, Roghayeh

2017-10-01

Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic structures. This paper focuses on the capability of the support vector machine (SVM) as a meta-model approach for predicting hydraulic jump characteristics in different sudden diverging stilling basins (i.e. basins with and without appurtenances). In this regard, different models were developed and tested using 1,018 experimental data. The obtained results proved the capability of the SVM technique in predicting hydraulic jump characteristics and it was found that the developed models for a channel with a central block performed more successfully than models for channels without appurtenances or with a negative step. The superior performance for the length of hydraulic jump was obtained for the model with parameters F 1 (Froude number) and (h 2- h 1 )/h 1 (h 1 and h 2 are sequent depth of upstream and downstream respectively). Concerning the relative energy dissipation and sequent depth ratio, the model with parameters F 1 and h 1 /B (B is expansion ratio) led to the best results. According to the outcome of sensitivity analysis, Froude number had the most significant effect on the modeling. Also comparison between SVM and empirical equations indicated the great performance of the SVM.

Resting-state connectivity predicts visuo-motor skill learning.

PubMed

Manuel, Aurélie L; Guggisberg, Adrian G; Thézé, Raphaël; Turri, Francesco; Schnider, Armin

2018-08-01

Spontaneous brain activity at rest is highly organized even when the brain is not explicitly engaged in a task. Functional connectivity (FC) in the alpha frequency band (α, 8-12 Hz) during rest is associated with improved performance on various cognitive and motor tasks. In this study we explored how FC is associated with visuo-motor skill learning and offline consolidation. We tested two hypotheses by which resting-state FC might achieve its impact on behavior: preparing the brain for an upcoming task or consolidating training gains. Twenty-four healthy participants were assigned to one of two groups: The experimental group (n = 12) performed a computerized mirror-drawing task. The control group (n = 12) performed a similar task but with concordant cursor direction. High-density 156-channel resting-state EEG was recorded before and after learning. Subjects were tested for offline consolidation 24h later. The Experimental group improved during training and showed offline consolidation. Increased α-FC between the left superior parietal cortex and the rest of the brain before training and decreased α-FC in the same region after training predicted learning. Resting-state FC following training did not predict offline consolidation and none of these effects were present in controls. These findings indicate that resting-state alpha-band FC is primarily implicated in providing optimal neural resources for upcoming tasks. Copyright © 2018 Elsevier Inc. All rights reserved.

Consensus statement: Using laryngeal electromyography for the diagnosis and treatment of vocal cord paralysis.

PubMed

Munin, Michael C; Heman-Ackah, Yolanda D; Rosen, Clark A; Sulica, Lucian; Maronian, Nicole; Mandel, Steven; Carey, Bridget T; Craig, Earl; Gronseth, Gary

2016-06-01

The purpose of this study was to develop an evidence-based consensus statement regarding use of laryngeal electromyography (LEMG) for diagnosis and treatment of vocal fold paralysis after recurrent laryngeal neuropathy (RLN). Two questions regarding LEMG were analyzed: (1) Does LEMG predict recovery in patients with acute unilateral or bilateral vocal fold paralysis? (2) Do LEMG findings change clinical management in these individuals? A systematic review was performed using American Academy of Neurology criteria for rating of diagnostic accuracy. Active voluntary motor unit potential recruitment and presence of polyphasic motor unit potentials within the first 6 months after lesion onset predicted recovery. Positive sharp waves and/or fibrillation potentials did not predict outcome. The presence of electrical synkinesis may decrease the likelihood of recovery, based on 1 published study. LEMG altered clinical management by changing the initial diagnosis from RLN in 48% of cases. Cricoarytenoid fixation and superior laryngeal neuropathy were the most common other diagnoses observed. If prognostic information is required in a patient with vocal fold paralysis that is more than 4 weeks and less than 6 months in duration, then LEMG should be performed. LEMG may be performed to clarify treatment decisions for vocal fold immobility that is presumed to be caused by RLN. Muscle Nerve 53: 850-855, 2016. © 2016 Wiley Periodicals, Inc.

[The assessment of ultrasonic measurement of superior vena cava blood flow for the volume responsiveness of patients with mechanical ventilation].

PubMed

Guo, Zhe; He, Wei; Hou, Jing; Li, Tong; Zhou, Hua; Xu, Yuan; Xi, Xiuming

2014-09-01

To approach the evaluative effect of respiratory variation of superior vena cava peak flow velocity measured using transthoracic echocardiography (TTE) on fluid responsiveness in patients with mechanical ventilation. A prospective cohort study was conducted. All mechanical ventilated critically ill patients whose fluid therapy was planned due to hypovolemia in Department of Critical Care Medicine of Beijing Tongren Hospital of Capital Medical University from April 2011 to April 2013 were enrolled. Volume expansion was performed with 500 mL Linger solution within 30 minutes. Patients were classified as responders if pulse pressure variation (PPV) increased ≥ 13% before volume expansion. The respiratory variation in superior vena cava peak velocity was calculated as the difference between maximum and minimum values of velocity in peak A, peak S and peak D over a single respiratory circle, and their variations (ΔA, ΔS, ΔD) were also calculated. The receiver operating characteristic curve (ROC curve) was plotted to assess the evaluative effect of respiratory variation of superior vena cava peak velocity on fluid responsiveness. Twenty-seven patients were enrolled in this study. Volume expansion increased PPV ≥ 13% happened in 14 patients (responders). The velocity of superior vena cava in peak A, peak S, peak D was significantly increased after volume expansion compared with that before volume expansion in responders [peak A (cm/s): 34.6 ± 2.2 vs. 31.3 ± 2.1, t=-2.493, P=0.027; peak S (cm/s): 39.1 ± 1.3 vs. 35.3 ± 2.1, t=-2.564, P=0.024; peak D (cm/s): 28.1 ± 1.2 vs. 23.3 ± 1.4, t=-4.995, P=0.000], but there was no significant difference in ΔA, ΔS and ΔD between before and after volume expansion. The ΔA, ΔS and ΔD were positively correlated with PPV (r=0.040, P=0.854; r=0.350, P=0.074; r=0.749, P=0.000). The area under ROC curve (AUC) of peak S was 0.36 [95% confidence interval (95%CI): 0.11-0.52], but the AUC of ΔS was 0.68 (95%CI 0.47-0.89), the AUC of peak D was 0.41 (95%CI 0.19-0.63), but the AUC of ΔD was 0.95 (95%CI 0.86-1.00), so the aberration rate of superior vena cava in respiration was better than the flow rate in superior vena cava. When the cut-off value of ΔS was 20.7% for predicting fluid responsiveness, the sensitivity was 78.6% and the specificity was 61.5%. When the cut-off value of ΔD was 12.7% for predicting fluid responsiveness, the sensitivity was 92.0% and the specificity was 92.3%. Respiratory variations in superior vena cava peak velocity measured by TTE could assess fluid responsiveness in patients with mechanical ventilation.

Image guided versus palpation guided core needle biopsy of palpable breast masses: a prospective study

PubMed Central

Hari, Smriti; Kumari, Swati; Srivastava, Anurag; Thulkar, Sanjay; Mathur, Sandeep; Veedu, Prasad Thotton

2016-01-01

Background & objectives: Biopsy of palpable breast masses can be performed manually by palpation guidance or under imaging guidance. Based on retrospective studies, image guided biopsy is considered more accurate than palpation guided breast biopsy; however, these techniques have not been compared prospectively. We conducted this prospective study to verify the superiority and determine the size of beneficial effect of image guided biopsy over palpation guided biopsy. Methods: Over a period of 18 months, 36 patients each with palpable breast masses were randomized into palpation guided and image guided breast biopsy arms. Ultrasound was used for image guidance in 33 patients and mammographic (stereotactic) guidance in three patients. All biopsies were performed using 14 gauge automated core biopsy needles. Inconclusive, suspicious or imaging-histologic discordant biopsies were repeated. Results: Malignancy was found in 30 of 36 women in palpation guided biopsy arm and 27 of 36 women in image guided biopsy arm. Palpation guided biopsy had sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 46.7, 100, 100, 27.3 per cent, respectively, for diagnosing breast cancer. Nineteen of 36 women (52.8%) required repeat biopsy because of inadequate samples (7 of 19), suspicious findings (2 of 19) or imaging-histologic discordance (10 of 19). On repeat biopsy, malignancy was found in all cases of imaging-histologic discordance. Image guided biopsy had 96.3 per cent sensitivity and 100 per cent specificity. There was no case of inadequate sample or imaging-histologic discordance with image guided biopsy. Interpretation & conclusions: Our results showed that in palpable breast masses, image guided biopsy was superior to palpation guided biopsy in terms of sensitivity, false negative rate and repeat biopsy rates. PMID:27488003

The P300 in middle cerebral artery strokes or hemorrhages: Outcome predictions and source localization.

PubMed

Ehlers, Mana R; López Herrero, Carmen; Kastrup, Andreas; Hildebrandt, Helmut

2015-08-01

There are no reliable outcome predictors for severely impaired patients suffering from large infarctions or hemorrhages within the territory of the middle cerebral artery. This study investigated whether the amplitude of the event-related potential (ERP) component P300 predicts if a patient will be transferred to the next stage of rehabilitation (positive outcome) or to a nursing home (negative outcome). The second goal was to look for lesion locations determining the generation of the P300 amplitude. Forty-seven patients performed an auditory oddball task to elicit the P300 and were assessed with different scores for activities of daily living (ADL). Patients were divided in two groups according to their outcome. P300 amplitudes were compared between these groups controlling for age and gender. Post-hoc analyses were performed to analyse the relationship between P300 amplitude and neurological outcome scores. In addition, lesion overlaps were created to detect which lesion pattern affects P300 generation. Patients with a positive outcome showed higher P300 amplitudes at frontal electrode sites than those with a negative outcome. P300 amplitude correlated with ADL score difference. Lesions in the superior temporal gyrus, middle and inferior frontal and prefrontal regions led to visibly diminished P300 amplitudes. The findings suggest that an impairment of attention (P300 amplitude reduction) negatively influences successful neurological rehabilitation. Left superior temporal lobe and the left premotor/prefrontal areas are essential brain areas for the generation of the P300. P300 amplitude may be used as an outcome predictor for severely impaired patients suffering from middle cerebral artery strokes or hemorrhages. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Workaholism vs. work engagement: the two different predictors of future well-being and performance.

PubMed

Shimazu, Akihito; Schaufeli, Wilmar B; Kamiyama, Kimika; Kawakami, Norito

2015-02-01

This study investigated the distinctiveness of two types of heavy work investment (i.e., workaholism and work engagement) by examining their 2-year longitudinal relationships with employee well-being and job performance. Based on a previous cross-sectional study by Shimazu and Schaufeli (Ind Health 47:495-502, 2009) and a shorter term longitudinal study by Shimazu et al. (Ind Health 50:316-21, 2012; measurement interval = 7 months), we predicted that workaholism predicts long-term future unwell-being (i.e., high ill-health and low life satisfaction) and poor job performance, whereas work engagement predicts future well-being (i.e., low ill-health and high life satisfaction) and superior job performance. A two-wave survey was conducted among employees from one Japanese company, and valid data from 1,196 employees was analyzed using structural equation modeling. T1-T2 changes in ill-health, life satisfaction, and job performance were measured as residual scores, which were included in the structural equation model. Workaholism and work engagement were weakly and positively related to each other. In addition, and as expected, workaholism was related to an increase in ill-health and to a decrease in life satisfaction. In contrast, and also as expected, work engagement was related to increases in both life satisfaction and job performance and to a decrease in ill-health. Although workaholism and work engagement are weakly positively related, they constitute two different concepts. More specifically, workaholism has negative consequences across an extended period of 2 years, whereas work engagement has positive consequences in terms of well-being and performance. Hence, workaholism should be prevented and work engagement should be stimulated.

A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.

PubMed

Ni, Qianwu; Chen, Lei

2017-01-01

Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

PARTS: Probabilistic Alignment for RNA joinT Secondary structure prediction

PubMed Central

Harmanci, Arif Ozgun; Sharma, Gaurav; Mathews, David H.

2008-01-01

A novel method is presented for joint prediction of alignment and common secondary structures of two RNA sequences. The joint consideration of common secondary structures and alignment is accomplished by structural alignment over a search space defined by the newly introduced motif called matched helical regions. The matched helical region formulation generalizes previously employed constraints for structural alignment and thereby better accommodates the structural variability within RNA families. A probabilistic model based on pseudo free energies obtained from precomputed base pairing and alignment probabilities is utilized for scoring structural alignments. Maximum a posteriori (MAP) common secondary structures, sequence alignment and joint posterior probabilities of base pairing are obtained from the model via a dynamic programming algorithm called PARTS. The advantage of the more general structural alignment of PARTS is seen in secondary structure predictions for the RNase P family. For this family, the PARTS MAP predictions of secondary structures and alignment perform significantly better than prior methods that utilize a more restrictive structural alignment model. For the tRNA and 5S rRNA families, the richer structural alignment model of PARTS does not offer a benefit and the method therefore performs comparably with existing alternatives. For all RNA families studied, the posterior probability estimates obtained from PARTS offer an improvement over posterior probability estimates from a single sequence prediction. When considering the base pairings predicted over a threshold value of confidence, the combination of sensitivity and positive predictive value is superior for PARTS than for the single sequence prediction. PARTS source code is available for download under the GNU public license at http://rna.urmc.rochester.edu. PMID:18304945

Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors

PubMed Central

Guo, Maozu; Guo, Yahong; Li, Jinbao; Ding, Jian; Liu, Yong; Dai, Qiguo; Li, Jin; Teng, Zhixia; Huang, Yufei

2013-01-01

Background The identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases. It is useful in providing reliable disease miRNA candidates for subsequent experimental studies. Methodology/Principal Findings It is known that miRNAs with similar functions are often associated with similar diseases and vice versa. Therefore, the functional similarity of two miRNAs has been successfully estimated by measuring the semantic similarity of their associated diseases. To effectively predict disease miRNAs, we calculated the functional similarity by incorporating the information content of disease terms and phenotype similarity between diseases. Furthermore, the members of miRNA family or cluster are assigned higher weight since they are more probably associated with similar diseases. A new prediction method, HDMP, based on weighted k most similar neighbors is presented for predicting disease miRNAs. Experiments validated that HDMP achieved significantly higher prediction performance than existing methods. In addition, the case studies examining prostatic neoplasms, breast neoplasms, and lung neoplasms, showed that HDMP can uncover potential disease miRNA candidates. Conclusions The superior performance of HDMP can be attributed to the accurate measurement of miRNA functional similarity, the weight assignment based on miRNA family or cluster, and the effective prediction based on weighted k most similar neighbors. The online prediction and analysis tool is freely available at http://nclab.hit.edu.cn/hdmpred. PMID:23950912

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

PubMed

Shang, Qiang; Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

2016-01-01

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine

PubMed Central

Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

2016-01-01

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust. PMID:27551829

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction

PubMed Central

Cruz-Cano, Raul; Chew, David S.H.; Kwok-Pui, Choi; Ming-Ying, Leung

2010-01-01

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications. PMID:20729987

Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction.

PubMed

Cruz-Cano, Raul; Chew, David S H; Kwok-Pui, Choi; Ming-Ying, Leung

2010-06-01

Replication of their DNA genomes is a central step in the reproduction of many viruses. Procedures to find replication origins, which are initiation sites of the DNA replication process, are therefore of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been tested within the family of herpesviruses. This paper proposes a new approach by least-squares support vector machines (LS-SVMs) and tests its performance not only on the herpes family but also on a collection of caudoviruses coming from three viral families under the order of caudovirales. The LS-SVM approach provides sensitivities and positive predictive values superior or comparable to those given by the previous methods. When suitably combined with previous methods, the LS-SVM approach further improves the prediction accuracy for the herpesvirus replication origins. Furthermore, by recursive feature elimination, the LS-SVM has also helped find the most significant features of the data sets. The results suggest that the LS-SVMs will be a highly useful addition to the set of computational tools for viral replication origin prediction and illustrate the value of optimization-based computing techniques in biomedical applications.

Prediction of exercise in patients across various stages of bariatric surgery: a comparison of the merits of the theory of reasoned action versus the theory of planned behavior.

PubMed

Hunt, Hillary R; Gross, Alan M

2009-11-01

Obesity is a world-wide health concern approaching epidemic proportions. Successful long-term treatment involves a combination of bariatric surgery, diet, and exercise. Social cognitive models, such as the Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB), are among the most commonly tested theories utilized in the prediction of exercise. As exercise is not a completely volitional behavior, it is hypothesized that the TPB is a superior theoretical model for the prediction of exercise intentions and behavior. This study tested validity of the TPB in a sample of bariatric patients and further validated its improvement over the TRA in predicting exercise adherence at different operative stages. Results generally confirmed research hypotheses. Superiority of the TPB model was validated in this sample of bariatric patients, and Perceived Behavioral Control emerged as the single-best predictor of both exercise intentions and self-reported behavior. Finally, results suggested that both subjective norms and attitudes toward exercise played a larger role in the prediction of intention and behavior than previously reported.

A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes.

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2016-01-01

Sediment transport without deposition is an essential consideration in the optimum design of sewer pipes. In this study, a novel method based on a combination of support vector regression (SVR) and the firefly algorithm (FFA) is proposed to predict the minimum velocity required to avoid sediment settling in pipe channels, which is expressed as the densimetric Froude number (Fr). The efficiency of support vector machine (SVM) models depends on the suitable selection of SVM parameters. In this particular study, FFA is used by determining these SVM parameters. The actual effective parameters on Fr calculation are generally identified by employing dimensional analysis. The different dimensionless variables along with the models are introduced. The best performance is attributed to the model that employs the sediment volumetric concentration (C(V)), ratio of relative median diameter of particles to hydraulic radius (d/R), dimensionless particle number (D(gr)) and overall sediment friction factor (λ(s)) parameters to estimate Fr. The performance of the SVR-FFA model is compared with genetic programming, artificial neural network and existing regression-based equations. The results indicate the superior performance of SVR-FFA (mean absolute percentage error = 2.123%; root mean square error =0.116) compared with other methods.

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting.

PubMed

Alamaniotis, Miltiadis; Bargiotas, Dimitrios; Tsoukalas, Lefteri H

2016-01-01

Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

Meta-path based heterogeneous combat network link prediction

NASA Astrophysics Data System (ADS)

Li, Jichao; Ge, Bingfeng; Yang, Kewei; Chen, Yingwu; Tan, Yuejin

2017-09-01

The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

Dimensions of Manic Symptoms in Youth: Psychosocial Impairment and Cognitive Performance in the IMAGEN Sample

ERIC Educational Resources Information Center

Stringaris, Argyris; Castellanos-Ryan, Natalie; Banaschewski, Tobias; Barker, Gareth J.; Bokde, Arun L.; Bromberg, Uli; Büchel, Christian; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Juergen; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Itterman, Bernd; Lawrence, Claire; Nees, Frauke; Paillere-Martinot, Marie-Laure; Paus, Tomas; Pausova, Zdenka; Rietschel, Marcella; Smolka, Michael N.; Schumann, Gunter; Goodman, Robert; Conrod, Patricia

2014-01-01

Background: It has been reported that mania may be associated with superior cognitive performance. In this study, we test the hypothesis that manic symptoms in youth separate along two correlated dimensions and that a symptom constellation of high energy and cheerfulness is associated with superior cognitive performance. Method: We studied 1755…

The Role of the Caudal Superior Parietal Lobule in Updating Hand Location in Peripheral Vision: Further Evidence from Optic Ataxia

PubMed Central

Granek, Joshua A.; Pisella, Laure; Blangero, Annabelle; Rossetti, Yves; Sergio, Lauren E.

2012-01-01

Patients with optic ataxia (OA), who are missing the caudal portion of their superior parietal lobule (SPL), have difficulty performing visually-guided reaches towards extra-foveal targets. Such gaze and hand decoupling also occurs in commonly performed non-standard visuomotor transformations such as the use of a computer mouse. In this study, we test two unilateral OA patients in conditions of 1) a change in the physical location of the visual stimulus relative to the plane of the limb movement, 2) a cue that signals a required limb movement 180° opposite to the cued visual target location, or 3) both of these situations combined. In these non-standard visuomotor transformations, the OA deficit is not observed as the well-documented field-dependent misreach. Instead, OA patients make additional eye movements to update hand and goal location during motor execution in order to complete these slow movements. Overall, the OA patients struggled when having to guide centrifugal movements in peripheral vision, even when they were instructed from visual stimuli that could be foveated. We propose that an intact caudal SPL is crucial for any visuomotor control that involves updating ongoing hand location in space without foveating it, i.e. from peripheral vision, proprioceptive or predictive information. PMID:23071599

The utility of Xpert MTB/RIF performed on bronchial washings obtained in patients with suspected pulmonary tuberculosis in a high prevalence setting.

PubMed

Barnard, Dewald A; Irusen, Elvis M; Bruwer, Johannes W; Plekker, Danté; Whitelaw, Andrew C; Deetlefs, Jacobus D; Koegelenberg, Coenraad F N

2015-09-16

Xpert MTB/RIF has been shown to have a superior sensitivity to microscopy for acid fast bacilli (AFB) in sputum and has been recommended as a standard first line investigation for pulmonary tuberculosis (PTB). Bronchoscopy is a valuable tool in diagnosing PTB in sputum negative patients. There is limited data on the utility of Xpert MTB/RIF performed on bronchial lavage specimens. Our aim was to evaluate the diagnostic efficiency of Xpert MTB/RIF performed on bronchial washings in sputum scarce/negative patients with suspected PTB. All patients with a clinical and radiological suspicion of PTB who underwent bronchoscopy between January 2013 and April 2014 were included. The diagnostic efficiencies of Xpert MTB/RIF and microscopy for AFB were compared to culture for Mycobacterium tuberculosis. Thirty nine of 112 patients were diagnosed with culture-positive PTB. Xpert MTB/RIF was positive in 36/39 with a sensitivity of 92.3% (95% CI 78-98%) for PTB, which was superior to that of smear microscopy (41%; 95% CI 26.0-57.8%, p = 0.005). The specificities of Xpert MTB/RIF and smear microscopy were 87.7% (95% CI 77.4-93.9%) and 98.6% (95% CI 91.6%-99.9%) respectively. Xpert MTB/RIF had a positive predictive value of 80% (95% CI; 65-89.9%) and negative predictive value of 95.5% (95% CI 86.6-98.8%). 3/9 patients with Xpert MTB/RIF positive culture negative results were treated for PTB based on clinical and radiological findings. Xpert MTB/RIF has a higher sensitivity than smear microscopy and similar specificity for the immediate confirmation of PTB in specimens obtained by bronchial washing, and should be utilised in patients with a high suspicion of pulmonary tuberculosis.

A Novel Local Learning based Approach With Application to Breast Cancer Diagnosis

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

Xu, Songhua; Tourassi, Georgia

2012-01-01

The purpose of this study is to develop and evaluate a novel local learning-based approach for computer-assisted diagnosis of breast cancer. Our new local learning based algorithm using the linear logistic regression method as its base learner is described. Overall, our algorithm will perform its stochastic searching process until the total allowed computing time is used up by our random walk process in identifying the most suitable population subdivision scheme and their corresponding individual base learners. The proposed local learning-based approach was applied for the prediction of breast cancer given 11 mammographic and clinical findings reported by physicians using themore » BI-RADS lexicon. Our database consisted of 850 patients with biopsy confirmed diagnosis (290 malignant and 560 benign). We also compared the performance of our method with a collection of publicly available state-of-the-art machine learning methods. Predictive performance for all classifiers was evaluated using 10-fold cross validation and Receiver Operating Characteristics (ROC) analysis. Figure 1 reports the performance of 54 machine learning methods implemented in the machine learning toolkit Weka (version 3.0). We introduced a novel local learning-based classifier and compared it with an extensive list of other classifiers for the problem of breast cancer diagnosis. Our experiments show that the algorithm superior prediction performance outperforming a wide range of other well established machine learning techniques. Our conclusion complements the existing understanding in the machine learning field that local learning may capture complicated, non-linear relationships exhibited by real-world datasets.« less

Heart Transplant in Patient With Isolated Left Superior Vena Cava by Atrial Appendage Rotation.

PubMed

Reyes, Karl M; Gupta, Dipankar; Fricker, Frederick Jay; Cooke, Susan; Bleiweis, Mark S

2018-06-01

Orthotopic heart transplantation in patients with an isolated persistent left superior vena cava is extremely rare, and the anastomotic connection between a right-sided donor superior vena cava and left-sided recipient superior vena cava can be challenging to perform. We present a novel technique used in an infant female, using the left atrial appendage to extend the superior vena cava anastomosis. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

A Novel Quasi-3D Method for Cascade Flow Considering Axial Velocity Density Ratio

NASA Astrophysics Data System (ADS)

Chen, Zhiqiang; Zhou, Ming; Xu, Quanyong; Huang, Xudong

2018-03-01

A novel quasi-3D Computational Fluid Dynamics (CFD) method of mid-span flow simulation for compressor cascades is proposed. Two dimension (2D) Reynolds-Averaged Navier-Stokes (RANS) method is shown facing challenge in predicting mid-span flow with a unity Axial Velocity Density Ratio (AVDR). Three dimension (3D) RANS solution also shows distinct discrepancies if the AVDR is not predicted correctly. In this paper, 2D and 3D CFD results discrepancies are analyzed and a novel quasi-3D CFD method is proposed. The new quasi-3D model is derived by reducing 3D RANS Finite Volume Method (FVM) discretization over a one-spanwise-layer structured mesh cell. The sidewall effect is considered by two parts. The first part is explicit interface fluxes of mass, momentum and energy as well as turbulence. The second part is a cell boundary scaling factor representing sidewall boundary layer contraction. The performance of the novel quasi-3D method is validated on mid-span pressure distribution, pressure loss and shock prediction of two typical cascades. The results show good agreement with the experiment data on cascade SJ301-20 and cascade AC6-10 at all test condition. The proposed quasi-3D method shows superior accuracy over traditional 2D RANS method and 3D RANS method in performance prediction of compressor cascade.

Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon.

PubMed

Benndorf, Matthias; Kotter, Elmar; Langer, Mathias; Herda, Christoph; Wu, Yirong; Burnside, Elizabeth S

2015-06-01

To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. We used separate training data (1,276 lesions, 138 malignant) and validation data (1,177 lesions, 175 malignant). We created naïve Bayes (NB) classifiers from the training data with tenfold cross-validation. Our "inclusive model" comprised BI-RADS categories, BI-RADS descriptors, and age as predictive variables; our "descriptor model" comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. In the training data, the inclusive model yields an AUC of 0.959; the descriptor model yields an AUC of 0.910 (P < 0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P < 0.001); the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935; the descriptor model yields an AUC of 0.876 (P < 0.001). Again, the inclusive model is superior to the clinical performance (P < 0.001); the descriptor model performs similarly. We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html . • We provide a decision support tool for mammographic masses at www.ebm-radiology.com/nbmm/index.html . • Our tool may reduce variability of practice in BI-RADS category assignment. • A formal analysis of BI-RADS descriptors may enhance radiologists' diagnostic performance.

46 CFR 401.410 - Basic rates and charges on Lakes Huron, Michigan and Superior and the St. Mary's River.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Superior and the St. Mary's River. 401.410 Section 401.410 Shipping COAST GUARD (GREAT LAKES PILOTAGE... Services § 401.410 Basic rates and charges on Lakes Huron, Michigan and Superior and the St. Mary's River... performed by U.S. registered pilots on Lakes Huron, Michigan, and Superior and the St. Mary's River. (a...

The development of anti-heat stress clothing for construction workers in hot and humid weather.

PubMed

Chan, Albert P C; Guo, Y P; Wong, Francis K W; Li, Y; Sun, S; Han, X

2016-04-01

The purpose of this study was to develop anti-heat stress clothing for construction workers in hot and humid weather. Following DeJonge's functional clothing design process, the design situation was explored, including clothing fabric heat/moisture transporting properties and UV protection and the aspects of clothing ergonomic design (mobility, convenience, and safety). The problem structure was derived from the results of the surveys in three local construction sites, which agreed well with the task requirements and observations. Specifications were consequently described and 30 commercially available fabrics were identified and tested. Fabric testing data and design considerations were inputted in S-smart system to predict the thermal functional performance of the clothing. A new uniform prototype was developed and evaluated. The results of all measurements suggest that the new uniform which incorporated fabrics with superior heat/moisture transporting properties and loose-fitting design could reduce the workers' heat stress and improve their comfort and work performance. Practitioner Summary: The construction workers' uniform currently used in Hong Kong during summer was unsatisfactory. Following DeJonge's functional clothing design process, an anti-heat stress uniform was developed by testing 30 fabrics and predicting clothing thermal functional performance using S-smart system. The new uniform could reduce the workers' heat stress and improve their comfort and work performance.

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

PubMed

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

2015-08-01

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

Clinical Utility of Urinary Cytology to Detect BK Viral Nephropathy.

PubMed

Nankivell, Brian J; Renthawa, Jasveen; Jeoffreys, Neisha; Kable, Kathy; O'Connell, Philip J; Chapman, Jeremy R; Wong, Germaine; Sharma, Raghwa N

2015-08-01

Reactivation of BK polyoma virus can result in destructive viral allograft nephropathy (BKVAN) with limited treatment options. Screening programs using surrogate markers of viral replication are important preventive strategies, guiding immunosuppression reduction. We prospectively evaluated the diagnostic test performance of urinary decoy cells and urinary SV40T immunochemistry of exfoliated cells, to screen for BKVAN, (defined by reference histology with SV40 immunohistochemistry, n = 704 samples), compared with quantitative viremia, from 211 kidney and 141 kidney-pancreas transplant recipients. The disease prevalence of BKVAN was 2.6%. Decoy cells occurred in 95 of 704 (13.5%) samples, with a sensitivity of 66.7%, specificity of 88.6%, positive predictive value (PPV) of 11.7%, and negative predictive value of 98.5% to predict histologically proven BKVAN. Quantification of decoy cells improved the PPV to 32.1% (10 ≥ cells threshold). Immunohistochemical staining of urinary exfoliated cells for SV40T improved sensitivity to 85.7%, detecting atypical or degenerate infected cells (specificity of 92.3% and PPV of 33.3%), but was hampered by technical failures. Viremia occurred in 90 of 704 (12.8%) with sensitivity of 96.3%, specificity of 90.3%, PPV of 31.5%, and negative predictive value of 99.8%. The receiver-operator curve performance of quantitative viremia surpassed decoy cells (area under the curve of 0.95 and 0.79, respectively, P = 0.0018 for differences). Combining decoy cell and BK viremia in a diagnostic matrix improved prediction of BKVAN and diagnostic risk stratification, especially for high-level positive results. Although quantified decoy cells are acceptable surrogate markers of BK viral replication with unexceptional test performances, quantitative viremia displayed superior test characteristics and is suggested as the screening test of choice.

High-Performance Sensors Based on Resistance Fluctuations of Single-Layer-Graphene Transistors.

PubMed

Amin, Kazi Rafsanjani; Bid, Aveek

2015-09-09

One of the most interesting predicted applications of graphene-monolayer-based devices is as high-quality sensors. In this article, we show, through systematic experiments, a chemical vapor sensor based on the measurement of low-frequency resistance fluctuations of single-layer-graphene field-effect-transistor devices. The sensor has extremely high sensitivity, very high specificity, high fidelity, and fast response times. The performance of the device using this scheme of measurement (which uses resistance fluctuations as the detection parameter) is more than 2 orders of magnitude better than a detection scheme in which changes in the average value of the resistance is monitored. We propose a number-density-fluctuation-based model to explain the superior characteristics of a noise-measurement-based detection scheme presented in this article.

Plasma properties in electron-bombardment ion thrusters

NASA Technical Reports Server (NTRS)

Matossian, J. N.; Beattie, J. R.

1987-01-01

The paper describes a technique for computing volume-averaged plasma properties within electron-bombardment ion thrusters, using spatially varying Langmuir-probe measurements. Average values of the electron densities are defined by integrating the spatially varying Maxwellian and primary electron densities over the ionization volume, and then dividing by the volume. Plasma properties obtained in the 30-cm-diameter J-series and ring-cusp thrusters are analyzed by the volume-averaging technique. The superior performance exhibited by the ring-cusp thruster is correlated with a higher average Maxwellian electron temperature. The ring-cusp thruster maintains the same fraction of primary electrons as does the J-series thruster, but at a much lower ion production cost. The volume-averaged predictions for both thrusters are compared with those of a detailed thruster performance model.

A longitudinal study: changes in cortical thickness and surface area during pubertal maturation.

PubMed

Herting, Megan M; Gautam, Prapti; Spielberg, Jeffrey M; Dahl, Ronald E; Sowell, Elizabeth R

2015-01-01

Sex hormones have been shown to contribute to the organization and function of the brain during puberty and adolescence. Moreover, it has been suggested that distinct hormone changes in girls versus boys may contribute to the emergence of sex differences in internalizing and externalizing behavior during adolescence. In the current longitudinal study, the influence of within-subject changes in puberty (physical and hormonal) on cortical thickness and surface area was examined across a 2-year span, while controlling for age. Greater increases in Tanner Stage predicted less superior frontal thinning and decreases in precuneus surface area in both sexes. Significant Tanner Stage and sex interactions were also seen, with less right superior temporal thinning in girls but not boys, as well as greater decreases in the right bank of the superior temporal sulcus surface area in boys compared to girls. In addition, within-subject changes in testosterone over the 2-year follow-up period were found to relate to decreases in middle superior frontal surface area in boys, but increases in surface area in girls. Lastly, larger increases in estradiol in girls predicted greater middle temporal lobe thinning. These results show that within-subject physical and hormonal markers of puberty relate to region and sex-specific changes in cortical development across adolescence.

Predicting preload responsiveness using simultaneous recordings of inferior and superior vena cavae diameters.

PubMed

Charbonneau, Hélène; Riu, Béatrice; Faron, Matthieu; Mari, Arnaud; Kurrek, Matt M; Ruiz, Jean; Geeraerts, Thomas; Fourcade, Olivier; Genestal, Michèle; Silva, Stein

2014-09-05

Echocardiographic indices based on respiratory variations of superior and inferior vena cavae diameters (ΔSVC and ΔIVC, respectively) have been proposed as predictors of fluid responsiveness in mechanically ventilated patients, but they have never been compared simultaneously in the same patient sample. The aim of this study was to compare the predictive value of these echocardiographic indices when concomitantly recorded in mechanically ventilated septic patients. Septic shock patients requiring hemodynamic monitoring were prospectively enrolled over a 1-year period in a mixed medical surgical ICU of a university teaching hospital (Toulouse, France). All patients were mechanically ventilated. Predictive indices were obtained by transesophageal and transthoracic echocardiography and were calculated as follows: (Dmax - Dmin)/Dmax for ΔSVC and (Dmax - Dmin)/Dmin for ΔIVC, where Dmax and Dmin are the maximal and minimal diameters of SVC and IVC. Measurements were performed at baseline and after a 7-ml/kg volume expansion using a plasma expander. Patients were separated into responders (increase in cardiac index ≥15%) and nonresponders (increase in cardiac index <15%). Among 44 included patients, 26 (59%) patients were responders (R). ΔSVC was significantly more accurate than ΔIVC in predicting fluid responsiveness. The areas under the receiver operating characteristic curves for ΔSVC and ΔIVC regarding assessment of fluid responsiveness were significantly different (0.74 (95% confidence interval (CI): 0.59 to 0.88) and 0.43 (95% CI: 0.25 to 0.61), respectively (P = 0.012)). No significant correlation between ΔSVC and ΔIVC was found (r = 0.005, P = 0.98). The best threshold values for discriminating R from NR was 29% for ΔSVC, with 54% sensitivity and 89% specificity, and 21% for ΔIVC, with 38% sensitivity and 61% specificity. ΔSVC was better than ΔIVC in predicting fluid responsiveness in our cohort. It is worth noting that the sensitivity and specificity values of ΔSVC and ΔIVC for predicting fluid responsiveness were lower than those reported in the literature, highlighting the limits of using these indices in a heterogeneous sample of medical and surgical septic patients.

Identifying Wrist Fracture Patients with High Accuracy by Automatic Categorization of X-ray Reports

PubMed Central

de Bruijn, Berry; Cranney, Ann; O’Donnell, Siobhan; Martin, Joel D.; Forster, Alan J.

2006-01-01

The authors performed this study to determine the accuracy of several text classification methods to categorize wrist x-ray reports. We randomly sampled 751 textual wrist x-ray reports. Two expert reviewers rated the presence (n = 301) or absence (n = 450) of an acute fracture of wrist. We developed two information retrieval (IR) text classification methods and a machine learning method using a support vector machine (TC-1). In cross-validation on the derivation set (n = 493), TC-1 outperformed the two IR based methods and six benchmark classifiers, including Naive Bayes and a Neural Network. In the validation set (n = 258), TC-1 demonstrated consistent performance with 93.8% accuracy; 95.5% sensitivity; 92.9% specificity; and 87.5% positive predictive value. TC-1 was easy to implement and superior in performance to the other classification methods. PMID:16929046

The effect of action video game playing on sensorimotor learning: Evidence from a movement tracking task.

PubMed

Gozli, Davood G; Bavelier, Daphne; Pratt, Jay

2014-10-12

Research on the impact of action video game playing has revealed performance advantages on a wide range of perceptual and cognitive tasks. It is not known, however, if playing such games confers similar advantages in sensorimotor learning. To address this issue, the present study used a manual motion-tracking task that allowed for a sensitive measure of both accuracy and improvement over time. When the target motion pattern was consistent over trials, gamers improved with a faster rate and eventually outperformed non-gamers. Performance between the two groups, however, did not differ initially. When the target motion was inconsistent, changing on every trial, results revealed no difference between gamers and non-gamers. Together, our findings suggest that video game playing confers no reliable benefit in sensorimotor control, but it does enhance sensorimotor learning, enabling superior performance in tasks with consistent and predictable structure. Copyright © 2014. Published by Elsevier B.V.

Comparing measures of attachment: "To whom one turns in times of stress", parental warmth, and partner satisfaction.

PubMed

Lindberg, Marc A; Fugett, April; Thomas, Stuart W

2012-01-01

The Attachment and Clinical Issues Questionnaire (ACIQ; M. A. Lindberg & S. W. Thomas, 2011), was developed over an 18-year period containing 29 scales. The purpose of the present study was to test (a) the validity of the attachment scales in terms of how they predict to whom one turns in times of stress and for affective sharing, and (b) how the attachment scales compared with the Experiences in Close Relationship Questionnaire (ECR) in terms of concurrent, convergent, and discriminant evidence. The relevant secure scales of the ACIQ predicted to whom one turned in study 1, and study 2 demonstrated good convergent evidence with the ECR, but superior concurrent evidence in predicting partner satisfaction, and superior discriminant evidence in differentially correlating with mother and father warmth. Thus, the ACIQ passed essential validity and psychometric tests and was a more robust measure than the ECR with these defining characteristics of attachment.

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu

2015-07-23

The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]'), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal.

Surgical approach to left ventricular inflow obstruction due to dilated coronary sinus.

PubMed

Vargas, Florentino J; Rozenbaum, Jorge; Lopez, Ricardo; Granja, Miguel; De Dios, Ana; Zarlenga, Beatriz; Flores, Enrique; Fischman, Enrique; Kreutzer, Eduardo

2006-07-01

Left superior vena cava draining to a dilated coronary sinus can cause left ventricular inflow obstruction. Our purpose is to report 4 severely ill patients with this malformation who were operated upon and in whom repair was accomplished using an original surgical approach. An operative procedure was designed, which included complete resection of the wall of the coronary sinus along its entire extension in the left atrium; division of the left superior vena cava; and establishment of the left superior vena cava-right atrial continuity by a wide left superior vena cava-right atrial appendage anastomosis. The series included 1 patient with interrupted inferior vena cava-hemiazygous continuation to left superior vena cava. There were no deaths. Absence of residual left ventricular inflow obstruction was demonstrated at follow-up in all cases, together with an unobstructed left superior vena cava-right atrial appendage-right atrial connection. A predictable relief of the left ventricular inflow obstruction, together with preservation of an adequate drainage for the systemic venous return, were both achieved with this repair.

Core Cutting Test with Vertical Rock Cutting Rig (VRCR)

NASA Astrophysics Data System (ADS)

Yasar, Serdar; Osman Yilmaz, Ali

2017-12-01

Roadheaders are frequently used machines in mining and tunnelling, and performance prediction of roadheaders is important for project economics and stability. Several methods were proposed so far for this purpose and, rock cutting tests are the best choice. Rock cutting tests are generally divided into two groups which are namely, full scale rock cutting tests and small scale rock cutting tests. These two tests have some superiorities and deficiencies over themselves. However, in many cases, where rock sampling becomes problematic, small scale rock cutting test (core cutting test) is preferred for performance prediction, since small block samples and core samples can be conducted to rock cutting testing. Common problem for rock cutting tests are that they can be found in very limited research centres. In this study, a new mobile rock cutting testing equipment, vertical rock cutting rig (VRCR) was introduced. Standard testing procedure was conducted on seven rock samples which were the part of a former study on cutting rocks with another small scale rock cutting test. Results showed that core cutting test can be realized successfully with VRCR with the validation of paired samples t-test.

PharmDock: a pharmacophore-based docking program

PubMed Central

2014-01-01

Background Protein-based pharmacophore models are enriched with the information of potential interactions between ligands and the protein target. We have shown in a previous study that protein-based pharmacophore models can be applied for ligand pose prediction and pose ranking. In this publication, we present a new pharmacophore-based docking program PharmDock that combines pose sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function. Results Tests of PharmDock on ligand pose prediction, binding affinity estimation, compound ranking and virtual screening yielded comparable or better performance to existing and widely used docking programs. The docking program comes with an easy-to-use GUI within PyMOL. Two features have been incorporated in the program suite that allow for user-defined guidance of the docking process based on previous experimental data. Docking with those features demonstrated superior performance compared to unbiased docking. Conclusion A protein pharmacophore-based docking program, PharmDock, has been made available with a PyMOL plugin. PharmDock and the PyMOL plugin are freely available from http://people.pharmacy.purdue.edu/~mlill/software/pharmdock. PMID:24739488

Evaluation of a two-dimensional numerical model for air quality simulation in a street canyon

NASA Astrophysics Data System (ADS)

Okamoto, Shin `Ichi; Lin, Fu Chi; Yamada, Hiroaki; Shiozawa, Kiyoshige

For many urban areas, the most severe air pollution caused by automobile emissions appears along a road surrounded by tall buildings: the so=called street canyon. A practical two-dimensional numerical model has been developed to be applied to this kind of road structure. This model contains two submodels: a wind-field model and a diffusion model based on a Monte Carlo particle scheme. In order to evaluate the predictive performance of this model, an air quality simulation was carried out at three trunk roads in the Tokyo metropolitan area: Nishi-Shimbashi, Aoyama and Kanda-Nishikicho (using SF 6 as a tracer and NO x measurement). Since this model has two-dimensional properties and cannot be used for the parallel wind condition, the perpendicular wind condition was selected for the simulation. The correlation coefficients for the SF 6 and NO x data in Aoyama were 0.67 and 0.62, respectively. When predictive performance of this model is compared with other models, this model is comparable to the SRI model, and superior to the APPS three-dimensional numerical model.

Higher clinical performance during a surgical clerkship is independently associated with matriculation of medical students into general surgery.

PubMed

Daly, Shaun C; Deal, Rebecca A; Rinewalt, Daniel E; Francescatti, Amanda B; Luu, Minh B; Millikan, Keith W; Anderson, Mary C; Myers, Jonathan A

2014-04-01

The purpose of our study was to determine the predictive impact of individual academic measures for the matriculation of senior medical students into a general surgery residency. Academic records were evaluated for third-year medical students (n = 781) at a single institution between 2004 and 2011. Cohorts were defined by student matriculation into either a general surgery residency program (n = 58) or a non-general surgery residency program (n = 723). Multivariate logistic regression was performed to evaluate independently significant academic measures. Clinical evaluation raw scores were predictive of general surgery matriculation (P = .014). In addition, multivariate modeling showed lower United States Medical Licensing Examination Step 1 scores to be independently associated with matriculation into general surgery (P = .007). Superior clinical aptitude is independently associated with general surgical matriculation. This is in contrast to the negative correlation United States Medical Licensing Examination Step 1 scores have on general surgery matriculation. Recognizing this, surgical clerkship directors can offer opportunities for continued surgical education to students showing high clinical aptitude, increasing their likelihood of surgical matriculation. Copyright © 2014 Elsevier Inc. All rights reserved.

Endovascular Treatment of Malignant Superior Vena Cava Syndrome: Results and Predictive Factors of Clinical Efficacy

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

Fagedet, Dorothee, E-mail: DFagedet@chu-grenoble.fr; Thony, Frederic, E-mail: FThony@chu-grenoble.fr; Timsit, Jean-Francois, E-mail: JFTimsit@chu-grenoble.fr

To demonstrate the effectiveness of endovascular treatment (EVT) with self-expandable bare stents for malignant superior vena cava syndrome (SVCS) and to analyze predictive factors of EVT efficacy. Retrospective review of the 164 patients with malignant SVCS treated with EVT in our hospital from August 1992 to December 2007 and followed until February 2009. Endovascular treatment includes angioplasty before and after stent placement. We used self-expandable bare stents. We studied results of this treatment and looked for predictive factors of clinical efficacy, recurrence, and complications by statistical analysis. Endovascular treatment was clinically successful in 95% of cases, with an acceptable ratemore » of early mortality (2.4%). Thrombosis of the superior vena cava was the only independent factor for EVT failure. The use of stents over 16 mm in diameter was a predictive factor for complications (P = 0.008). Twenty-one complications (12.8%) occurred during the follow-up period. Relapse occurred in 36 patients (21.9%), with effective restenting in 75% of cases. Recurrence of SVCS was significantly increased in cases of occlusion (P = 0.01), initial associated thrombosis (P = 0.006), or use of steel stents (P = 0.004). Long-term anticoagulant therapy did not influence the risk of recurrence or complications. In malignancy, EVT with self-expandable bare stents is an effective SVCS therapy. These results prompt us to propose treatment with stents earlier in the clinical course of patients with SVCS and to avoid dilatation greater than 16 mm.« less

Cell-specific prediction and application of drug-induced gene expression profiles.

PubMed

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

2018-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Cell-specific prediction and application of drug-induced gene expression profiles

PubMed Central

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R.; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David

2017-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes. PMID:29218867

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

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

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

Prediction of p38 map kinase inhibitory activity of 3, 4-dihydropyrido [3, 2-d] pyrimidone derivatives using an expert system based on principal component analysis and least square support vector machine

PubMed Central

Shahlaei, M.; Saghaie, L.

2014-01-01

A quantitative structure–activity relationship (QSAR) study is suggested for the prediction of biological activity (pIC50) of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors. Modeling of the biological activities of compounds of interest as a function of molecular structures was established by means of principal component analysis (PCA) and least square support vector machine (LS-SVM) methods. The results showed that the pIC50 values calculated by LS-SVM are in good agreement with the experimental data, and the performance of the LS-SVM regression model is superior to the PCA-based model. The developed LS-SVM model was applied for the prediction of the biological activities of pyrimidone derivatives, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.460 for LS-SVM. The study provided a novel and effective approach for predicting biological activities of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors and disclosed that LS-SVM can be used as a powerful chemometrics tool for QSAR studies. PMID:26339262

A study of Minnesota forests and lakes using data from Earth Resources Technology Satellites

NASA Technical Reports Server (NTRS)

1974-01-01

Highlights of research and practical benefits are discussed for the following projects which utilized ERTS 1 data to provide municipal, state, federal, and industrial users with environmental resource information for the state of Minnesota: (1) forest disease detection and control; (2) evaluation of water quality by remote sensing techniques; (3) forest vegetation classification and management; (4) detection of saline soils in the Red River Valley; (5) snowmelt flood prediction; (6) remote sensing applications to hydrology; (7) Rice Creek watershed project; (8) water quality in Lake Superior and the Duluth Superior Harbor; and (9) determination of Lake Superior currents from turbidity patterns.

Amorphous Red Phosphorus Embedded in Sandwiched Porous Carbon Enabling Superior Sodium Storage Performances.

PubMed

Wu, Ying; Liu, Zheng; Zhong, Xiongwu; Cheng, Xiaolong; Fan, Zhuangjun; Yu, Yan

2018-03-01

The red P anode for sodium ion batteries has attracted great attention recently due to the high theoretical capacity, but the poor intrinsic electronic conductivity and large volume expansion restrain its widespread applications. Herein, the red P is successfully encapsulated into the cube shaped sandwich-like interconnected porous carbon building (denoted as P@C-GO/MOF-5) via the vaporization-condensation method. Superior cycling stability (high capacity retention of about 93% at 2 A g -1 after 100 cycles) and excellent rate performance (502 mAh g -1 at 10 A g -1 ) can be obtained for the P@C-GO/MOF-5 electrode. The superior electrochemical performance can be ascribed to the successful incorporation of red P into the unique carbon matrix with large surface area and pore volume, interconnected porous structure, excellent electronic conductivity and superior structural stability. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Imaging-based biomarkers of cognitive performance in older adults constructed via high-dimensional pattern regression applied to MRI and PET.

PubMed

Wang, Ying; Goh, Joshua O; Resnick, Susan M; Davatzikos, Christos

2013-01-01

In this study, we used high-dimensional pattern regression methods based on structural (gray and white matter; GM and WM) and functional (positron emission tomography of regional cerebral blood flow; PET) brain data to identify cross-sectional imaging biomarkers of cognitive performance in cognitively normal older adults from the Baltimore Longitudinal Study of Aging (BLSA). We focused on specific components of executive and memory domains known to decline with aging, including manipulation, semantic retrieval, long-term memory (LTM), and short-term memory (STM). For each imaging modality, brain regions associated with each cognitive domain were generated by adaptive regional clustering. A relevance vector machine was adopted to model the nonlinear continuous relationship between brain regions and cognitive performance, with cross-validation to select the most informative brain regions (using recursive feature elimination) as imaging biomarkers and optimize model parameters. Predicted cognitive scores using our regression algorithm based on the resulting brain regions correlated well with actual performance. Also, regression models obtained using combined GM, WM, and PET imaging modalities outperformed models based on single modalities. Imaging biomarkers related to memory performance included the orbito-frontal and medial temporal cortical regions with LTM showing stronger correlation with the temporal lobe than STM. Brain regions predicting executive performance included orbito-frontal, and occipito-temporal areas. The PET modality had higher contribution to most cognitive domains except manipulation, which had higher WM contribution from the superior longitudinal fasciculus and the genu of the corpus callosum. These findings based on machine-learning methods demonstrate the importance of combining structural and functional imaging data in understanding complex cognitive mechanisms and also their potential usage as biomarkers that predict cognitive status.

Transient Elastography is Superior to FIB-4 in Assessing the Risk of Hepatocellular Carcinoma in Patients With Chronic Hepatitis B

PubMed Central

Kim, Seung Up; Kim, Beom Kyung; Park, Jun Yong; Kim, Do Young; Ahn, Sang Hoon; Song, Kijun; Han, Kwang-Hyub

2016-01-01

Abstract Liver stiffness (LS), assessed using transient elastography (TE), and (FIB-4) can both estimate the risk of developing hepatocellular carcinoma (HCC). We compared prognostic performances of LS and FIB-4 to predict HCC development in patients with chronic hepatitis B (CHB). Data from 1308 patients with CHB, who underwent TE, were retrospectively analyzed. FIB-4 was calculated for all patients. The cumulative rate of HCC development was assessed using Kaplan–Meier curves. The predictive performances of LS and FIB-4 were evaluated using time-dependent receiver-operating characteristic (ROC) curves. The mean age (883 men) was 50 years. During follow-up (median 6.1 years), 119 patients developed HCC. The areas under the ROC curves (AUROCs) predicting HCC risk at 3, 5, and 7 years were consistently greater for LS than for FIB-4 (0.791–0.807 vs 0.691–0.725; all P < 0.05). Similarly, when the respective AUROCs for LS and FIB-4 at every time point during the 7-year follow-up were plotted, LS also showed consistently better performance than FIB-4 after 1 year of enrollment. The combined use of LS and FIB-4 significantly enhanced the prognostic performance compared with the use of FIB-4 alone (P < 0.05), but the performance of the combined scores was statistically similar to that of LS alone (P > 0.05). LS showed significantly better performance than FIB-4 in assessing the risk of HCC development, and the combined use of LS and FIB-4 did not provide additional benefit compared with the use of LS alone. Hence, LS assessed using TE might be helpful for optimizing HCC surveillance strategies. PMID:27196449

Transient Elastography is Superior to FIB-4 in Assessing the Risk of Hepatocellular Carcinoma in Patients With Chronic Hepatitis B.

PubMed

Kim, Seung Up; Kim, Beom Kyung; Park, Jun Yong; Kim, Do Young; Ahn, Sang Hoon; Song, Kijun; Han, Kwang-Hyub

2016-05-01

Liver stiffness (LS), assessed using transient elastography (TE), and (FIB-4) can both estimate the risk of developing hepatocellular carcinoma (HCC). We compared prognostic performances of LS and FIB-4 to predict HCC development in patients with chronic hepatitis B (CHB).Data from 1308 patients with CHB, who underwent TE, were retrospectively analyzed. FIB-4 was calculated for all patients. The cumulative rate of HCC development was assessed using Kaplan-Meier curves. The predictive performances of LS and FIB-4 were evaluated using time-dependent receiver-operating characteristic (ROC) curves.The mean age (883 men) was 50 years. During follow-up (median 6.1 years), 119 patients developed HCC. The areas under the ROC curves (AUROCs) predicting HCC risk at 3, 5, and 7 years were consistently greater for LS than for FIB-4 (0.791-0.807 vs 0.691-0.725; all P < 0.05). Similarly, when the respective AUROCs for LS and FIB-4 at every time point during the 7-year follow-up were plotted, LS also showed consistently better performance than FIB-4 after 1 year of enrollment. The combined use of LS and FIB-4 significantly enhanced the prognostic performance compared with the use of FIB-4 alone (P < 0.05), but the performance of the combined scores was statistically similar to that of LS alone (P > 0.05).LS showed significantly better performance than FIB-4 in assessing the risk of HCC development, and the combined use of LS and FIB-4 did not provide additional benefit compared with the use of LS alone. Hence, LS assessed using TE might be helpful for optimizing HCC surveillance strategies.

TargetSpy: a supervised machine learning approach for microRNA target prediction.

PubMed

Sturm, Martin; Hackenberg, Michael; Langenberger, David; Frishman, Dmitrij

2010-05-28

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org.

TargetSpy: a supervised machine learning approach for microRNA target prediction

PubMed Central

2010-01-01

Background Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. Results We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Conclusion Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org. PMID:20509939

Morphological and radiological study of ossified superior transverse scapular ligament as potential risk factor of suprascapular nerve entrapment.

PubMed

Polguj, Michał; Sibiński, Marcin; Grzegorzewski, Andrzej; Waszczykowski, Michał; Majos, Agata; Topol, Mirosław

2014-01-01

The suprascapular notch is covered superiorly by the superior transverse scapular ligament. This region is the most common place of suprascapular nerve entrapment formation. The study was performed on 812 specimens: 86 dry scapulae, 104 formalin-fixed cadaveric shoulders, and 622 computer topography scans of scapulae. In the cases with completely ossified superior transverse scapular ligament, the following measurements were performed: proximal and distal width of the bony bridge, middle transverse and vertical diameter of the suprascapular foramen, and area of the suprascapular foramen. An ossified superior transverse scapular ligament was observed more often in men and in the right scapula. The mean age of the subjects with a completely ossified superior transverse scapular ligament was found to be similar than in those without ossification. The ossified band-shaped type of superior transverse scapular ligament was more common than the fan-shaped type and reduced the space below the ligament to a significantly greater degree. The ossified band-shaped type should be taken into consideration as a potential risk factor in the formation of suprascapular nerve entrapment. It could explain the comparable frequency of neuropathy in various populations throughout the world despite the significant differences between them in occurrence of ossified superior transverse scapular ligament.

"Hot Spots" of Land Atmosphere Coupling

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Dirmeyer, Paul A.; Guo, Zhi-Chang; Bonan, Gordan; Chan, Edmond; Cox, Peter; Gordon, T. C.; Kanae, Shinjiro; Kowalczyk, Eva; Lawrence, David

2004-01-01

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a severe paucity of relevant observational data and by the model-dependence of the various computational estimates. To counter this limitation, a dozen climate modeling groups have recently performed the same highly-controlled numerical experiment as part of a coordinated intercomparison project. This allows, for the first time ever, a superior multi-model approach to the estimation of the regions on the globe where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Such estimation has many potential benefits; it can contribute, for example, to seasonal rainfall prediction efforts.

Enhanced pinning in superconducting thin films with graded pinning landscapes

NASA Astrophysics Data System (ADS)

Motta, M.; Colauto, F.; Ortiz, W. A.; Fritzsche, J.; Cuppens, J.; Gillijns, W.; Moshchalkov, V. V.; Johansen, T. H.; Sanchez, A.; Silhanek, A. V.

2013-05-01

A graded distribution of antidots in superconducting a-Mo79Ge21 thin films has been investigated by magnetization and magneto-optical imaging measurements. The pinning landscape has maximum density at the sample border, decreasing linearly towards the center. Its overall performance is noticeably superior than that for a sample with uniformly distributed antidots: For high temperatures and low fields, the critical current is enhanced, whereas the region of thermomagnetic instabilities in the field-temperature diagram is significantly suppressed. These findings confirm the relevance of graded landscapes on the enhancement of pinning efficiency, as recently predicted by Misko and Nori [Phys. Rev. B 85, 184506 (2012)].

Measurement error in earnings data: Using a mixture model approach to combine survey and register data.

PubMed

Meijer, Erik; Rohwedder, Susann; Wansbeek, Tom

2012-01-01

Survey data on earnings tend to contain measurement error. Administrative data are superior in principle, but they are worthless in case of a mismatch. We develop methods for prediction in mixture factor analysis models that combine both data sources to arrive at a single earnings figure. We apply the methods to a Swedish data set. Our results show that register earnings data perform poorly if there is a (small) probability of a mismatch. Survey earnings data are more reliable, despite their measurement error. Predictors that combine both and take conditional class probabilities into account outperform all other predictors.

Predictive markers in calpastatin for tenderness in commercial pig populations

USDA-ARS?s Scientific Manuscript database

The identification of predictive DNA markers for pork quality would allow U.S. pork producers and breeders to more quickly and efficiently select genetically superior animals for production of consistent, high quality meat. Genome scans have identified QTL for tenderness on pig chromosome 2 which ha...

Aging and the Picture Superiority Effect in Recall.

ERIC Educational Resources Information Center

Winograd, Eugene; And Others

1982-01-01

Compared verbal and visual encoding using the picture superiority effect. One experiment found an interaction between age and type of material. In other experiments, the picture superiority effect was found in both age groups with no interaction. Performing a semantic-orienting task had no effect on recall. (Author/RC)

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.

Verification of predicted specimen-specific natural and implanted patellofemoral kinematics during simulated deep knee bend.

PubMed

Baldwin, Mark A; Clary, Chadd; Maletsky, Lorin P; Rullkoetter, Paul J

2009-10-16

Verified computational models represent an efficient method for studying the relationship between articular geometry, soft-tissue constraint, and patellofemoral (PF) mechanics. The current study was performed to evaluate an explicit finite element (FE) modeling approach for predicting PF kinematics in the natural and implanted knee. Experimental three-dimensional kinematic data were collected on four healthy cadaver specimens in their natural state and after total knee replacement in the Kansas knee simulator during a simulated deep knee bend activity. Specimen-specific FE models were created from medical images and CAD implant geometry, and included soft-tissue structures representing medial-lateral PF ligaments and the quadriceps tendon. Measured quadriceps loads and prescribed tibiofemoral kinematics were used to predict dynamic kinematics of an isolated PF joint between 10 degrees and 110 degrees femoral flexion. Model sensitivity analyses were performed to determine the effect of rigid or deformable patellar representations and perturbed PF ligament mechanical properties (pre-tension and stiffness) on model predictions and computational efficiency. Predicted PF kinematics from the deformable analyses showed average root mean square (RMS) differences for the natural and implanted states of less than 3.1 degrees and 1.7 mm for all rotations and translations. Kinematic predictions with rigid bodies increased average RMS values slightly to 3.7 degrees and 1.9 mm with a five-fold decrease in computational time. Two-fold increases and decreases in PF ligament initial strain and linear stiffness were found to most adversely affect kinematic predictions for flexion, internal-external tilt and inferior-superior translation in both natural and implanted states. The verified models could be used to further investigate the effects of component alignment or soft-tissue variability on natural and implant PF mechanics.

Can multi-subpopulation reference sets improve the genomic predictive ability for pigs?

PubMed

Fangmann, A; Bergfelder-Drüing, S; Tholen, E; Simianer, H; Erbe, M

2015-12-01

In most countries and for most livestock species, genomic evaluations are obtained from within-breed analyses. To achieve reliable breeding values, however, a sufficient reference sample size is essential. To increase this size, the use of multibreed reference populations for small populations is considered a suitable option in other species. Over decades, the separate breeding work of different pig breeding organizations in Germany has led to stratified subpopulations in the breed German Large White. Due to this fact and the limited number of Large White animals available in each organization, there was a pressing need for ascertaining if multi-subpopulation genomic prediction is superior compared with within-subpopulation prediction in pigs. Direct genomic breeding values were estimated with genomic BLUP for the trait "number of piglets born alive" using genotype data (Illumina Porcine 60K SNP BeadChip) from 2,053 German Large White animals from five different commercial pig breeding companies. To assess the prediction accuracy of within- and multi-subpopulation reference sets, a random 5-fold cross-validation with 20 replications was performed. The five subpopulations considered were only slightly differentiated from each other. However, the prediction accuracy of the multi-subpopulations approach was not better than that of the within-subpopulation evaluation, for which the predictive ability was already high. Reference sets composed of closely related multi-subpopulation sets performed better than sets of distantly related subpopulations but not better than the within-subpopulation approach. Despite the low differentiation of the five subpopulations, the genetic connectedness between these different subpopulations seems to be too small to improve the prediction accuracy by applying multi-subpopulation reference sets. Consequently, resources should be used for enlarging the reference population within subpopulation, for example, by adding genotyped females.

Comparison of the predictive validity of diagnosis-based risk adjusters for clinical outcomes.

PubMed

Petersen, Laura A; Pietz, Kenneth; Woodard, LeChauncy D; Byrne, Margaret

2005-01-01

Many possible methods of risk adjustment exist, but there is a dearth of comparative data on their performance. We compared the predictive validity of 2 widely used methods (Diagnostic Cost Groups [DCGs] and Adjusted Clinical Groups [ACGs]) for 2 clinical outcomes using a large national sample of patients. We studied all patients who used Veterans Health Administration (VA) medical services in fiscal year (FY) 2001 (n = 3,069,168) and assigned both a DCG and an ACG to each. We used logistic regression analyses to compare predictive ability for death or long-term care (LTC) hospitalization for age/gender models, DCG models, and ACG models. We also assessed the effect of adding age to the DCG and ACG models. Patients in the highest DCG categories, indicating higher severity of illness, were more likely to die or to require LTC hospitalization. Surprisingly, the age/gender model predicted death slightly more accurately than the ACG model (c-statistic of 0.710 versus 0.700, respectively). The addition of age to the ACG model improved the c-statistic to 0.768. The highest c-statistic for prediction of death was obtained with a DCG/age model (0.830). The lowest c-statistics were obtained for age/gender models for LTC hospitalization (c-statistic 0.593). The c-statistic for use of ACGs to predict LTC hospitalization was 0.783, and improved to 0.792 with the addition of age. The c-statistics for use of DCGs and DCG/age to predict LTC hospitalization were 0.885 and 0.890, respectively, indicating the best prediction. We found that risk adjusters based upon diagnoses predicted an increased likelihood of death or LTC hospitalization, exhibiting good predictive validity. In this comparative analysis using VA data, DCG models were generally superior to ACG models in predicting clinical outcomes, although ACG model performance was enhanced by the addition of age.

Abdominal Circumference Versus Body Mass Index as Predictors of Lower Extremity Overuse Injury Risk.

PubMed

Nye, Nathaniel S; Kafer, Drew S; Olsen, Cara; Carnahan, David H; Crawford, Paul F

2018-02-01

Abdominal circumference (AC) is superior to body mass index (BMI) as a measure of risk for various health outcomes. Our objective was to compare AC and BMI as predictors of lower extremity overuse injury (LEOI) risk. Retrospective review of electronic medical records of 79,868 US Air Force personnel over a 7-year period (2005-2011) for incidence of new LEOI. Subjects were stratified by BMI and AC. Injury risk for BMI/AC subgroups was calculated using Kaplan-Meier curves and Cox proportional-hazards regression. Receiver operating characteristic curves with area under the curve were used to compare each model's predictive value. Cox proportional-hazards regression showed significant risk association between elevated BMI, AC, and all injury types, with hazard ratios ranging 1.230-3.415 for obese versus normal BMI and 1.665-3.893 for high-risk versus low-risk AC (P < .05 for all measures). Receiver operating characteristic curves with area under the curve showed equivalent performance between BMI and AC for predicting all injury types. However, the combined model (AC and BMI) showed improved predictive ability over either model alone for joint injury, overall LEOI, and most strongly for osteoarthritis. Although AC and BMI alone performed similarly well, a combined approach using BMI and AC together improved risk estimation for LEOI.

Functional connections between activated and deactivated brain regions mediate emotional interference during externally directed cognition.

PubMed

Di Plinio, Simone; Ferri, Francesca; Marzetti, Laura; Romani, Gian Luca; Northoff, Georg; Pizzella, Vittorio

2018-04-24

Recent evidence shows that task-deactivations are functionally relevant for cognitive performance. Indeed, higher cognitive engagement has been associated with higher suppression of activity in task-deactivated brain regions - usually ascribed to the Default Mode Network (DMN). Moreover, a negative correlation between these regions and areas actively engaged by the task is associated with better performance. DMN regions show positive modulation during autobiographical, social, and emotional tasks. However, it is not clear how processing of emotional stimuli affects the interplay between the DMN and executive brain regions. We studied this interplay in an fMRI experiment using emotional negative stimuli as distractors. Activity modulations induced by the emotional interference of negative stimuli were found in frontal, parietal, and visual areas, and were associated with modulations of functional connectivity between these task-activated areas and DMN regions. A worse performance was predicted both by lower activity in the superior parietal cortex and higher connectivity between visual areas and frontal DMN regions. Connectivity between right inferior frontal gyrus and several DMN regions in the left hemisphere was related to the behavioral performance. This relation was weaker in the negative than in the neutral condition, likely suggesting less functional inhibitions of DMN regions during emotional processing. These results show that both executive and DMN regions are crucial for the emotional interference process and suggest that DMN connections are related to the interplay between externally-directed and internally-focused processes. Among DMN regions, superior frontal gyrus may be a key node in regulating the interference triggered by emotional stimuli. © 2018 Wiley Periodicals, Inc.

The effects of training volume and competition on the salivary cortisol concentrations of Olympic weightlifters.

PubMed

Crewther, Blair T; Heke, Taati; Keogh, Justin W L

2011-01-01

This study examined the effects of training volume and competition on the salivary cortisol (Sal-C) concentrations of Olympic weightlifters. Male (n = 5) and female (n = 4) Olympic weightlifters provided saliva samples across a 5-week experimental = period. The first aim was to assess the weekly effects of high (≥ 200 sets) and low (≤ 100 sets) training volume on Sal-C. The second aim was to compare Sal-C concentrations and 1 repetition maximum (1RM) performance during 2 simulated and 2 actual competitions. Performance was assessed using the snatch, clean and jerk, and the Olympic total lift. Data from each competition setting were pooled before analysis. There were no significant weekly changes in Sal-C levels (p > 0.05). The actual competitions produced higher (128-130%) Sal-C concentrations (p < 0.001) and superior 1RM lifts (1.9-2.6%) for the clean and jerk, and the Olympic total, than the simulated competitions (p < 0.05). Individual Sal-C concentrations before the simulated competitions were positively correlated to all of the 1RM lifts (r = 0.48-0.49, p < 0.05). In conclusion, actual competitions produced greater Sal-C responses than simulated competitions, and this appeared to benefit the 1RM performance of Olympic weightlifters. Individuals with higher Sal-C concentrations also tended to exhibit superior 1RM lifts during the simulated competitions. Given these findings, greater emphasis should be placed upon the monitoring of C to establish normative values, training standards and to assist with performance prediction.

Taboo: Working memory and mental control in an interactive task

PubMed Central

Hansen, Whitney A.; Goldinger, Stephen D.

2014-01-01

Individual differences in working memory (WM) predict principled variation in tasks of reasoning, response time, memory, and other abilities. Theoretically, a central function of WM is keeping task-relevant information easily accessible while suppressing irrelevant information. The present experiment was a novel study of mental control, using performance in the game Taboo as a measure. We tested effects of WM capacity on several indices, including perseveration errors (repeating previous guesses or clues) and taboo errors (saying at least part of a taboo or target word). By most measures, high-span participants were superior to low-span participants: High-spans were better at guessing answers, better at encouraging correct guesses from teammates, and less likely to either repeat themselves or produce taboo clues. Differences in taboo errors occurred only in an easy control condition. The results suggest that WM capacity predicts behavior in tasks requiring mental control, extending this finding to an interactive group setting. PMID:19827699

Forecasting daily lake levels using artificial intelligence approaches

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher

2012-04-01

Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.

Artificial Intelligence Procedures for Tree Taper Estimation within a Complex Vegetation Mosaic in Brazil

PubMed Central

Nunes, Matheus Henrique

2016-01-01

Tree stem form in native tropical forests is very irregular, posing a challenge to establishing taper equations that can accurately predict the diameter at any height along the stem and subsequently merchantable volume. Artificial intelligence approaches can be useful techniques in minimizing estimation errors within complex variations of vegetation. We evaluated the performance of Random Forest® regression tree and Artificial Neural Network procedures in modelling stem taper. Diameters and volume outside bark were compared to a traditional taper-based equation across a tropical Brazilian savanna, a seasonal semi-deciduous forest and a rainforest. Neural network models were found to be more accurate than the traditional taper equation. Random forest showed trends in the residuals from the diameter prediction and provided the least precise and accurate estimations for all forest types. This study provides insights into the superiority of a neural network, which provided advantages regarding the handling of local effects. PMID:27187074

Dual-catalyst system to broaden the window of operability in the reduction of NO/sub chi/ with ammonia

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

Medros, F.G.; Eldridge, J.W.; Kittrell, J.R.

1989-08-01

The objective of the research discussed in this paper was to determine if a dual-catalyst system for NO reduction with NH/sub 3/ can achieve a given percent NO reduction over a wider range of temperatures and space velocities than either catalyst used alone in the same total reactor volume. Hydrogen mordenite (20/32 mesh) and copper-ion-exchanged hydrogen mordenite (2.2% Cu) were used in series at temperatures from 200 to 600 {sup 0}C and space velocities from 1000000 to 450000 h/sup -1/ (STP). The superiority of the dual-catalyst system was demonstrated experimentally, and a model was developed which predicted its performance verymore » well from data on the individual catalysts. A technique was then developed for predicting quantitatively the dual-catalyst enhancement of the space velocity versus temperature window for achieving a given percent NO conversion.« less

Artificial Intelligence Procedures for Tree Taper Estimation within a Complex Vegetation Mosaic in Brazil.

PubMed

Nunes, Matheus Henrique; Görgens, Eric Bastos

2016-01-01

Tree stem form in native tropical forests is very irregular, posing a challenge to establishing taper equations that can accurately predict the diameter at any height along the stem and subsequently merchantable volume. Artificial intelligence approaches can be useful techniques in minimizing estimation errors within complex variations of vegetation. We evaluated the performance of Random Forest® regression tree and Artificial Neural Network procedures in modelling stem taper. Diameters and volume outside bark were compared to a traditional taper-based equation across a tropical Brazilian savanna, a seasonal semi-deciduous forest and a rainforest. Neural network models were found to be more accurate than the traditional taper equation. Random forest showed trends in the residuals from the diameter prediction and provided the least precise and accurate estimations for all forest types. This study provides insights into the superiority of a neural network, which provided advantages regarding the handling of local effects.

Facile synthesis of nanostructured TiNb2O7 anode materials with superior performance for high-rate lithium ion batteries.

PubMed

Lou, Shuaifeng; Ma, Yulin; Cheng, Xinqun; Gao, Jinlong; Gao, Yunzhi; Zuo, Pengjian; Du, Chunyu; Yin, Geping

2015-12-18

One-dimensional nanostructured TiNb2O7 was prepared by a simple solution-based process and subsequent thermal annealing. The obtained anode materials exhibited excellent electrochemical performance with superior reversible capacity, rate capability and cyclic stability.

Development of a MEMS device for acoustic emission testing

NASA Astrophysics Data System (ADS)

Ozevin, Didem; Pessiki, Stephen P.; Jain, Akash; Greve, David W.; Oppenheim, Irving J.

2003-08-01

Acoustic emission testing is an important technology for evaluating structural materials, and especially for detecting damage in structural members. Significant new capabilities may be gained by developing MEMS transducers for acoustic emission testing, including permanent bonding or embedment for superior coupling, greater density of transducer placement, and a bundle of transducers on each device tuned to different frequencies. Additional advantages include capabilities for maintenance of signal histories and coordination between multiple transducers. We designed a MEMS device for acoustic emission testing that features two different mechanical types, a hexagonal plate design and a spring-mass design, with multiple detectors of each type at ten different frequencies in the range of 100 kHz to 1 MHz. The devices were fabricated in the multi-user polysilicon surface micromachining (MUMPs) process and we have conducted electrical characterization experiments and initial experiments on acoustic emission detection. We first report on C(V) measurements and perform a comparison between predicted (design) and measured response. We next report on admittance measurements conducted at pressures varying from vacuum to atmospheric, identifying the resonant frequencies and again providing a comparison with predicted performance. We then describe initial calibration experiments that compare the performance of the detectors to other acoustic emission transducers, and we discuss the overall performance of the device as a sensor suite, as contrasted to the single-channel performance of most commercial transducers.

Flight motor set 360L008 (STS-32R). Volume 1: System overview

NASA Technical Reports Server (NTRS)

Garecht, D. M.

1990-01-01

Flight motor set 360L008 was launched as part of NASA space shuttle mission STS-32R. As with all previous redesigned solid rocket motor launches, overall motor performance was excellent. All ballistic contract end item specification parameters were verified with the exception of ignition interval and rise rates, which could not be verified due to elimination of developmental flight instrumentation. But the available low sample rate data showed nominal propulsion performance. All ballistic and mass property parameters closely matched the predicted values and were well within the required contract end item specification levels that could be assessed. All field joint heaters and igniter joint heaters performed without anomalies. Redesigned field joint heaters and the redesigned left-hand igniter heater were used on this flight. The changes to the heaters were primarily to improve durability and reducing handling damage. Evaluation of the ground environment instrumentation measurements again verified thermal mode analysis data and showed agreement with predicted environmental effects. No launch commit criteria violation occurred. Postflight inspection again verified superior performance of the insulation, phenolics, metal parts, and seals. Postflight evaluation indicated both nozzles performed as expected during flight. All combustion gas was contained by insulation in the field and case-to-nozzle joints. Recommendations were made concerning improved thermal modeling and measurements. The rationale for these recommendations and complete result details are presented.

Learning predictive models that use pattern discovery--a bootstrap evaluative approach applied in organ functioning sequences.

PubMed

Toma, Tudor; Bosman, Robert-Jan; Siebes, Arno; Peek, Niels; Abu-Hanna, Ameen

2010-08-01

An important problem in the Intensive Care is how to predict on a given day of stay the eventual hospital mortality for a specific patient. A recent approach to solve this problem suggested the use of frequent temporal sequences (FTSs) as predictors. Methods following this approach were evaluated in the past by inducing a model from a training set and validating the prognostic performance on an independent test set. Although this evaluative approach addresses the validity of the specific models induced in an experiment, it falls short of evaluating the inductive method itself. To achieve this, one must account for the inherent sources of variation in the experimental design. The main aim of this work is to demonstrate a procedure based on bootstrapping, specifically the .632 bootstrap procedure, for evaluating inductive methods that discover patterns, such as FTSs. A second aim is to apply this approach to find out whether a recently suggested inductive method that discovers FTSs of organ functioning status is superior over a traditional method that does not use temporal sequences when compared on each successive day of stay at the Intensive Care Unit. The use of bootstrapping with logistic regression using pre-specified covariates is known in the statistical literature. Using inductive methods of prognostic models based on temporal sequence discovery within the bootstrap procedure is however novel at least in predictive models in the Intensive Care. Our results of applying the bootstrap-based evaluative procedure demonstrate the superiority of the FTS-based inductive method over the traditional method in terms of discrimination as well as accuracy. In addition we illustrate the insights gained by the analyst into the discovered FTSs from the bootstrap samples. Copyright 2010 Elsevier Inc. All rights reserved.

Multi-modal information processing for visual workload relief

NASA Technical Reports Server (NTRS)

Burke, M. W.; Gilson, R. D.; Jagacinski, R. J.

1980-01-01

The simultaneous performance of two single-dimensional compensatory tracking tasks, one with the left hand and one with the right hand, is discussed. The tracking performed with the left hand was considered the primary task and was performed with a visual display or a quickened kinesthetic-tactual (KT) display. The right-handed tracking was considered the secondary task and was carried out only with a visual display. Although the two primary task displays had afforded equivalent performance in a critical tracking task performed alone, in the dual-task situation the quickened KT primary display resulted in superior secondary visual task performance. Comparisons of various combinations of primary and secondary visual displays in integrated or separated formats indicate that the superiority of the quickened KT display is not simply due to the elimination of visual scanning. Additional testing indicated that quickening per se also is not the immediate cause of the observed KT superiority.

SU-F-J-218: Predicting Radiation-Induced Xerostomia by Dosimetrically Accounting for Daily Setup Uncertainty During Head and Neck IMRT

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

Park, S; Quon, H; McNutt, T

2016-06-15

Purpose: To determine if the accumulated parotid dosimetry using planning CT to daily CBCT deformation and dose re-calculation can predict for radiation-induced xerostomia. Methods: To track and dosimetrically account for the effects of anatomical changes on the parotid glands, we propagated physicians’ contours from planning CT to daily CBCT using a deformable registration with iterative CBCT intensity correction. A surface mesh for each OAR was created with the deformation applied to the mesh to obtain the deformed parotid volumes. Daily dose was computed on the deformed CT and accumulated to the last fraction. For both the accumulated and the plannedmore » parotid dosimetry, we tested the prediction power of different dosimetric parameters including D90, D50, D10, mean, standard deviation, min/max dose to the combined parotids and patient age to severe xerostomia (NCI-CTCAE grade≥2 at 6 mo follow-up). We also tested the dosimetry to parotid sub-volumes. Three classification algorithms, random tree, support vector machine, and logistic regression were tested to predict severe xerostomia using a leave-one-out validation approach. Results: We tested our prediction model on 35 HN IMRT cases. Parameters from the accumulated dosimetry model demonstrated an 89% accuracy for predicting severe xerostomia. Compared to the planning dosimetry, the accumulated dose consistently demonstrated higher prediction power with all three classification algorithms, including 11%, 5% and 30% higher accuracy, sensitivity and specificity, respectively. Geometric division of the combined parotid glands into superior-inferior regions demonstrated ∼5% increased accuracy than the whole volume. The most influential ranked features include age, mean accumulated dose of the submandibular glands and the accumulated D90 of the superior parotid glands. Conclusion: We demonstrated that the accumulated parotid dosimetry using CT-CBCT registration and dose re-calculation more accurately predicts for severe xerostomia and that the superior portion of the parotid glands may be particularly important in predicting for severe xerostomia. This work was supported in part by NIH/NCI under grant R42CA137886 and in part by Toshiba big data research project funds.« less

Anthropogenic climate change has altered primary productivity in Lake Superior

PubMed Central

O'Beirne, M. D.; Werne, J. P.; Hecky, R. E.; Johnson, T. C.; Katsev, S.; Reavie, E. D.

2017-01-01

Anthropogenic climate change has the potential to alter many facets of Earth's freshwater resources, especially lacustrine ecosystems. The effects of anthropogenic changes in Lake Superior, which is Earth's largest freshwater lake by area, are not well documented (spatially or temporally) and predicted future states in response to climate change vary. Here we show that Lake Superior experienced a slow, steady increase in production throughout the Holocene using (paleo)productivity proxies in lacustrine sediments to reconstruct past changes in primary production. Furthermore, data from the last century indicate a rapid increase in primary production, which we attribute to increasing surface water temperatures and longer seasonal stratification related to longer ice-free periods in Lake Superior due to anthropogenic climate warming. These observations demonstrate that anthropogenic effects have become a prominent influence on one of Earth's largest, most pristine lacustrine ecosystems. PMID:28598413

Anthropogenic climate change has altered primary productivity in Lake Superior.

PubMed

O'Beirne, M D; Werne, J P; Hecky, R E; Johnson, T C; Katsev, S; Reavie, E D

2017-06-09

Anthropogenic climate change has the potential to alter many facets of Earth's freshwater resources, especially lacustrine ecosystems. The effects of anthropogenic changes in Lake Superior, which is Earth's largest freshwater lake by area, are not well documented (spatially or temporally) and predicted future states in response to climate change vary. Here we show that Lake Superior experienced a slow, steady increase in production throughout the Holocene using (paleo)productivity proxies in lacustrine sediments to reconstruct past changes in primary production. Furthermore, data from the last century indicate a rapid increase in primary production, which we attribute to increasing surface water temperatures and longer seasonal stratification related to longer ice-free periods in Lake Superior due to anthropogenic climate warming. These observations demonstrate that anthropogenic effects have become a prominent influence on one of Earth's largest, most pristine lacustrine ecosystems.

Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning.

PubMed

Nielsen, Anne; Hansen, Mikkel Bo; Tietze, Anna; Mouridsen, Kim

2018-06-01

Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume. Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNN deep ) to predict final imaging outcome. A total of 222 patients were included, of which 187 were treated with rtPA (recombinant tissue-type plasminogen activator). The performance of CNN deep was compared with a shallow CNN based on the perfusion-weighted imaging biomarker Tmax (CNN Tmax ), a shallow CNN based on a combination of 9 different biomarkers (CNN shallow ), a generalized linear model, and thresholding of the diffusion-weighted imaging biomarker apparent diffusion coefficient (ADC) at 600×10 -6 mm 2 /s (ADC thres ). To assess whether CNN deep is capable of differentiating outcomes of ±intravenous rtPA, patients not receiving intravenous rtPA were included to train CNN deep, -rtpa to access a treatment effect. The networks' performances were evaluated using visual inspection, area under the receiver operating characteristic curve (AUC), and contrast. CNN deep yields significantly better performance in predicting final outcome (AUC=0.88±0.12) than generalized linear model (AUC=0.78±0.12; P =0.005), CNN Tmax (AUC=0.72±0.14; P <0.003), and ADC thres (AUC=0.66±0.13; P <0.0001) and a substantially better performance than CNN shallow (AUC=0.85±0.11; P =0.063). Measured by contrast, CNN deep improves the predictions significantly, showing superiority to all other methods ( P ≤0.003). CNN deep also seems to be able to differentiate outcomes based on treatment strategy with the volume of final infarct being significantly different ( P =0.048). The considerable prediction improvement accuracy over current state of the art increases the potential for automated decision support in providing recommendations for personalized treatment plans. © 2018 American Heart Association, Inc.

SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

PubMed

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-05-01

Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

PubMed Central

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-01-01

Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods. PMID:18452616

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.

PubMed

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C M

2016-03-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The "competition" (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest--ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. Copyright © 2015 Elsevier Ltd. All rights reserved.

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning

PubMed Central

Deng, Zhizhou; Chandrasekaran, Bharath; Wang, Suiping; Wong, Patrick C.M.

2016-01-01

A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The “competition” (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest – ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success. PMID:26866283

The Use of Magnetic Resonance Imaging in Planning a Pedicled Perforator Flap for Pressure Sores in the Gluteal Region.

PubMed

Park, Sun-June; Lee, Kyeong-Tae; Jeon, Byung-Joon; Woo, Kyong-Je

2018-04-01

Pedicled perforator flaps (PPFs) have been widely used to treat pressure sores in the gluteal region. Selection of a reliable perforator is crucial for successful surgical treatment of pressure sores using PPFs. In this study, we evaluate the role of magnetic resonance imaging (MRI) in planning PPF reconstruction of pressure sores in the gluteal region. A retrospective chart review was performed in patients who had undergone these PPF reconstructions and who had received preoperative MRI. Preoperatively, the extent of infection and necrotic tissue was evaluated using MRI, and a reliable perforator was identified, considering the perforator location in relation to the defect, perforator size, and perforator courses. Intraoperatively, the targeted perforator was marked on the skin at the locations measured on the MRI images, and the marked location was confirmed using intraoperative handheld Doppler. Superior gluteal artery, inferior gluteal artery, or parasacral perforators were used for the PPFs. Surgical outcomes were evaluated. A total of 12 PPFs were performed in 12 patients. Superior gluteal artery perforator flaps were performed in 7 patients, inferior gluteal artery perforator flaps were performed in 3 patients, and parasacral perforator flaps were performed in 2 patients. We could identify a reliable perforator on MRI, and it was found at the predicted locations in all cases. There was only one case of partial flap necrosis. There was no recurrence of the pressure sores during the mean follow-up period of 6.7 months (range = 3-15 months). In selected patients with gluteal pressure sores, MRI is a suitable means for not only providing information about disease extent and comorbidities but also for evaluating perforators for PPF reconstructions.

Magnetic Resonance Imaging Measures of Brain Structure to Predict Antidepressant Treatment Outcome in Major Depressive Disorder.

PubMed

Korgaonkar, Mayuresh S; Rekshan, William; Gordon, Evian; Rush, A John; Williams, Leanne M; Blasey, Christine; Grieve, Stuart M

2015-01-01

Less than 50% of patients with Major Depressive Disorder (MDD) reach symptomatic remission with their initial antidepressant medication (ADM). There are currently no objective measures with which to reliably predict which individuals will achieve remission to ADMs. 157 participants with MDD from the International Study to Predict Optimized Treatment in Depression (iSPOT-D) underwent baseline MRIs and completed eight weeks of treatment with escitalopram, sertraline or venlafaxine-ER. A score at week 8 of 7 or less on the 17 item Hamilton Rating Scale for Depression defined remission. Receiver Operator Characteristics (ROC) analysis using the first 50% participants was performed to define decision trees of baseline MRI volumetric and connectivity (fractional anisotropy) measures that differentiated non-remitters from remitters with maximal sensitivity and specificity. These decision trees were tested for replication in the remaining participants. Overall, 35% of all participants achieved remission. ROC analyses identified two decision trees that predicted a high probability of non-remission and that were replicated: 1. Left middle frontal volume < 14 · 8 mL & right angular gyrus volume > 6 · 3 mL identified 55% of non-remitters with 85% accuracy; and 2. Fractional anisotropy values in the left cingulum bundle < 0 · 63, right superior fronto-occipital fasciculus < 0 · 54 and right superior longitudinal fasciculus < 0 · 50 identified 15% of the non-remitters with 84% accuracy. All participants who met criteria for both decision trees were correctly identified as non-remitters. Pretreatment MRI measures seem to reliably identify a subset of patients who do not remit with a first step medication that includes one of these commonly used medications. Findings are consistent with a neuroanatomical basis for non-remission in depressed patients. Brain Resource Ltd is the sponsor for the iSPOT-D study (NCT00693849).

Role of Periarticular Liposomal Bupivacaine Infiltration in Patients Undergoing Total Knee Arthroplasty-A Meta-analysis of Comparative Trials.

PubMed

Singh, Preet Mohinder; Borle, Anuradha; Trikha, Anjan; Michos, Lia; Sinha, Ashish; Goudra, Basavana

2017-02-01

Over last 2 years, many trials have evaluated newly approved liposomal bupivacaine for periarticular infiltration in total knee arthroplasty (TKA) with mixed results. Our meta-analysis attempts to consolidate the results and make evidence-based conclusions. Trails comparing periarticular infiltration of liposomal bupivacaine to conventional analgesic regimens for total knee arthroplasty published till June 2016 were searched in medical database. Comparisons were made for length of stay (LOS), postoperative pain scores, range of motion, and opioid consumption. Meta-regression was performed for LOS to evaluate various analgesic control subgroups. Sixteen trials were included in the final analysis. Liposomal bupivacaine group showed a shorter LOS (reported in 13 subgroups) than control group by 0.17 ± 0.04 days (random effects, P < .001, I 2  = 84.66%). Meta-regression for various types of control showed a predictability (R 2 ) of 73%, τ 2  = 0.013 (random effects, P < .001, I 2  = 45.16). Only femoral block subgroup attained statistically significant shorter LOS on splitting the control group. Numeric pain scores were lower for pooled control group and local anesthetic infiltration subgroup in immediate postoperative phase. Second postoperative day analgesia was statistically superior to overall clubbed controls and femoral block subgroup. Superiority and/or inferiority of liposomal bupivacaine could not be proven for opioid consumption and range of motion because of a small pooled sample size. Publication bias is likely for LOS (Egger test, X intercept = 2.42, P < .001). Liposomal bupivacaine infiltration has questionable clinical advantage, as it marginally shortens patient's hospital stay especially in comparison with patients receiving analgesic femoral nerve block. Compared with conventional regimens, it can provide slightly superior yet sustained (till second postoperative day) perioperative analgesia. High heterogeneity suggests need for standardization of infiltration techniques for better predictability of results. Copyright © 2016 Elsevier Inc. All rights reserved.

Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

USGS Publications Warehouse

Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.

2009-01-01

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in multi-model ensembles. The reasons behind these observations may relate to the effects of the weighting schemes, non-stationarity of the climate series and possible cross-correlations between models. Crown Copyright ?? 2008.

Multi-Fuel Rotary Engine for General Aviation Aircraft

NASA Technical Reports Server (NTRS)

Jones, C.; Ellis, D. R.; Meng, P. R.

1983-01-01

Design studies, conducted for NASA, of Advanced Multi-fuel General Aviation and Commuter Aircraft Rotary Stratified Charge Engines are summarized. Conceptual design studies of an advanced engine sized to provide 186/250 shaft KW/HP under cruise conditions at 7620/25,000 m/ft. altitude were performed. Relevant engine development background covering both prior and recent engine test results of the direct injected unthrottled rotary engine technology, including the capability to interchangeably operate on gasoline, diesel fuel, kerosene, or aviation jet fuel, are presented and related to growth predictions. Aircraft studies, using these resultant growth engines, define anticipated system effects of the performance and power density improvements for both single engine and twin engine airplanes. The calculated results indicate superior system performance and 30 to 35% fuel economy improvement for the Rotary-engine airplanes as compared to equivalent airframe concept designs with current baseline engines. The research and technology activities required to attain the projected engine performance levels are also discussed.

Category and design fluency in mild cognitive impairment: Performance, strategy use, and neural correlates.

PubMed

Peter, Jessica; Kaiser, Jannis; Landerer, Verena; Köstering, Lena; Kaller, Christoph P; Heimbach, Bernhard; Hüll, Michael; Bormann, Tobias; Klöppel, Stefan

2016-12-01

The exploration and retrieval of words during category fluency involves different strategies to improve or maintain performance. Deficits in that task, which are common in patients with amnestic mild cognitive impairment (aMCI), mirror either impaired semantic memory or dysfunctional executive control mechanisms. Relating category fluency to tasks that place greater demands on either semantic knowledge or executive functions might help to determine the underlying cognitive process. The aims of this study were to compare performance and strategy use of 20 patients with aMCI to 30 healthy elderly controls (HC) and to identify the dominant component (either executive or semantic) for better task performance in category fluency. Thus, the relationship between category fluency, design fluency and naming was examined. As fluency tasks have been associated with the superior frontal gyrus (SFG), the inferior frontal gyrus (IFG), and the temporal pole, we further explored the relationship between gray matter volume in these areas and both performance and strategy use. Patients with aMCI showed significantly lower performance and significantly less strategy use during fluency tasks compared to HC. However, both groups equally improved their performance when repeatedly confronted with the same task. In aMCI, performance during category fluency was significantly predicted by design fluency performance, while in HC, it was significantly predicted by naming performance. In HC, volume of the SFG significantly predicted both category and design fluency performance, and strategy use during design fluency. In aMCI, the SFG and the IFG predicted performance during both category and design fluency. The IFG significantly predicted strategy use during category fluency in both groups. The reduced category fluency performance in aMCI seems to be primarily due to dysfunctional executive control mechanisms rather than impaired semantic knowledge. This finding is directly relevant to patients in the different stages of Alzheimer's disease as it links the known semantic fluency deficit in this population to executive functions. Although patients with aMCI are impaired in both performance and strategy use compared to HC, they are able to increase performance over time. However, only HC were able to significantly improve the utilization of fluency strategies in both category and design fluency over time. HC seem to rely more heavily on the SFG during fluency tasks, while in patients with aMCI additional frontal brain areas are involved, possibly reflecting compensational processes. Copyright © 2016 Elsevier Ltd. All rights reserved.

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm.

PubMed

Kim, Seong Gon; Harwani, Mrudul; Grama, Ananth; Chaterji, Somali

2016-12-08

We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). We train EP-DNN using p300 binding sites, as enhancers, and TSS and random non-DHS sites, as non-enhancers. We perform same-cell and cross-cell predictions to quantify the validation rate and compare against two state-of-the-art methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, for a given number of enhancer predictions and also scales better for a larger number of enhancer predictions. Moreover, our H1 → IMR90 predictions turn out to be more accurate than IMR90 → IMR90, potentially because H1 exhibits a richer signature set and our EP-DNN model is expressive enough to extract these subtleties. Our work shows how to leverage the full expressivity of deep learning models, using multiple hidden layers, while avoiding overfitting on the training data. We also lay the foundation for exploration of cross-cell enhancer predictions, potentially reducing the need for expensive experimentation.

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm

NASA Astrophysics Data System (ADS)

Kim, Seong Gon; Harwani, Mrudul; Grama, Ananth; Chaterji, Somali

2016-12-01

We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). We train EP-DNN using p300 binding sites, as enhancers, and TSS and random non-DHS sites, as non-enhancers. We perform same-cell and cross-cell predictions to quantify the validation rate and compare against two state-of-the-art methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, for a given number of enhancer predictions and also scales better for a larger number of enhancer predictions. Moreover, our H1 → IMR90 predictions turn out to be more accurate than IMR90 → IMR90, potentially because H1 exhibits a richer signature set and our EP-DNN model is expressive enough to extract these subtleties. Our work shows how to leverage the full expressivity of deep learning models, using multiple hidden layers, while avoiding overfitting on the training data. We also lay the foundation for exploration of cross-cell enhancer predictions, potentially reducing the need for expensive experimentation.

ProTox: a web server for the in silico prediction of rodent oral toxicity.

PubMed

Drwal, Malgorzata N; Banerjee, Priyanka; Dunkel, Mathias; Wettig, Martin R; Preissner, Robert

2014-07-01

Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein-ligand-based pharmacophore models ('toxicophores') for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Audiovisual synchrony enhances BOLD responses in a brain network including multisensory STS while also enhancing target-detection performance for both modalities

PubMed Central

Marchant, Jennifer L; Ruff, Christian C; Driver, Jon

2012-01-01

The brain seeks to combine related inputs from different senses (e.g., hearing and vision), via multisensory integration. Temporal information can indicate whether stimuli in different senses are related or not. A recent human fMRI study (Noesselt et al. [2007]: J Neurosci 27:11431–11441) used auditory and visual trains of beeps and flashes with erratic timing, manipulating whether auditory and visual trains were synchronous or unrelated in temporal pattern. A region of superior temporal sulcus (STS) showed higher BOLD signal for the synchronous condition. But this could not be related to performance, and it remained unclear if the erratic, unpredictable nature of the stimulus trains was important. Here we compared synchronous audiovisual trains to asynchronous trains, while using a behavioral task requiring detection of higher-intensity target events in either modality. We further varied whether the stimulus trains had predictable temporal pattern or not. Synchrony (versus lag) between auditory and visual trains enhanced behavioral sensitivity (d') to intensity targets in either modality, regardless of predictable versus unpredictable patterning. The analogous contrast in fMRI revealed BOLD increases in several brain areas, including the left STS region reported by Noesselt et al. [2007: J Neurosci 27:11431–11441]. The synchrony effect on BOLD here correlated with the subject-by-subject impact on performance. Predictability of temporal pattern did not affect target detection performance or STS activity, but did lead to an interaction with audiovisual synchrony for BOLD in inferior parietal cortex. PMID:21953980

Tacit Knowledge Flows and Institutional Theory: Accelerating Acculturation

DTIC Science & Technology

2010-01-01

sustainable competitive advantage , but different kinds of knowledge affect competitive advantage differently. This applies especially to the...such qualitative fieldwork and informs theory and practice alike. 1. Introduction Knowledge is key to sustainable competitive advantage ...4,8,19]. Knowledge enables effective action; effective action drives superior performance; and superior performance supports competitive advantage

The Role of Perceived In-group Moral Superiority in Reparative Intentions and Approach Motivation.

PubMed

Szabó, Zsolt P; Mészáros, Noémi Z; Csertő, István

2017-01-01

Three studies examined how members of a national group react to in-group wrongdoings. We expected that perceived in-group moral superiority would lead to unwillingness to repair the aggression. We also expected that internal-focused emotions such as group-based guilt and group-based shame would predict specific, misdeed-related reparative intentions but not general approach motivation toward the victim groups. In Study 1, facing the in-group's recent aggression, participants who believed that the Hungarians have been more moral throughout their history than members of other nations, used more exonerating cognitions, experienced less in-group critical emotions and showed less willingness to provide reparations for the members of the victim group. Study 2 and Study 3 confirmed most findings of Study 1. Perceived in-group moral superiority directly or indirectly reduced willingness to provide either general or specific reparations, while internally focused in-group critical emotions predicted specific misdeed-related reparative intentions but not general approach motivation. The role of emotional attachment to the in-group is considered.

The Role of Perceived In-group Moral Superiority in Reparative Intentions and Approach Motivation

PubMed Central

Szabó, Zsolt P.; Mészáros, Noémi Z.; Csertő, István

2017-01-01

Three studies examined how members of a national group react to in-group wrongdoings. We expected that perceived in-group moral superiority would lead to unwillingness to repair the aggression. We also expected that internal-focused emotions such as group-based guilt and group-based shame would predict specific, misdeed-related reparative intentions but not general approach motivation toward the victim groups. In Study 1, facing the in-group’s recent aggression, participants who believed that the Hungarians have been more moral throughout their history than members of other nations, used more exonerating cognitions, experienced less in-group critical emotions and showed less willingness to provide reparations for the members of the victim group. Study 2 and Study 3 confirmed most findings of Study 1. Perceived in-group moral superiority directly or indirectly reduced willingness to provide either general or specific reparations, while internally focused in-group critical emotions predicted specific misdeed-related reparative intentions but not general approach motivation. The role of emotional attachment to the in-group is considered. PMID:28620333

Do Superior or Inferior Interlaminar Approach or Bevel Orientation Predispose to Nonepidural Needle Penetration?

PubMed

Koontz, Nicholas A; Wiggins, Richard H; Stoddard, Gregory J; Shah, Lubdha M

2017-10-01

There is a paucity of evidence-based literature regarding the advantages and disadvantages of the interlaminar approach and needle bevel orientation for performing a lumbar interlaminar epidural steroid injection (ESI). The purpose of this study was to determine if superior versus inferior lamina approach, needle bevel tip orientation, or both may predispose to inadvertent nonepidural penetration during lumbar interlaminar ESI. A prospective study was performed of patients with low back pain with or without radicular pain or neurogenic claudication referred for lumbar interlaminar ESI. Two hundred eleven patients were randomized by interlaminar approach (superior vs inferior) and bevel tip orientation (cranial vs caudal). Lumbar interlaminar ESI was performed by six interventionalists of varying levels of experience using fluoroscopic guidance with curved tip epidural needles, using loss-of-resistance technique and confirmation with contrast opacification. Exact Poisson regression was used to model the study outcome. Two hundred twenty-one lumbar interlaminar ESIs were performed on 211 patients, randomized to a superior (n = 121) or inferior lamina approach (n = 100) and to a cranial (n = 103) or caudal (n = 118) orientation of the bevel tip. Epidural needle placement was confirmed in 96.4% (n = 213) of cases. Nonepidural needle placement was most commonly associated with superior lamina approach and caudal bevel tip orientation, which was marginally significant (adjusted risk ratio, 6.88; 95% CI, 0.93-∞; p = 0.059). Inadvertent nonepidural needle penetration during fluoroscopically guided lumbar interlaminar ESI appears to be affected by approach, with superior lamina approach and caudal bevel tip orientation being the least favorable technique.

Which AIS based scoring system is the best predictor of outcome in orthopaedic blunt trauma patients?

PubMed

Harwood, Paul J; Giannoudis, Peter V; Probst, Christian; Van Griensven, Martijn; Krettek, Christian; Pape, Hans-Christoph

2006-02-01

Abbreviated Injury Scale (AIS)-based systems-the Injury Severity Score (ISS), New Injury Severity Score (NISS), and AISmax-are used to assess trauma patients. The merits of each in predicting outcome are controversial. A large prospective database was used to assess their predictive capacity using receiver operator characteristic curves. In all, 10,062 adult, blunt-trauma patients met the inclusion criteria. All systems were significant outcome predictors for sepsis, multiple organ failure (MOF), length of hospital stay, length of intensive care unit (ICU) admission and mortality (p < 0.0001). NISS was a significantly better predictor than the ISS for mortality (p < 0.0001). NISS was equivalent to the AISmax for mortality prediction and superior in patients with orthopaedic injuries. NISS was significantly better for sepsis, MOF, ICU stay, and total hospital stay (p < 0.0001). NISS is superior or equivalent to the ISS and AISmax for prediction of all investigated outcomes in a population of blunt trauma patients. As NISS is easier to calculate, its use is recommended to stratify patients for clinical and research purposes.

The role of anti-cyclic citrullinated peptide antibodies in predicting rheumatoid arthritis.

PubMed

Rexhepi, Sylejman; Rexhepi, Mjellma; Sahatçiu-Meka, Vjollca; Tafaj, Argjend; Izairi, Remzi; Rexhepi, Blerta

2011-01-01

The study presents the results of predicting role of anti-cyclic citrullinated peptide antibodies in rheumatoid arthritis, compared to rheumatoid factor. 32 patients with rheumatoid arthritis were identified from a retrospective chart review. The results of our study show that presence of the rheumatoid factor has less diagnostic and prognostic significance than the anti-cyclic citrullinated peptide, and suggests its superiority in predicting an erosive disease course.

Support vector machines for prediction and analysis of beta and gamma-turns in proteins.

PubMed

Pham, Tho Hoan; Satou, Kenji; Ho, Tu Bao

2005-04-01

Tight turns have long been recognized as one of the three important features of proteins, together with alpha-helix and beta-sheet. Tight turns play an important role in globular proteins from both the structural and functional points of view. More than 90% tight turns are beta-turns and most of the rest are gamma-turns. Analysis and prediction of beta-turns and gamma-turns is very useful for design of new molecules such as drugs, pesticides, and antigens. In this paper we investigated two aspects of applying support vector machine (SVM), a promising machine learning method for bioinformatics, to prediction and analysis of beta-turns and gamma-turns. First, we developed two SVM-based methods, called BTSVM and GTSVM, which predict beta-turns and gamma-turns in a protein from its sequence. When compared with other methods, BTSVM has a superior performance and GTSVM is competitive. Second, we used SVMs with a linear kernel to estimate the support of amino acids for the formation of beta-turns and gamma-turns depending on their position in a protein. Our analysis results are more comprehensive and easier to use than the previous results in designing turns in proteins.

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

NASA Astrophysics Data System (ADS)

Sahoo, Sasmita; Jha, Madan K.

2013-12-01

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

PubMed Central

Chiu, Herng-Chia; Ho, Te-Wei; Lee, King-Teh; Chen, Hong-Yaw; Ho, Wen-Hsien

2013-01-01

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation. PMID:23737707

Non-animal methods to predict skin sensitization (II): an assessment of defined approaches *.

PubMed

Kleinstreuer, Nicole C; Hoffmann, Sebastian; Alépée, Nathalie; Allen, David; Ashikaga, Takao; Casey, Warren; Clouet, Elodie; Cluzel, Magalie; Desprez, Bertrand; Gellatly, Nichola; Göbel, Carsten; Kern, Petra S; Klaric, Martina; Kühnl, Jochen; Martinozzi-Teissier, Silvia; Mewes, Karsten; Miyazawa, Masaaki; Strickland, Judy; van Vliet, Erwin; Zang, Qingda; Petersohn, Dirk

2018-05-01

Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals. Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients. The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple in vitro and in chemico skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain in silico predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization. Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools. Multiple non-animal testing strategies incorporating in vitro, in chemico, and in silico inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization.

Predicting vapor liquid equilibria using density functional theory: A case study of argon

NASA Astrophysics Data System (ADS)

Goel, Himanshu; Ling, Sanliang; Ellis, Breanna Nicole; Taconi, Anna; Slater, Ben; Rai, Neeraj

2018-06-01

Predicting vapor liquid equilibria (VLE) of molecules governed by weak van der Waals (vdW) interactions using the first principles approach is a significant challenge. Due to the poor scaling of the post Hartree-Fock wave function theory with system size/basis functions, the Kohn-Sham density functional theory (DFT) is preferred for systems with a large number of molecules. However, traditional DFT cannot adequately account for medium to long range correlations which are necessary for modeling vdW interactions. Recent developments in DFT such as dispersion corrected models and nonlocal van der Waals functionals have attempted to address this weakness with a varying degree of success. In this work, we predict the VLE of argon and assess the performance of several density functionals and the second order Møller-Plesset perturbation theory (MP2) by determining critical and structural properties via first principles Monte Carlo simulations. PBE-D3, BLYP-D3, and rVV10 functionals were used to compute vapor liquid coexistence curves, while PBE0-D3, M06-2X-D3, and MP2 were used for computing liquid density at a single state point. The performance of the PBE-D3 functional for VLE is superior to other functionals (BLYP-D3 and rVV10). At T = 85 K and P = 1 bar, MP2 performs well for the density and structural features of the first solvation shell in the liquid phase.

Review article: scoring systems for assessing prognosis in critically ill adult cirrhotics.

PubMed

Cholongitas, E; Senzolo, M; Patch, D; Shaw, S; Hui, C; Burroughs, A K

2006-08-01

Cirrhotic patients admitted to intensive care units (ICU) still have poor outcomes. Some current ICU prognostic models [Acute Physiology and Chronic Health Evaluation (APACHE), Organ System Failure (OSF) and Sequential Organ Failure Assessment (SOFA)] were used to stratify cirrhotics into risk categories, but few cirrhotics were included in the original model development. Liver-specific scores [Child-Turcotte-Pugh (CTP) and model for end-stage liver disease (MELD)] could be useful in this setting. To evaluate whether ICU prognostic models perform better compared with liver-disease specific ones in cirrhotics admitted to ICU. We performed a structured literature review identifying clinical studies focusing on prognosis and risk factors for mortality in adult cirrhotics admitted to ICU. We found 21 studies (five solely dealing with gastrointestinal bleeding) published during the last 20 years (54-420 patients in each). APACHE II and III, SOFA and OSF had better discrimination for correctly predicting death compared with the CTP score. The MELD score was evaluated only in one study and had good predictive accuracy [receiver operator characteristic (ROC) curve: 0.81). Organ dysfunction models (OSF, SOFA) were superior compared with APACHE II and III (ROC curve: range 0.83-0.94 vs. 0.66-0.88 respectively). Cardiovascular, liver and renal system dysfunction were more frequently independently associated with mortality. General-ICU models had better performance in cirrhotic populations compared with CTP score; OSF and SOFA had the best predictive ability. Further prospective and validation studies are needed.

Fusion Imaging: A Novel Staging Modality in Testis Cancer

PubMed Central

Sterbis, Joseph R.; Rice, Kevin R.; Javitt, Marcia C.; Schenkman, Noah S.; Brassell, Stephen A.

2010-01-01

Objective: Computed tomography and chest radiographs provide the standard imaging for staging, treatment, and surveillance of testicular germ cell neoplasms. Positron emission tomography has recently been utilized for staging, but is somewhat limited in its ability to provide anatomic localization. Fusion imaging combines the metabolic information provided by positron emission tomography with the anatomic precision of computed tomography. To the best of our knowledge, this represents the first study of the effectiveness using fusion imaging in evaluation of patients with testis cancer. Methods: A prospective study of 49 patients presenting to Walter Reed Army Medical Center with testicular cancer from 2003 to 2009 was performed. Fusion imaging was compared with conventional imaging, tumor markers, pathologic results, and clinical follow-up. Results: There were 14 true positives, 33 true negatives, 1 false positive, and 1 false negative. Sensitivity, specificity, positive predictive value, and negative predictive value were 93.3, 97.0, 93.3, and 97.0% respectively. In 11 patient scenarios, fusion imaging differed from conventional imaging. Utility was found in superior lesion detection compared to helical computed tomography due to anatomical/functional image co-registration, detection of micrometastasis in lymph nodes (pathologic nodes < 1cm), surveillance for recurrence post-chemotherapy, differentiating fibrosis from active disease in nodes < 2.5cm, and acting as a quality assurance measure to computed tomography alone. Conclusions: In addition to demonstrating a sensitivity and specificity comparable or superior to conventional imaging, fusion imaging shows promise in providing additive data that may assist in clinical decision-making. PMID:21103077

Activity in the superior colliculus reflects dynamic interactions between voluntary and involuntary influences on orienting behaviour.

PubMed

Bell, Andrew H; Munoz, Douglas P

2008-10-01

Performance in a behavioural task can be influenced by both bottom-up and top-down processes such as stimulus modality and prior probability. Here, we exploited differences in behavioural strategy to explore the role of the intermediate and deep layers of the superior colliculus (dSC) in covert orienting. Two monkeys were trained on a predictive cued-saccade task in which the cue predicted the target's upcoming location with 80% validity. When the delay between cue and target onset was 250 ms, both monkeys showed faster responses to the uncued (Invalid) location. This was associated with a reduced target-aligned response in the dSC on Valid trials for both monkeys and is consistent with a bottom-up (i.e. involuntary) bias. When the delay was increased to 650 ms, one monkey continued to show faster responses to the Invalid location whereas the other monkey showed faster responses to the Valid location, consistent with a top-down (i.e. voluntary) bias. This latter behaviour was correlated with an increase in activity in dSC neurons preceding target onset that was absent in the other monkey. Thus, using the information provided by the cue shifted the emphasis towards top-down processing, while ignoring this information allowed bottom-up processing to continue to dominate. Regardless of the selected strategy, however, neurons in the dSC consistently reflected the current bias between the two processes, emphasizing its role in both the bottom-up and top-down control of orienting behaviour.

[Radiotherapy with androgen deprivation in high-risk prostate cancer: what outcomes on a Caribbean population?].

PubMed

Foahom Kamwa, A D; Vian, E; Agoua, G; Sénéchal, C; Bentaleb, Y; Fofana, M; Manip-M'ebobisse, N; Blanchet, P

2012-11-01

To analyze in a Caribbean population at 90% of African descent, the results of radiotherapy with androgen deprivation (AD) in high-risk prostate cancer (PCa). Fifty-nine consecutive patients with a high-risk PCa as defined by the D'AMICO classification and treated by radiotherapy with AD between January 2003 and April 2009 in our center were analyzed. The median dose of radiation and the median duration of AD were 70Gy and 37months respectively. Biochemical recurrence (BF), as primary outcome was defined according to the PHOENIX criteria (nadir PSA+2ng/mL). Multivariate analysis was performed to identify predictive factors of BF. The median follow-up was 47months. Eight (13.6%) patients had BF and four (6.8%) developed metastases. Six (10.2%) died during the follow-up. The 5years acturial biochemical disease-free survival was 79.7%. Multivariate analyses have shown that Gleason sum (GS) superior to 7 (P=0.029), AD duration less than 24months (P=0.004) and the rate of Nadir PSA greater or equal to 0.5ng/mL (P=0.011) were independent predictive factors of BF. This study was the first to our knowledge, to provide that radiotherapy associate with AD for HRPC among Caribbean men is effective as observed in other populations. Patients with GS superior to 7 could be considered for more aggressive treatments in clinical trials. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Fusion imaging: a novel staging modality in testis cancer.

PubMed

Sterbis, Joseph R; Rice, Kevin R; Javitt, Marcia C; Schenkman, Noah S; Brassell, Stephen A

2010-11-05

Computed tomography and chest radiographs provide the standard imaging for staging, treatment, and surveillance of testicular germ cell neoplasms. Positron emission tomography has recently been utilized for staging, but is somewhat limited in its ability to provide anatomic localization. Fusion imaging combines the metabolic information provided by positron emission tomography with the anatomic precision of computed tomography. To the best of our knowledge, this represents the first study of the effectiveness using fusion imaging in evaluation of patients with testis cancer. A prospective study of 49 patients presenting to Walter Reed Army Medical Center with testicular cancer from 2003 to 2009 was performed. Fusion imaging was compared with conventional imaging, tumor markers, pathologic results, and clinical follow-up. There were 14 true positives, 33 true negatives, 1 false positive, and 1 false negative. Sensitivity, specificity, positive predictive value, and negative predictive value were 93.3, 97.0, 93.3, and 97.0% respectively. In 11 patient scenarios, fusion imaging differed from conventional imaging. Utility was found in superior lesion detection compared to helical computed tomography due to anatomical/functional image co-registration, detection of micrometastasis in lymph nodes (pathologic nodes < 1cm), surveillance for recurrence post-chemotherapy, differentiating fibrosis from active disease in nodes < 2.5cm, and acting as a quality assurance measure to computed tomography alone. In addition to demonstrating a sensitivity and specificity comparable or superior to conventional imaging, fusion imaging shows promise in providing additive data that may assist in clinical decision-making.

Underconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism

PubMed Central

Woolley, Daniel G.; Steyaert, Jean; Di Martino, Adriana; Swinnen, Stephan P.; Wenderoth, Nicole

2014-01-01

Neurodevelopmental disconnections have been assumed to cause behavioral alterations in autism spectrum disorders (ASDs). Here, we combined measurements of intrinsic functional connectivity (iFC) from resting-state functional magnetic resonance imaging (fMRI) with task-based fMRI to explore whether altered activity and/or iFC of the right posterior superior temporal sulcus (pSTS) mediates deficits in emotion recognition in ASD. Fifteen adults with ASD and 15 matched-controls underwent resting-state and task-based fMRI, during which participants discriminated emotional states from point light displays (PLDs). Intrinsic FC of the right pSTS was further examined using 584 (278 ASD/306 controls) resting-state data of the Autism Brain Imaging Data Exchange (ABIDE). Participants with ASD were less accurate than controls in recognizing emotional states from PLDs. Analyses revealed pronounced ASD-related reductions both in task-based activity and resting-state iFC of the right pSTS with fronto-parietal areas typically encompassing the action observation network (AON). Notably, pSTS-hypo-activity was related to pSTS-hypo-connectivity, and both measures were predictive of emotion recognition performance with each measure explaining a unique part of the variance. Analyses with the large independent ABIDE dataset replicated reductions in pSTS-iFC to fronto-parietal regions. These findings provide novel evidence that pSTS hypo-activity and hypo-connectivity with the fronto-parietal AON are linked to the social deficits characteristic of ASD. PMID:24078018

Alternative male morphs solve sperm performance/longevity trade-off in opposite directions.

PubMed

Taborsky, Michael; Schütz, Dolores; Goffinet, Olivier; van Doorn, G Sander

2018-05-01

Males pursuing alternative reproductive tactics have been predicted to face a trade-off between maximizing either swimming performance or endurance of their sperm. However, empirical evidence for this trade-off is equivocal, which may be due to simplistic assumptions. In the shell-brooding cichlid fish Lamprologus callipterus , two Mendelian male morphs compete for fertilization by divergent means: Bourgeois nest males ejaculate sperm, on average, about six times farther from the unfertilized ova than do parasitic dwarf males. This asymmetry is opposite to the usual situation, in which bourgeois males typically benefit from superior fertilization opportunities, suggesting that nest males' sperm should persist longer than dwarf male sperm. The assumed trade-off between sperm swimming performance and longevity predicts that, in turn, sperm of dwarf males should outperform that of nest males in swimming efficiency. Measurement of sperm performance and endurance reveals that dwarf male spermatozoa swim straighter initially than those of nest males, but their motility declines earlier and their velocity slows down more abruptly. Nest male sperm survives longer, which relates to a larger sperm head plus midpiece, implying more mitochondria. Thus, the trade-off between sperm performance and endurance is optimized in opposite directions by alternative male morphs. We argue that the relative success of alternative sperm performance strategies can be influenced strongly by environmental factors such as the time window between gamete release and fertilization, and the position of gamete release. This is an important yet little understood aspect of gametic adaptations to sperm competition.

Datamining approaches for modeling tumor control probability.

PubMed

Naqa, Issam El; Deasy, Joseph O; Mu, Yi; Huang, Ellen; Hope, Andrew J; Lindsay, Patricia E; Apte, Aditya; Alaly, James; Bradley, Jeffrey D

2010-11-01

Tumor control probability (TCP) to radiotherapy is determined by complex interactions between tumor biology, tumor microenvironment, radiation dosimetry, and patient-related variables. The complexity of these heterogeneous variable interactions constitutes a challenge for building predictive models for routine clinical practice. We describe a datamining framework that can unravel the higher order relationships among dosimetric dose-volume prognostic variables, interrogate various radiobiological processes, and generalize to unseen data before when applied prospectively. Several datamining approaches are discussed that include dose-volume metrics, equivalent uniform dose, mechanistic Poisson model, and model building methods using statistical regression and machine learning techniques. Institutional datasets of non-small cell lung cancer (NSCLC) patients are used to demonstrate these methods. The performance of the different methods was evaluated using bivariate Spearman rank correlations (rs). Over-fitting was controlled via resampling methods. Using a dataset of 56 patients with primary NCSLC tumors and 23 candidate variables, we estimated GTV volume and V75 to be the best model parameters for predicting TCP using statistical resampling and a logistic model. Using these variables, the support vector machine (SVM) kernel method provided superior performance for TCP prediction with an rs=0.68 on leave-one-out testing compared to logistic regression (rs=0.4), Poisson-based TCP (rs=0.33), and cell kill equivalent uniform dose model (rs=0.17). The prediction of treatment response can be improved by utilizing datamining approaches, which are able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications.

Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling.

PubMed

Nakas, Christos T; Schütz, Narayan; Werners, Marcus; Leichtle, Alexander B

2016-01-01

Electronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the "big data" era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures.

Artificial intelligence in predicting bladder cancer outcome: a comparison of neuro-fuzzy modeling and artificial neural networks.

PubMed

Catto, James W F; Linkens, Derek A; Abbod, Maysam F; Chen, Minyou; Burton, Julian L; Feeley, Kenneth M; Hamdy, Freddie C

2003-09-15

New techniques for the prediction of tumor behavior are needed, because statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. Whereas artificial neural networks (ANN), the best-studied form of AI, have been used successfully, its hidden networks remain an obstacle to its acceptance. Neuro-fuzzy modeling (NFM), another AI method, has a transparent functional layer and is without many of the drawbacks of ANN. We have compared the predictive accuracies of NFM, ANN, and traditional statistical methods, for the behavior of bladder cancer. Experimental molecular biomarkers, including p53 and the mismatch repair proteins, and conventional clinicopathological data were studied in a cohort of 109 patients with bladder cancer. For all three of the methods, models were produced to predict the presence and timing of a tumor relapse. Both methods of AI predicted relapse with an accuracy ranging from 88% to 95%. This was superior to statistical methods (71-77%; P < 0.0006). NFM appeared better than ANN at predicting the timing of relapse (P = 0.073). The use of AI can accurately predict cancer behavior. NFM has a similar or superior predictive accuracy to ANN. However, unlike the impenetrable "black-box" of a neural network, the rules of NFM are transparent, enabling validation from clinical knowledge and the manipulation of input variables to allow exploratory predictions. This technique could be used widely in a variety of areas of medicine.

Comparison of self-report-based and physical performance-based frailty definitions among patients receiving maintenance hemodialysis.

PubMed

Johansen, Kirsten L; Dalrymple, Lorien S; Delgado, Cynthia; Kaysen, George A; Kornak, John; Grimes, Barbara; Chertow, Glenn M

2014-10-01

A well-accepted definition of frailty includes measurements of physical performance, which may limit its clinical utility. In a cross-sectional study, we compared prevalence and patient characteristics based on a frailty definition that uses self-reported function to the classic performance-based definition and developed a modified self-report-based definition. Prevalent adult patients receiving hemodialysis in 14 centers around San Francisco and Atlanta in 2009-2011. Self-report-based frailty definition in which a score lower than 75 on the Physical Function scale of the 36-Item Short Form Health Survey (SF-36) was substituted for gait speed and grip strength in the classic definition; modified self-report definition with optimized Physical Function score cutoff points derived in a development (one-half) cohort and validated in the other half. Performance-based frailty defined as 3 of the following: weight loss, weakness, exhaustion, low physical activity, and slow gait speed. 387 (53%) patients were frail based on self-reported function, of whom 209 (29% of the cohort) met the performance-based definition. Only 23 (3%) met the performance-based definition of frailty only. The self-report definition had 90% sensitivity, 64% specificity, 54% positive predictive value, 93% negative predictive value, and 72.5% overall accuracy. Intracellular water per kilogram of body weight and serum albumin, prealbumin, and creatinine levels were highest among nonfrail individuals, intermediate among those who were frail by self-report, and lowest among those who also were frail by performance. Age, percentage of body fat, and C-reactive protein level followed an opposite pattern. The modified self-report definition had better accuracy (84%; 95% CI, 79%-89%) and superior specificity (88%) and positive predictive value (67%). Our study did not address prediction of outcomes. Patients who meet the self-report-based but not the performance-based definition of frailty may represent an intermediate phenotype. A modified self-report definition can improve the accuracy of a questionnaire-based method of defining frailty. Published by Elsevier Inc.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

The Montreal Cognitive Assessment and the mini-mental state examination as screening instruments for cognitive impairment: item analyses and threshold scores.

PubMed

Damian, Anne M; Jacobson, Sandra A; Hentz, Joseph G; Belden, Christine M; Shill, Holly A; Sabbagh, Marwan N; Caviness, John N; Adler, Charles H

2011-01-01

To perform an item analysis of the Montreal Cognitive Assessment (MoCA) versus the Mini-Mental State Examination (MMSE) in the prediction of cognitive impairment, and to examine the characteristics of different MoCA threshold scores. 135 subjects enrolled in a longitudinal clinicopathologic study were administered the MoCA by a single physician and the MMSE by a trained research assistant. Subjects were classified as cognitively impaired or cognitively normal based on independent neuropsychological testing. 89 subjects were found to be cognitively normal, and 46 cognitively impaired (20 with dementia, 26 with mild cognitive impairment). The MoCA was superior in both sensitivity and specificity to the MMSE, although not all MoCA tasks were of equal predictive value. A MoCA threshold score of 26 had a sensitivity of 98% and a specificity of 52% in this population. In a population with a 20% prevalence of cognitive impairment, a threshold of 24 was optimal (negative predictive value 96%, positive predictive value 47%). This analysis suggests the potential for creating an abbreviated MoCA. For screening in primary care, the MoCA threshold of 26 appears optimal. For testing in a memory disorders clinic, a lower threshold has better predictive value. Copyright © 2011 S. Karger AG, Basel.

Combining disparate data sources for improved poverty prediction and mapping.

PubMed

Pokhriyal, Neeti; Jacques, Damien Christophe

2017-11-14

More than 330 million people are still living in extreme poverty in Africa. Timely, accurate, and spatially fine-grained baseline data are essential to determining policy in favor of reducing poverty. The potential of "Big Data" to estimate socioeconomic factors in Africa has been proven. However, most current studies are limited to using a single data source. We propose a computational framework to accurately predict the Global Multidimensional Poverty Index (MPI) at a finest spatial granularity and coverage of 552 communes in Senegal using environmental data (related to food security, economic activity, and accessibility to facilities) and call data records (capturing individualistic, spatial, and temporal aspects of people). Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated with predictions. We perform model selection using elastic net regularization to prevent overfitting. Our results empirically prove the superior accuracy when using disparate data (Pearson correlation of 0.91). Our approach is used to accurately predict important dimensions of poverty: health, education, and standard of living (Pearson correlation of 0.84-0.86). All predictions are validated using deprivations calculated from census. Our approach can be used to generate poverty maps frequently, and its diagnostic nature is, likely, to assist policy makers in designing better interventions for poverty eradication. Copyright © 2017 the Author(s). Published by PNAS.

Combining disparate data sources for improved poverty prediction and mapping

PubMed Central

2017-01-01

More than 330 million people are still living in extreme poverty in Africa. Timely, accurate, and spatially fine-grained baseline data are essential to determining policy in favor of reducing poverty. The potential of “Big Data” to estimate socioeconomic factors in Africa has been proven. However, most current studies are limited to using a single data source. We propose a computational framework to accurately predict the Global Multidimensional Poverty Index (MPI) at a finest spatial granularity and coverage of 552 communes in Senegal using environmental data (related to food security, economic activity, and accessibility to facilities) and call data records (capturing individualistic, spatial, and temporal aspects of people). Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing uncertainty associated with predictions. We perform model selection using elastic net regularization to prevent overfitting. Our results empirically prove the superior accuracy when using disparate data (Pearson correlation of 0.91). Our approach is used to accurately predict important dimensions of poverty: health, education, and standard of living (Pearson correlation of 0.84–0.86). All predictions are validated using deprivations calculated from census. Our approach can be used to generate poverty maps frequently, and its diagnostic nature is, likely, to assist policy makers in designing better interventions for poverty eradication. PMID:29087949

Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing.

PubMed

Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2017-01-01

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This includes considering the limitations of current operationalisations and recommendations for future research that align outcomes and subsequent work more closely to specific dual-process models.

Real-time forecasting at weekly timescales of the SST and SLA of the Ligurian Sea with a satellite-based ocean forecasting (SOFT) system

NASA Astrophysics Data System (ADS)

ÁLvarez, A.; Orfila, A.; Tintoré, J.

2004-03-01

Satellites are the only systems able to provide continuous information on the spatiotemporal variability of vast areas of the ocean. Relatively long-term time series of satellite data are nowadays available. These spatiotemporal time series of satellite observations can be employed to build empirical models, called satellite-based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. SOFT systems can predict satellite-observed fields at different timescales. The forecast skill of SOFT systems forecasting the sea surface temperature (SST) at monthly timescales has been extensively explored in previous works. In this work we study the performance of two SOFT systems forecasting, respectively, the SST and sea level anomaly (SLA) at weekly timescales, that is, providing forecasts of the weekly averaged SST and SLA fields with 1 week in advance. The SOFT systems were implemented in the Ligurian Sea (Western Mediterranean Sea). Predictions from the SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the SOFT system forecasting the SST field is always superior in terms of predictability to persistence. Minimum prediction errors in the SST are obtained during winter and spring seasons. On the other hand, the biggest differences between the performance of SOFT and persistence models are found during summer and autumn. These changes in the predictability are explained on the basis of the particular variability of the SST field in the Ligurian Sea. Concerning the SLA field, no improvements with respect to persistence have been found for the SOFT system forecasting the SLA field.

Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing

PubMed Central

Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2017-01-01

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This includes considering the limitations of current operationalisations and recommendations for future research that align outcomes and subsequent work more closely to specific dual-process models. PMID:29062288

An injury mortality prediction based on the anatomic injury scale

PubMed Central

Wang, Muding; Wu, Dan; Qiu, Wusi; Wang, Weimi; Zeng, Yunji; Shen, Yi

2017-01-01

Abstract To determine whether the injury mortality prediction (IMP) statistically outperforms the trauma mortality prediction model (TMPM) as a predictor of mortality. The TMPM is currently the best trauma score method, which is based on the anatomic injury. Its ability of mortality prediction is superior to the injury severity score (ISS) and to the new injury severity score (NISS). However, despite its statistical significance, the predictive power of TMPM needs to be further improved. Retrospective cohort study is based on the data of 1,148,359 injured patients in the National Trauma Data Bank hospitalized from 2010 to 2011. Sixty percent of the data was used to derive an empiric measure of severity of different Abbreviated Injury Scale predot codes by taking the weighted average death probabilities of trauma patients. Twenty percent of the data was used to create computing method of the IMP model. The remaining 20% of the data was used to evaluate the statistical performance of IMP and then be compared with the TMPM and the single worst injury by examining area under the receiver operating characteristic curve (ROC), the Hosmer–Lemeshow (HL) statistic, and the Akaike information criterion. IMP exhibits significantly both better discrimination (ROC-IMP, 0.903 [0.899–0.907] and ROC-TMPM, 0.890 [0.886–0.895]) and calibration (HL-IMP, 9.9 [4.4–14.7] and HL-TMPM, 197 [143–248]) compared with TMPM. All models show slight changes after the extension of age, gender, and mechanism of injury, but the extended IMP still dominated TMPM in every performance. The IMP has slight improvement in discrimination and calibration compared with the TMPM and can accurately predict mortality. Therefore, we consider it as a new feasible scoring method in trauma research. PMID:28858124

Comparison of techniques for correction of magnification of pelvic X-rays for hip surgery planning.

PubMed

The, Bertram; Kootstra, Johan W J; Hosman, Anton H; Verdonschot, Nico; Gerritsma, Carina L E; Diercks, Ron L

2007-12-01

The aim of this study was to develop an accurate method for correction of magnification of pelvic x-rays to enhance accuracy of hip surgery planning. All investigated methods aim at estimating the anteroposterior location of the hip joint in supine position to correctly position a reference object for correction of magnification. An existing method-which is currently being used in clinical practice in our clinics-is based on estimating the position of the hip joint by palpation of the greater trochanter. It is only moderately accurate and difficult to execute reliably in clinical practice. To develop a new method, 99 patients who already had a hip implant in situ were included; this enabled determining the true location of the hip joint deducted from the magnification of the prosthesis. Physical examination was used to obtain predictor variables possibly associated with the height of the hip joint. This included a simple dynamic hip joint examination to estimate the position of the center of rotation. Prediction equations were then constructed using regression analysis. The performance of these prediction equations was compared with the performance of the existing protocol. The mean absolute error in predicting the height of the hip joint center using the old method was 20 mm (range -79 mm to +46 mm). This was 11 mm for the new method (-32 mm to +39 mm). The prediction equation is: height (mm) = 34 + 1/2 abdominal circumference (cm). The newly developed prediction equation is a superior method for predicting the height of the hip joint center for correction of magnification of pelvic x-rays. We recommend its implementation in the departments of radiology and orthopedic surgery.

Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine.

PubMed

Lu, Jing; Lu, Dong; Zhang, Xiaochen; Bi, Yi; Cheng, Keguang; Zheng, Mingyue; Luo, Xiaomin

2016-11-01

Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life. In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold. Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed. An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698). Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016 Elsevier B.V. All rights reserved.

Is There a Limit to the Superiority of Individuals with ASD in Visual Search?

ERIC Educational Resources Information Center

Hessels, Roy S.; Hooge, Ignace T. C.; Snijders, Tineke M.; Kemner, Chantal

2014-01-01

Superiority in visual search for individuals diagnosed with autism spectrum disorder (ASD) is a well-reported finding. We administered two visual search tasks to individuals with ASD and matched controls. One showed no difference between the groups, and one did show the expected superior performance for individuals with ASD. These results offer an…

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method

PubMed Central

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu

2015-01-01

The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]′), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal. PMID:26213935

Pseudoaneurysm of the superior lateral genicular artery: case report of a rare complication after total knee arthroplasty.

PubMed

Saini, Pramod; Meena, Sanjay; Malhotra, Rajesh; Gamanagatti, Shivanand; Kumar, Vijay; Jain, Vaibhav

2013-01-01

Pseudoaneurysm of superior lateral genicular artery following total knee arthroplasty is a rare complication and has been reported following lateral release performed for eversion of patella in a knee with tight lateral structures. This report describes a case of pseudo aneurysm of superior lateral geniculate artery that developed after primary Total knee arthroplasty for a stiff knee in a 68 year old patient. Patient presented with pain and rapidly increasing swelling in early post operative period. Diagnosis was made on duplex ultrasound and confirmed by angiography. Angiographic coil embolisation of the pseudoaneurysm was performed. Since no lateral release was performed in this case, the probable mechanism was shear injury to the vessel. Pseudoaneurysm of superior lateral genicular artery can occur in absence of lateral release by shear injury to an atherosclerotic vessel. Angiographic coil embolisation appears to be the best method for treating such post arthroplasty pseudoaneurysm because of less chance of infection, non interference with rehabilitation and diagnosis and treatment during same procedure.

The architecture of dynamic reservoir in the echo state network

NASA Astrophysics Data System (ADS)

Cui, Hongyan; Liu, Xiang; Li, Lixiang

2012-09-01

Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.

HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy.

PubMed

Hu, Huan; Zhang, Li; Ai, Haixin; Zhang, Hui; Fan, Yetian; Zhao, Qi; Liu, Hongsheng

2018-03-27

LncRNA plays an important role in many biological and disease progression by binding to related proteins. However, the experimental methods for studying lncRNA-protein interactions are time-consuming and expensive. Although there are a few models designed to predict the interactions of ncRNA-protein, they all have some common drawbacks that limit their predictive performance. In this study, we present a model called HLPI-Ensemble designed specifically for human lncRNA-protein interactions. HLPI-Ensemble adopts the ensemble strategy based on three mainstream machine learning algorithms of Support Vector Machines (SVM), Random Forests (RF) and Extreme Gradient Boosting (XGB) to generate HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble, respectively. The results of 10-fold cross-validation show that HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble achieved AUCs of 0.95, 0.96 and 0.96, respectively, in the test dataset. Furthermore, we compared the performance of the HLPI-Ensemble models with the previous models through external validation dataset. The results show that the false positives (FPs) of HLPI-Ensemble models are much lower than that of the previous models, and other evaluation indicators of HLPI-Ensemble models are also higher than those of the previous models. It is further showed that HLPI-Ensemble models are superior in predicting human lncRNA-protein interaction compared with previous models. The HLPI-Ensemble is publicly available at: http://ccsipb.lnu.edu.cn/hlpiensemble/ .

Application of a soft computing technique in predicting the percentage of shear force carried by walls in a rectangular channel with non-homogeneous roughness.

PubMed

Khozani, Zohreh Sheikh; Bonakdari, Hossein; Zaji, Amir Hossein

2016-01-01

Two new soft computing models, namely genetic programming (GP) and genetic artificial algorithm (GAA) neural network (a combination of modified genetic algorithm and artificial neural network methods) were developed in order to predict the percentage of shear force in a rectangular channel with non-homogeneous roughness. The ability of these methods to estimate the percentage of shear force was investigated. Moreover, the independent parameters' effectiveness in predicting the percentage of shear force was determined using sensitivity analysis. According to the results, the GP model demonstrated superior performance to the GAA model. A comparison was also made between the GP program determined as the best model and five equations obtained in prior research. The GP model with the lowest error values (root mean square error ((RMSE) of 0.0515) had the best function compared with the other equations presented for rough and smooth channels as well as smooth ducts. The equation proposed for rectangular channels with rough boundaries (RMSE of 0.0642) outperformed the prior equations for smooth boundaries.

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

PubMed Central

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy. PMID:29768463

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

PubMed

Rani R, Hannah Jessie; Victoire T, Aruldoss Albert

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.

Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

PubMed

Zhao, Yang; Zheng, Wei; Zhuo, Daisy Y; Lu, Yuefeng; Ma, Xiwen; Liu, Hengchang; Zeng, Zhen; Laird, Glen

2017-10-11

Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

Network-based H.264/AVC whole frame loss visibility model and frame dropping methods.

PubMed

Chang, Yueh-Lun; Lin, Ting-Lan; Cosman, Pamela C

2012-08-01

We examine the visual effect of whole frame loss by different decoders. Whole frame losses are introduced in H.264/AVC compressed videos which are then decoded by two different decoders with different common concealment effects: frame copy and frame interpolation. The videos are seen by human observers who respond to each glitch they spot. We found that about 39% of whole frame losses of B frames are not observed by any of the subjects, and over 58% of the B frame losses are observed by 20% or fewer of the subjects. Using simple predictive features which can be calculated inside a network node with no access to the original video and no pixel level reconstruction of the frame, we developed models which can predict the visibility of whole B frame losses. The models are then used in a router to predict the visual impact of a frame loss and perform intelligent frame dropping to relieve network congestion. Dropping frames based on their visual scores proves superior to random dropping of B frames.

Portfolio optimization for seed selection in diverse weather scenarios.

PubMed

Marko, Oskar; Brdar, Sanja; Panić, Marko; Šašić, Isidora; Despotović, Danica; Knežević, Milivoje; Crnojević, Vladimir

2017-01-01

The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.

Portfolio optimization for seed selection in diverse weather scenarios

PubMed Central

Brdar, Sanja; Panić, Marko; Šašić, Isidora; Despotović, Danica; Knežević, Milivoje; Crnojević, Vladimir

2017-01-01

The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017. PMID:28863173

Adaptability and stability of soybean genotypes in off-season cultivation.

PubMed

Batista, R O; Hamawaki, R L; Sousa, L B; Nogueira, A P O; Hamawaki, O T

2015-08-14

The oil and protein contents of soybean grains are important quantitative traits for use in breeding. However, few breeding programs perform selection based on these traits in different environments. This study assessed the adaptability and stability of 14 elite early soybean breeding lines in off-season cultivation with respect to yield, and oil and protein contents. A range of statistical methods was applied and these analyses indicated that for off-season cultivation, the lines UFUS 5 and UFUS 10 could be recommended due to their superior performance in grain yield, oil content, and specific adaptability to unfavorable environments along with high stability in these characteristics. Also recommended were UFUS 06, which demonstrated superior performance in all three tested characteristics and showed adaptation to favorable environments, and UFUS 13, which showed high adaptability and stability and a superior performance for protein content.

Artificial neural network modeling of dissolved oxygen in reservoir.

PubMed

Chen, Wei-Bo; Liu, Wen-Cheng

2014-02-01

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

Aero-thermal optimization of film cooling flow parameters on the suction surface of a high pressure turbine blade

NASA Astrophysics Data System (ADS)

El Ayoubi, Carole; Hassan, Ibrahim; Ghaly, Wahid

2012-11-01

This paper aims to optimize film coolant flow parameters on the suction surface of a high-pressure gas turbine blade in order to obtain an optimum compromise between a superior cooling performance and a minimum aerodynamic penalty. An optimization algorithm coupled with three-dimensional Reynolds-averaged Navier Stokes analysis is used to determine the optimum film cooling configuration. The VKI blade with two staggered rows of axially oriented, conically flared, film cooling holes on its suction surface is considered. Two design variables are selected; the coolant to mainstream temperature ratio and total pressure ratio. The optimization objective consists of maximizing the spatially averaged film cooling effectiveness and minimizing the aerodynamic penalty produced by film cooling. The effect of varying the coolant flow parameters on the film cooling effectiveness and the aerodynamic loss is analyzed using an optimization method and three dimensional steady CFD simulations. The optimization process consists of a genetic algorithm and a response surface approximation of the artificial neural network type to provide low-fidelity predictions of the objective function. The CFD simulations are performed using the commercial software CFX. The numerical predictions of the aero-thermal performance is validated against a well-established experimental database.

Interrelationships Between Morphometric, Densitometric, and Mechanical Properties of Teeth in 5-Month-Old Polish Merino Sheep.

PubMed

Tatara, Marcin R; Szabelska, Anna; Krupski, Witold; Tymczyna, Barbara; Łuszczewska-Sierakowska, Iwona; Bieniaś, Jarosław; Ostapiuk, Monika

2018-06-01

Interrelationships between morphological, densitometric, and mechanical properties of deciduous mandibular teeth (incisors, canine, second premolar) were investigated. To perform morphometric, densitometric, and mechanical analyses, teeth were obtained from 5-month-old sheep. Measurements of mean volumetric tooth mineral density and total tooth volume were performed using quantitative computed tomography. Microcomputed tomography was used to measure total enamel volume, volumetric enamel mineral density, total dentin volume, and volumetric dentin mineral density. Maximum elastic strength and ultimate force of teeth were determined using 3-point bending and compression tests. Pearson correlation coefficients were determined between all investigated variables. Mutual dependence was observed between morphological and mechanical properties of the investigated teeth. The highest number of positive correlations of the investigated parameters was stated in first incisor indicating its superior predictive value of tooth quality and masticatory organ function in sheep. Positive correlations of the volumetric dentin mineral density in second premolar with final body weight may indicate predictive value of this parameter in relation with growth rate in sheep. Evaluation of deciduous tooth properties may prove helpful for breeding selection and further reproduction of sheep possessing favorable traits of teeth and better masticatory organ function, leading to improved performance and economic efficiency of the flock.

Recognition Memory and Source Memory in Autism Spectrum Disorder: A Study of the Intention Superiority and Enactment Effects

ERIC Educational Resources Information Center

Grainger, Catherine; Williams, David M.; Lind, Sophie E.

2017-01-01

It is well established that neurotypical individuals generally show better memory for actions they have performed than actions they have observed others perform or merely read about, a so-called "enactment effect." Strikingly, research has also shown that neurotypical individuals demonstrate superior memory for actions they…

Performance Analyses and Improvements for the IEEE 802.15.4 CSMA/CA Scheme with Heterogeneous Buffered Conditions

PubMed Central

Zhu, Jianping; Tao, Zhengsu; Lv, Chunfeng

2012-01-01

Studies of the IEEE 802.15.4 Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme have been received considerable attention recently, with most of these studies focusing on homogeneous or saturated traffic. Two novel transmission schemes—OSTS/BSTS (One Service a Time Scheme/Bulk Service a Time Scheme)—are proposed in this paper to improve the behaviors of time-critical buffered networks with heterogeneous unsaturated traffic. First, we propose a model which contains two modified semi-Markov chains and a macro-Markov chain combined with the theory of M/G/1/K queues to evaluate the characteristics of these two improved CSMA/CA schemes, in which traffic arrivals and accessing packets are bestowed with non-preemptive priority over each other, instead of prioritization. Then, throughput, packet delay and energy consumption of unsaturated, unacknowledged IEEE 802.15.4 beacon-enabled networks are predicted based on the overall point of view which takes the dependent interactions of different types of nodes into account. Moreover, performance comparisons of these two schemes with other non-priority schemes are also proposed. Analysis and simulation results show that delay and fairness of our schemes are superior to those of other schemes, while throughput and energy efficiency are superior to others in more heterogeneous situations. Comprehensive simulations demonstrate that the analysis results of these models match well with the simulation results. PMID:22666076

Performance analyses and improvements for the IEEE 802.15.4 CSMA/CA scheme with heterogeneous buffered conditions.

PubMed

Zhu, Jianping; Tao, Zhengsu; Lv, Chunfeng

2012-01-01

Studies of the IEEE 802.15.4 Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme have been received considerable attention recently, with most of these studies focusing on homogeneous or saturated traffic. Two novel transmission schemes-OSTS/BSTS (One Service a Time Scheme/Bulk Service a Time Scheme)-are proposed in this paper to improve the behaviors of time-critical buffered networks with heterogeneous unsaturated traffic. First, we propose a model which contains two modified semi-Markov chains and a macro-Markov chain combined with the theory of M/G/1/K queues to evaluate the characteristics of these two improved CSMA/CA schemes, in which traffic arrivals and accessing packets are bestowed with non-preemptive priority over each other, instead of prioritization. Then, throughput, packet delay and energy consumption of unsaturated, unacknowledged IEEE 802.15.4 beacon-enabled networks are predicted based on the overall point of view which takes the dependent interactions of different types of nodes into account. Moreover, performance comparisons of these two schemes with other non-priority schemes are also proposed. Analysis and simulation results show that delay and fairness of our schemes are superior to those of other schemes, while throughput and energy efficiency are superior to others in more heterogeneous situations. Comprehensive simulations demonstrate that the analysis results of these models match well with the simulation results.

A digital spatial predictive model of land-use change using economic and environmental inputs and a statistical tree classification approach: Thailand, 1970s--1990s

NASA Astrophysics Data System (ADS)

Felkner, John Sames

The scale and extent of global land use change is massive, and has potentially powerful effects on the global climate and global atmospheric composition (Turner & Meyer, 1994). Because of this tremendous change and impact, there is an urgent need for quantitative, empirical models of land use change, especially predictive models with an ability to capture the trajectories of change (Agarwal, Green, Grove, Evans, & Schweik, 2000; Lambin et al., 1999). For this research, a spatial statistical predictive model of land use change was created and run in two provinces of Thailand. The model utilized an extensive spatial database, and used a classification tree approach for explanatory model creation and future land use (Breiman, Friedman, Olshen, & Stone, 1984). Eight input variables were used, and the trees were run on a dependent variable of land use change measured from 1979 to 1989 using classified satellite imagery. The derived tree models were used to create probability of change surfaces, and these were then used to create predicted land cover maps for 1999. These predicted 1999 maps were compared with actual 1999 landcover derived from 1999 Landsat 7 imagery. The primary research hypothesis was that an explanatory model using both economic and environmental input variables would better predict future land use change than would either a model using only economic variables or a model using only environmental. Thus, the eight input variables included four economic and four environmental variables. The results indicated a very slight superiority of the full models to predict future agricultural change and future deforestation, but a slight superiority of the economic models to predict future built change. However, the margins of superiority were too small to be statistically significant. The resulting tree structures were used, however, to derive a series of principles or "rules" governing land use change in both provinces. The model was able to predict future land use, given a series of assumptions, with 90 percent overall accuracies. The model can be used in other developing or developed country locations for future land use prediction, determination of future threatened areas, or to derive "rules" or principles driving land use change.

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

Agility performance in high-level junior basketball players: the predictive value of anthropometrics and power qualities.

PubMed

Sisic, Nedim; Jelicic, Mario; Pehar, Miran; Spasic, Miodrag; Sekulic, Damir

2016-01-01

In basketball, anthropometric status is an important factor when identifying and selecting talents, while agility is one of the most vital motor performances. The aim of this investigation was to evaluate the influence of anthropometric variables and power capacities on different preplanned agility performances. The participants were 92 high-level, junior-age basketball players (16-17 years of age; 187.6±8.72 cm in body height, 78.40±12.26 kg in body mass), randomly divided into a validation and cross-validation subsample. The predictors set consisted of 16 anthropometric variables, three tests of power-capacities (Sargent-jump, broad-jump and medicine-ball-throw) as predictors. The criteria were three tests of agility: a T-Shape-Test; a Zig-Zag-Test, and a test of running with a 180-degree turn (T180). Forward stepwise multiple regressions were calculated for validation subsamples and then cross-validated. Cross validation included correlations between observed and predicted scores, dependent samples t-test between predicted and observed scores; and Bland Altman graphics. Analysis of the variance identified centres being advanced in most of the anthropometric indices, and medicine-ball-throw (all at P<0.05); with no significant between-position-differences for other studied motor performances. Multiple regression models originally calculated for the validation subsample were then cross-validated, and confirmed for Zig-zag-Test (R of 0.71 and 0.72 for the validation and cross-validation subsample, respectively). Anthropometrics were not strongly related to agility performance, but leg length is found to be negatively associated with performance in basketball-specific agility. Power capacities are confirmed to be an important factor in agility. The results highlighted the importance of sport-specific tests when studying pre-planned agility performance in basketball. The improvement in power capacities will probably result in an improvement in agility in basketball athletes, while anthropometric indices should be used in order to identify those athletes who can achieve superior agility performance.

Association of pituitary adenylate cyclase-activating polypeptide with cognitive decline in mild cognitive impairment due to Alzheimer disease.

PubMed

Han, Pengcheng; Caselli, Richard J; Baxter, Leslie; Serrano, Geidy; Yin, Junxiang; Beach, Thomas G; Reiman, Eric M; Shi, Jiong

2015-03-01

There is a deficit of pituitary adenylate cyclase-activating polypeptide (PACAP) in patients with neuropathologically confirmed Alzheimer dementia. However, whether this deficit is associated with the earlier stages of Alzheimer disease (AD) is unknown. This study was conducted to clarify the association between PACAP biomarkers and preclinical, mild cognitive impairment (MCI), and dementia stages of AD in postmortem brain tissue. To examine PACAP and PACAP receptor levels in postmortem brain tissues and cerebrospinal fluid from cognitively and neuropathologically normal control individuals, patients with MCI due to AD (MCI-AD), and individuals with AD; analyze the relationship between PACAP, cognitive, and pathologic features; and propose a model to assess these relationships. We measured PACAP and its receptor (PAC1) levels using enzyme-linked immunoassay. A total of 35 cases were included. All the brain tissue and cerebrospinal fluid samples were selected from Banner Sun Health Research Institute Brain and Body Donation Program. All cognitive test results were in record with the Arizona Alzheimer's Consortium. A comparison of PACAP and PAC1 levels among the healthy controls, MCI-AD, and AD dementia groups, as well as a systematic correlation analysis between PACAP level, cognitive performance, and pathologic severity. The PACAP levels in cerebrospinal fluid, the superior frontal gyrus, and the middle temporal gyrus were inversely related to dementia severity. The PACAP levels in cerebrospinal fluid correlated with the Mattis Dementia Rating Scale score (Pearson r = 0.50; P = .03) and inversely correlated with total amyloid plaques (Pearson r = -0.48; P < .01) and tangles (Pearson r = -0.55; P = .01) in the brain. The PACAP in the superior frontal gyrus and middle temporal gyrus correlated with the Stroop Color-Word Interference Test (Pearson r = 0.58; P < .01) and the Auditory Verbal Learning Test-Total Learning (Pearson r = 0.33; P = .02), respectively. The PACAP in the primary visual cortex did not correlate with the Judgment of Line orientation test (P = .14). Furthermore, the PAC1 level in the superior frontal gyrus showed an upregulation in MCI-AD but not in AD. The pharmacodynamic model of the PACAP-PAC1 interaction best predicted cognitive function in the superior frontal gyrus, but it was less predictive in the middle temporal gyrus and failed to be predictive in the primary visual cortex. Deficits in PACAP are associated with clinical severity in the MCI and dementia stages of AD. Additional studies are needed to clarify the role of PACAP deficits in the predisposition to, pathogenesis of, and treatment of AD.

Evaluating the behavior of polychlorinated biphenyl compounds in Lake Superior using a dynamic multimedia model

NASA Astrophysics Data System (ADS)

Khan, T.; Perlinger, J. A.; Urban, N. R.

2017-12-01

Certain toxic, persistent, bioaccumulative, and semivolatile compounds known as atmosphere-surface exchangeable pollutants or ASEPs are emitted into the environment by primary sources, are transported, deposited to water surfaces, and can be later re-emitted causing the water to act as a secondary source. Polychlorinated biphenyl (PCB) compounds, a class of ASEPs, are of major concern in the Laurentian Great Lakes because of their historical use primarily as additives to oils and industrial fluids, and discharge from industrial sources. Following the ban on production in the U.S. in 1979, atmospheric concentrations of PCBs in the Lake Superior region decreased rapidly. Subsequently, PCB concentrations in the lake surface water also reached near equilibrium as the atmospheric levels of PCBs declined. However, previous studies on long-term PCB levels and trends in lake trout and walleye suggested that the initial rate of decline of PCB concentrations in fish has leveled off in Lake Superior. In this study, a dynamic multimedia flux model was developed with the objective to investigate the observed levelling off of PCB concentrations in Lake Superior fish. The model structure consists of two water layers (the epilimnion and the hypolimnion), and the surface mixed sediment layer, while atmospheric deposition is the primary external pathway of PCB inputs to the lake. The model was applied for different PCB congeners having a range of hydrophobicity and volatility. Using this model, we compare the long-term trends in predicted PCB concentrations in different environmental media with relevant available measurements for Lake Superior. We examine the seasonal depositional and exchange patterns, the relative importance of different process terms, and provide the most probable source of the current observed PCB levels in Lake Superior fish. In addition, we evaluate the role of current atmospheric PCB levels in sustaining the observed fish concentrations and appraise the need for continuous atmospheric PCB monitoring by the Great Lakes Integrated Atmospheric Deposition Network. By combining the modeled lake and biota response times resulting from atmospheric PCB inputs, we predict the time scale for safe fish consumption in Lake Superior.

Performance of some diagnostic systems in the prediction of occlusal caries in permanent molars in 6- and 11-year-old children.

PubMed

Fennis-Ie, Y L; Verdonschot, E H; van't Hof, M A

1998-01-01

Attempts have been made to develop diagnostic methods which enable an early diagnosis of occlusal lesions which are not detectable by visual inspection. The aim of this study was to compare the performance of visual inspection focused on finding signs of fissure decalcification and discoloration, visual inspection upon fibre-optic transillumination (FOTI), and electrical conductance measurements (ECMs) in predicting the onset of occlusal caries in 6- and 11-year-old children. Fifty children aged 5-7 and 11-15 years, having first or second permanent molar teeth that were not exposed to the oral environment for more than half a year, participated in the study. Following baseline data recording, the diagnostic measurements were repeated six times at 6-month intervals over a period of 2.5 years. Data were collected at predefined sites in the fissures. During the study, 220 of the 652 sites, i.e. 75 of 197 molars in 31 of the 50 children were judged to require a sealant or a sealant restoration. Two examiners jointly decided on the decay status at the sites. Survival plots showed that ECMs were superior to FOTI and fissure discoloration in predicting the onset of occlusal caries, although the differences were small. ECM is a better predictor of occlusal caries than fissure discoloration and FOTI, although the differences among the performance of the three methods in this study were very small. A cost-effective analysis is envisaged to obtain insight into the practical value of ECMs in the prediction of occlusal caries and, thus, into the effectiveness of sealant application.

Predicting the Functional Impact of KCNQ1 Variants of Unknown Significance.

PubMed

Li, Bian; Mendenhall, Jeffrey L; Kroncke, Brett M; Taylor, Keenan C; Huang, Hui; Smith, Derek K; Vanoye, Carlos G; Blume, Jeffrey D; George, Alfred L; Sanders, Charles R; Meiler, Jens

2017-10-01

An emerging standard-of-care for long-QT syndrome uses clinical genetic testing to identify genetic variants of the KCNQ1 potassium channel. However, interpreting results from genetic testing is confounded by the presence of variants of unknown significance for which there is inadequate evidence of pathogenicity. In this study, we curated from the literature a high-quality set of 107 functionally characterized KCNQ1 variants. Based on this data set, we completed a detailed quantitative analysis on the sequence conservation patterns of subdomains of KCNQ1 and the distribution of pathogenic variants therein. We found that conserved subdomains generally are critical for channel function and are enriched with dysfunctional variants. Using this experimentally validated data set, we trained a neural network, designated Q1VarPred, specifically for predicting the functional impact of KCNQ1 variants of unknown significance. The estimated predictive performance of Q1VarPred in terms of Matthew's correlation coefficient and area under the receiver operating characteristic curve were 0.581 and 0.884, respectively, superior to the performance of 8 previous methods tested in parallel. Q1VarPred is publicly available as a web server at http://meilerlab.org/q1varpred. Although a plethora of tools are available for making pathogenicity predictions over a genome-wide scale, previous tools fail to perform in a robust manner when applied to KCNQ1. The contrasting and favorable results for Q1VarPred suggest a promising approach, where a machine-learning algorithm is tailored to a specific protein target and trained with a functionally validated data set to calibrate informatics tools. © 2017 American Heart Association, Inc.

Sensorimotor and Cognitive Predictors of Impaired Gait Adaptability in Older People.

PubMed

Caetano, Maria Joana D; Menant, Jasmine C; Schoene, Daniel; Pelicioni, Paulo H S; Sturnieks, Daina L; Lord, Stephen R

2017-09-01

The ability to adapt gait when negotiating unexpected hazards is crucial to maintain stability and avoid falling. This study investigated whether impaired gait adaptability in a task including obstacle and stepping targets is associated with cognitive and sensorimotor capacities in older adults. Fifty healthy older adults (74±7 years) were instructed to either (a) avoid an obstacle at usual step distance or (b) step onto a target at either a short or long step distance projected on a walkway two heel strikes ahead and then continue walking. Participants also completed cognitive and sensorimotor function assessments. Stroop test and reaction time performance significantly discriminated between participants who did and did not make stepping errors, and poorer Trail-Making test performance predicted shorter penultimate step length in the obstacle avoidance condition. Slower reaction time predicted poorer stepping accuracy; increased postural sway, weaker quadriceps strength, and poorer Stroop and Trail-Making test performances predicted increased number of steps taken to approach the target/obstacle and shorter step length; and increased postural sway and higher concern about falling predicted slower step velocity. Superior executive function, fast processing speed, and good muscle strength and balance were all associated with successful gait adaptability. Processing speed appears particularly important for precise foot placements; cognitive capacity for step length adjustments; and early and/or additional cognitive processing involving the inhibition of a stepping pattern for obstacle avoidance. This information may facilitate fall risk assessments and fall prevention strategies. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Automotive Stirling engine: Mod 2 design report

NASA Technical Reports Server (NTRS)

Nightingale, Noel P.

1986-01-01

The design of an automotive Stirling engine that achieves the superior fuel economy potential of the Stirling cycle is described. As the culmination of a 9-yr development program, this engine, designated the Mod 2, also nullifies arguments that Stirling engines are heavy, expensive, unreliable, demonstrating poor performance. Installed in a General Motors Chevrolet Celebrity car, this engine has a predicted combined fuel economy on unleaded gasoline of 17.5 km/l (41 mpg)- a value 50% above the current vehicle fleet average. The Mod 2 Stirling engine is a four-cylinder V-drive design with a single crankshaft. The engine is also equipped with all the controls and auxiliaries necessary for automotive operation.

Forecasting volatility of SSEC in Chinese stock market using multifractal analysis

NASA Astrophysics Data System (ADS)

Wei, Yu; Wang, Peng

2008-03-01

In this paper, taking about 7 years’ high-frequency data of the Shanghai Stock Exchange Composite Index (SSEC) as an example, we propose a daily volatility measure based on the multifractal spectrum of the high-frequency price variability within a trading day. An ARFIMA model is used to depict the dynamics of this multifractal volatility (MFV) measures. The one-day ahead volatility forecasting performances of the MFV model and some other existing volatility models, such as the realized volatility model, stochastic volatility model and GARCH, are evaluated by the superior prediction ability (SPA) test. The empirical results show that under several loss functions, the MFV model obtains the best forecasting accuracy.

Indium phosphide solar cell research in the US: Comparison with nonphotovoltaic sources

NASA Technical Reports Server (NTRS)

Weinberg, I.; Swartz, C. K.; Hart, R. E., Jr.

1989-01-01

Highlights of the InP solar cell research program are presented. Homojunction cells with AMO efficiences approaching 19 percent were demonstrated while 17 percent was achieved for indium tin oxide (ITO)/InP cells. The superior radiation resistance of these latter two cell configurations over both Si and GaAs were demonstrated. InP cells on board the LIPS III satellite show no degradation after more than a year in orbit. Computer modeling calculations were directed toward radiation damage predictions and the specification of concentrator cell parameters. Computed array specific powers, for a specific orbit, are used to compare the performance of an InP solar cell array to solar dynamic and nuclear systems.

Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

PubMed

Alavash, Mohsen; Doebler, Philipp; Holling, Heinz; Thiel, Christiane M; Gießing, Carsten

2015-03-01

Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing, especially in brain regions involved in working memory performance. Copyright © 2014 Elsevier Inc. All rights reserved.

Characteristics of superior orbital subperiosteal abscesses in children.

PubMed

Quintanilla-Dieck, Lourdes; Chinnadurai, Sivakumar; Goudy, Steven L; Virgin, Frank W

2017-03-01

Superior pediatric orbital subperiosteal abscesses (SPAs) are less common than medial ones, and clinical features specific to patients with superior SPAs have not been well defined. Clinical characteristics between patients with superior and medial SPAs are compared to determine whether superior location is a risk factor for surgical intervention. Retrospective cohort study. The target population consisted of patients diagnosed with an SPA and seen by the pediatric otolaryngology service at a tertiary children's hospital between January 2010 and October 2014. Imaging characteristics including proptosis, hypoglobus, intraorbital air, and abscess volume as well as treatment interventions were reviewed. Forty patients between 5 and 17 years of age treated for an orbital SPA were identified. Thirteen patients were identified as having superior SPAs; 27 had medial SPAs. The average ages in the two groups were 10.92 and 9.26 years, respectively. The odds ratio for surgical treatment per each increasing year of age was 1.5 (P = .004). The proportion of patients requiring surgery was significantly different between the groups (12/13 superior vs. 13/27 medial, P = .01). The predominant organism group cultured in surgical patients was Streptococcus anginosus (8/24, 29.17%). Superior SPA patients had significantly more proptosis, hypoglobus, and abscess volume on computed tomography scan. Patients with superior SPAs may present with more advanced disease, leading to a higher rate of characteristics such as proptosis, hypoglobus, and intraorbital air, factors that would predispose to surgical drainage. We found that abscess volume was the most predictive of surgery. 4 Laryngoscope, 127:735-740, 2017. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

Prediction Surface Morphology of Nanostructure Fabricated by Nano-Oxidation Technology.

PubMed

Huang, Jen-Ching; Chang, Ho; Kuo, Chin-Guo; Li, Jeen-Fong; You, Yong-Chin

2015-12-04

Atomic force microscopy (AFM) was used for visualization of a nano-oxidation technique performed on diamond-like carbon (DLC) thin film. Experiments of the nano-oxidation technique of the DLC thin film include those on nano-oxidation points and nano-oxidation lines. The feature sizes of the DLC thin film, including surface morphology, depth, and width, were explored after application of a nano-oxidation technique to the DLC thin film under different process parameters. A databank for process parameters and feature sizes of thin films was then established, and multiple regression analysis (MRA) and a back-propagation neural network (BPN) were used to carry out the algorithm. The algorithmic results are compared with the feature sizes acquired from experiments, thus obtaining a prediction model of the nano-oxidation technique of the DLC thin film. The comparative results show that the prediction accuracy of BPN is superior to that of MRA. When the BPN algorithm is used to predict nano-point machining, the mean absolute percentage errors (MAPE) of depth, left side, and right side are 8.02%, 9.68%, and 7.34%, respectively. When nano-line machining is being predicted, the MAPEs of depth, left side, and right side are 4.96%, 8.09%, and 6.77%, respectively. The obtained data can also be used to predict cross-sectional morphology in the DLC thin film treated with a nano-oxidation process.

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method.

PubMed

Liang, Gaozhen; Dong, Chunwang; Hu, Bin; Zhu, Hongkai; Yuan, Haibo; Jiang, Yongwen; Hao, Guoshuang

2018-05-18

Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture content of withered leaves. First, according to the time sequences using computer visual system collected visible light images of tea leaf surfaces, and color and texture characteristics are extracted through the spatial changes of colors. Then quantitative prediction models for moisture content detection of withered tea leaves was established through linear PLS (Partial Least Squares) and non-linear SVM (Support Vector Machine). The results showed correlation coefficients higher than 0.8 between the water contents and green component mean value (G), lightness component mean value (L * ) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.

Prediction of welding shrinkage deformation of bridge steel box girder based on wavelet neural network

NASA Astrophysics Data System (ADS)

Tao, Yulong; Miao, Yunshui; Han, Jiaqi; Yan, Feiyun

2018-05-01

Aiming at the low accuracy of traditional forecasting methods such as linear regression method, this paper presents a prediction method for predicting the relationship between bridge steel box girder and its displacement with wavelet neural network. Compared with traditional forecasting methods, this scheme has better local characteristics and learning ability, which greatly improves the prediction ability of deformation. Through analysis of the instance and found that after compared with the traditional prediction method based on wavelet neural network, the rigid beam deformation prediction accuracy is higher, and is superior to the BP neural network prediction results, conform to the actual demand of engineering design.

Dual tracer functional imaging of gastroenteropancreatic neuroendocrine tumors using 68Ga-DOTA-NOC PET-CT and 18F-FDG PET-CT: competitive or complimentary?

PubMed

Naswa, Niraj; Sharma, Punit; Gupta, Santosh Kumar; Karunanithi, Sellam; Reddy, Rama Mohan; Patnecha, Manish; Lata, Sneh; Kumar, Rakesh; Malhotra, Arun; Bal, Chandrasekhar

2014-01-01

This study aimed to compare the diagnostic performance of Ga-DOTANOC PET/CT with F-FDG PET/CT in the patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs). Data of 51 patients with definite histological diagnosis of GEP-NET who underwent both Ga-DOTA-NOC PET-CT and F-FDG PET-CT within a span of 15 days were selected for this retrospective analysis. Sensitivity, specificity, and predictive values were calculated for Ga-DOTA-NOC PET-CT and F-FDG PET-CT, and results were compared both on patientwise and regionwise analysis. Ga-DOTA-NOC PET-CT is superior to F-FDG PET-CT on patientwise analysis (P < 0.0001). On regionwise analysis, Ga-DOTA-NOC PET-CT is superior to F-FDG PET-CT only for lymph node metastases (P < 0.003). Although Ga-DOTA-NOC PET-CT detected more liver and skeletal lesions compared with F-FDG PET-CT, the difference was not statistically significant. In addition, the results of combined imaging helped in selecting candidates who would undergo the appropriate mode of treatment, whether octreotide therapy or conventional chemotherapy Ga-DOTA-NOC PET-CT seems to be superior to F-FDG PET-CT for imaging GEP-NETs. However, their role seems to be complementary because combination of Ga-DOTA-NOC PET-CT and F-FDG PET-CT in such patients helps demonstrate the total disease burden and segregate them to proper therapeutic groups.

Enhanced timing abilities in percussionists generalize to rhythms without a musical beat.

PubMed

Cameron, Daniel J; Grahn, Jessica A

2014-01-01

The ability to entrain movements to music is arguably universal, but it is unclear how specialized training may influence this. Previous research suggests that percussionists have superior temporal precision in perception and production tasks. Such superiority may be limited to temporal sequences that resemble real music or, alternatively, may generalize to musically implausible sequences. To test this, percussionists and nonpercussionists completed two tasks that used rhythmic sequences varying in musical plausibility. In the beat tapping task, participants tapped with the beat of a rhythmic sequence over 3 stages: finding the beat (as an initial sequence played), continuation of the beat (as a second sequence was introduced and played simultaneously), and switching to a second beat (the initial sequence finished, leaving only the second). The meters of the two sequences were either congruent or incongruent, as were their tempi (minimum inter-onset intervals). In the rhythm reproduction task, participants reproduced rhythms of four types, ranging from high to low musical plausibility: Metric simple rhythms induced a strong sense of the beat, metric complex rhythms induced a weaker sense of the beat, nonmetric rhythms had no beat, and jittered nonmetric rhythms also had no beat as well as low temporal predictability. For both tasks, percussionists performed more accurately than nonpercussionists. In addition, both groups were better with musically plausible than implausible conditions. Overall, the percussionists' superior abilities to entrain to, and reproduce, rhythms generalized to musically implausible sequences.

Performance characteristics of five triage tools for major incidents involving traumatic injuries to children.

PubMed

Price, C L; Brace-McDonnell, S J; Stallard, N; Bleetman, A; Maconochie, I; Perkins, G D

2016-05-01

Context Triage tools are an essential component of the emergency response to a major incident. Although fortunately rare, mass casualty incidents involving children are possible which mandate reliable triage tools to determine the priority of treatment. To determine the performance characteristics of five major incident triage tools amongst paediatric casualties who have sustained traumatic injuries. Retrospective observational cohort study using data from 31,292 patients aged less than 16 years who sustained a traumatic injury. Data were obtained from the UK Trauma Audit and Research Network (TARN) database. Interventions Statistical evaluation of five triage tools (JumpSTART, START, CareFlight, Paediatric Triage Tape/Sieve and Triage Sort) to predict death or severe traumatic injury (injury severity score >15). Main outcome measures Performance characteristics of triage tools (sensitivity, specificity and level of agreement between triage tools) to identify patients at high risk of death or severe injury. Of the 31,292 cases, 1029 died (3.3%), 6842 (21.9%) had major trauma (defined by an injury severity score >15) and 14,711 (47%) were aged 8 years or younger. There was variation in the performance accuracy of the tools to predict major trauma or death (sensitivities ranging between 36.4 and 96.2%; specificities 66.0-89.8%). Performance characteristics varied with the age of the child. CareFlight had the best overall performance at predicting death, with the following sensitivity and specificity (95% CI) respectively: 95.3% (93.8-96.8) and 80.4% (80.0-80.9). JumpSTART was superior for the triaging of children under 8 years; sensitivity and specificity (95% CI) respectively: 86.3% (83.1-89.5) and 84.8% (84.2-85.5). The triage tools were generally better at identifying patients who would die than those with non-fatal severe injury. This statistical evaluation has demonstrated variability in the accuracy of triage tools at predicting outcomes for children who sustain traumatic injuries. No single tool performed consistently well across all evaluated scenarios. Copyright © 2015 Elsevier Ltd. All rights reserved.

Aging and the picture superiority effect in recall.

PubMed

Winograd, E; Smith, A D; Simon, E W

1982-01-01

One recurrent theme in the literature on aging and memory is that the decline of memory for nonverbal information is steeper than for verbal information. This research compares verbal and visual encoding using the picture superiority effect, the finding that pictures are remembered better than words. In the first experiment, an interaction was found between age and type of material; younger subjects recalled more pictures than words while older subjects did not. However, the overall effect was small and two further experiments were conducted. In both of these experiments, the picture superiority effect was found in both age groups with no interaction. In addition, performing a semantic orienting task had no effect on recall. The finding of a picture superiority effect in older subjects indicates that nonverbal codes can be effectively used by subjects in all age groups to facilitate memory performance.

A cost-effective process to prepare VO{sub 2} (M) powder and films with superior thermochromic properties

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

Xiao, Xiudi; Zhang, Hua; Chai, Guanqi

2014-03-01

Graphical abstract: Combining codeposition and short time post annealing, VO{sub 2} (M) with high quality and excellent phase transition performance is obtained. After mixing the VO{sub 2} powder with acrylic resin, the composite films deposited on glass show superior visible transmission and solar modulation, which can be used as an excellent candidate of low cost smart window in energy saving field. - Highlights: • The VO{sub 2} powder obtained by short time thermolysis method is high purity and crystallinity with superior phase transition performance. • The maximum decreasing efficiency of phase transition temperature is about −30 K/at% with w =more » 0.4 at%. • After mixing VO{sub 2} powder with acrylic resin, the maximal visible transmission of the composite films is 48% and the transmission modulation at 2000 nm is 37.3% with phase transition temperature of 66.2 °C. • Though the phase transition performance is weakened by tungsten doping, the film prepared by 1.3 at% tungsten doped VO{sub 2} still show superior transmission modulation about 26.4%, which means that it is a potential candidate as smart windows. - Abstract: VO{sub 2} powder with superior phase transition performance was prepared by convenient thermolysis method. The results illustrated that VO{sub 2} powder show high purity and crystallinity. VO{sub 2} particles are transformed from cluster to quasi-sphere with the increase of annealing temperature. The DSC analysis proves that VO{sub 2} show superior phase transition performance around 68 °C. The phase transition temperature can be reduced to 33.5 °C by 1.8 at% tungsten doping. The maximum decreasing efficiency of phase transition temperature is about −30 K/at% with w = 0.4 at%. After mixing VO{sub 2} powder with acrylic resin, the maximal visible transmission of the composite thin films on glass is 48% and the transmission modulation at 2000 nm is 37.3% with phase transition temperature of 66.2 °C. Though the phase transition performance is weakened by tungsten doping, the film prepared by 1.3 at% tungsten doped VO{sub 2} still show superior transmission modulation about 26.4% at 2000 nm, which means that it is a potential candidate as smart windows.« less

Do Children with Visual Impairments Demonstrate Superior Short-term Memory, Memory Strategies, and Metamemory?

ERIC Educational Resources Information Center

Wyver, Shirley R.; Markham, Roslyn

1998-01-01

This study compared the memory processes underpinning the performance of 19 children with visual impairments and 19 sighted children on the Digit Span subtest of the Wechsler Intelligence Scales. No support was found for claims of the superior performance of children with visual impairments on the subtest nor of a greater awareness of memory…

Composite Quality Measures for Common Inpatient Medical Conditions

PubMed Central

Chen, Lena M.; Staiger, Douglas O.; Birkmeyer, John D.; Ryan, Andrew M.; Zhang, Wenying; Dimick, Justin B.

2014-01-01

Background Public reporting on quality aims to help patients select better hospitals. However, individual quality measures are sub-optimal in identifying superior and inferior hospitals based on outcome performance. Objective To combine structure, process, and outcome measures into an empirically-derived composite quality measure for heart failure (HF), acute myocardial infarction (AMI), and pneumonia (PNA). To assess how well the composite measure predicts future high and low performers, and explains variance in future hospital mortality. Research Design Using national Medicare data, we created a cohort of older patients treated at an acute care hospital for HF (n=1,203,595), AMI (n=625,595), or PNA (n=1,234,299). We ranked hospitals based on their July 2005 to June 2008 performance on the composite. We then estimated the odds of future (July to December 2009) 30-day, risk-adjusted mortality at the worst vs. best quintile of hospitals. We repeated this analysis using 2005-2008 performance on existing quality indicators, including mortality. Results The composite (vs. Hospital Compare) explained 68% (vs. 39%) of variation in future AMI mortality rates. In 2009, if an AMI patient had chosen a hospital in the worst vs. best quintile of performance using 2005-2008 composite (vs. Hospital Compare) rankings, he or she would have had 1.61 (vs. 1.39) times the odds of dying in 30 days (p-value for difference < 0.001). Results were similar for HF and PNA. Conclusions Composite measures of quality for HF, AMI, and PNA performed better than existing measures at explaining variation in future mortality and predicting future high and low performers. PMID:23942222

Flight motor set 360L007 (STS-33R)

NASA Technical Reports Server (NTRS)

Garecht, Diane

1990-01-01

Flight motor set 360L007 was launched as part of NASA space shuttle mission STS-33R. As with all previous redesigned solid rocket motor launches, overall motor performance was excellent. There were no debris concerns for either motor. Both motors exhibited unbonds on one factory joint weatherseal. All ballistics contract end item specification parameters were verified, with the exception of ignition interval and rise rates. Ignition interval and rise rates could not be verified due to the elimination of developmental flight instrumentation from fourth flight and subsequent, but the low sample rate data that were available showed nominal propulsion performance. All ballistic and mass property parameters closely matched the predicted values and were well within the required contract end item specification levels that could be assessed. All 108 Ground Environment Instrumentation (GEI) measurements performed properly throughout the prelaunch phase. Evaluation of the GEI measurements again verified thermal model analysis data and showed agreement with predicted environmental effects. No launch commit criteria thermal violations occurred. All joint heaters operated normally, but a high voltage reading was noted on the left hand aft heater, which was immediately determined to be a voltage sensor error and not a heater anomaly due to no current increase. Postflight inspection again verified superior performance of the insulation, phenolics, metal parts, and seals. Postflight evaluation indicated both nozzles performed as expected during flight. All combustion gas was contained by insulation in the field and case-to-nozzle joints.

Can somatosensory and visual evoked potentials predict neurological outcome during targeted temperature management in post cardiac arrest patients?

PubMed

Choi, Seung Pill; Park, Kyu Nam; Wee, Jung Hee; Park, Jeong Ho; Youn, Chun Song; Kim, Han Joon; Oh, Sang Hoon; Oh, Yoon Sang; Kim, Soo Hyun; Oh, Joo Suk

2017-10-01

In cardiac arrest patients treated with targeted temperature management (TTM), it is not certain if somatosensory evoked potentials (SEPs) and visual evoked potentials (VEPs) can predict neurological outcomes during TTM. The aim of this study was to investigate the prognostic value of SEPs and VEPs during TTM and after rewarming. This retrospective cohort study included comatose patients resuscitated from cardiac arrest and treated with TTM between March 2007 and July 2015. SEPs and VEPs were recorded during TTM and after rewarming in these patients. Neurological outcome was assessed at discharge by the Cerebral Performance Category (CPC) Scale. In total, 115 patients were included. A total of 175 SEPs and 150 VEPs were performed. Five SEPs during treated with TTM and nine SEPs after rewarming were excluded from outcome prediction by SEPs due to an indeterminable N20 response because of technical error. Using 80 SEPs and 85 VEPs during treated with TTM, absent SEPs yielded a sensitivity of 58% and a specificity of 100% for poor outcome (CPC 3-5), and absent VEPs predicted poor neurological outcome with a sensitivity of 44% and a specificity of 96%. The AUC of combination of SEPs and VEPs was superior to either test alone (0.788 for absent SEPs and 0.713 for absent VEPs compared with 0.838 for the combination). After rewarming, absent SEPs and absent VEPs predicted poor neurological outcome with a specificity of 100%. When SEPs and VEPs were combined, VEPs slightly increased the prognostic accuracy of SEPs alone. Although one patient with absent VEP during treated with TTM had a good neurological outcome, none of the patients with good neurological outcome had an absent VEP after rewarming. Absent SEPs could predict poor neurological outcome during TTM as well as after rewarming. Absent VEPs may predict poor neurological outcome in both periods and VEPs may provide additional prognostic value in outcome prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1) for operational seasonal forecasting

NASA Astrophysics Data System (ADS)

Takaya, Yuhei; Yasuda, Tamaki; Fujii, Yosuke; Matsumoto, Satoshi; Soga, Taizo; Mori, Hirotoshi; Hirai, Masayuki; Ishikawa, Ichiro; Sato, Hitoshi; Shimpo, Akihiko; Kamachi, Masafumi; Ose, Tomoaki

2017-01-01

This paper describes the operational seasonal prediction system of the Japan Meteorological Agency (JMA), the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1), which was in operation at JMA during the period between February 2010 and May 2015. The predictive skill of the system was assessed with a set of retrospective seasonal predictions (reforecasts) covering 30 years (1981-2010). JMA/MRI-CPS1 showed reasonable predictive skill for the El Niño-Southern Oscillation, comparable to the skills of other state-of-the-art systems. The one-tiered approach adopted in JMA/MRI-CPS1 improved its overall predictive skills for atmospheric predictions over those of the two-tiered approach of the previous uncoupled system. For 3-month predictions with a 1-month lead, JMA/MRI-CPS1 showed statistically significant skills in predicting 500-hPa geopotential height and 2-m temperature in East Asia in most seasons; thus, it is capable of providing skillful seasonal predictions for that region. Furthermore, JMA/MRI-CPS1 was superior overall to the previous system for atmospheric predictions with longer (4-month) lead times. In particular, JMA/MRI-CPS1 was much better able to predict the Asian Summer Monsoon than the previous two-tiered system. This enhanced performance was attributed to the system's ability to represent atmosphere-ocean coupled variability over the Indian Ocean and the western North Pacific from boreal winter to summer following winter El Niño events, which in turn influences the East Asian summer climate through the Pacific-Japan teleconnection pattern. These substantial improvements obtained by using an atmosphere-ocean coupled general circulation model underpin its success in providing more skillful seasonal forecasts on an operational basis.

Performance Models for the Spike Banded Linear System Solver

DOE PAGES

Manguoglu, Murat; Saied, Faisal; Sameh, Ahmed; ...

2011-01-01

With availability of large-scale parallel platforms comprised of tens-of-thousands of processors and beyond, there is significant impetus for the development of scalable parallel sparse linear system solvers and preconditioners. An integral part of this design process is the development of performance models capable of predicting performance and providing accurate cost models for the solvers and preconditioners. There has been some work in the past on characterizing performance of the iterative solvers themselves. In this paper, we investigate the problem of characterizing performance and scalability of banded preconditioners. Recent work has demonstrated the superior convergence properties and robustness of banded preconditioners,more » compared to state-of-the-art ILU family of preconditioners as well as algebraic multigrid preconditioners. Furthermore, when used in conjunction with efficient banded solvers, banded preconditioners are capable of significantly faster time-to-solution. Our banded solver, the Truncated Spike algorithm is specifically designed for parallel performance and tolerance to deep memory hierarchies. Its regular structure is also highly amenable to accurate performance characterization. Using these characteristics, we derive the following results in this paper: (i) we develop parallel formulations of the Truncated Spike solver, (ii) we develop a highly accurate pseudo-analytical parallel performance model for our solver, (iii) we show excellent predication capabilities of our model – based on which we argue the high scalability of our solver. Our pseudo-analytical performance model is based on analytical performance characterization of each phase of our solver. These analytical models are then parameterized using actual runtime information on target platforms. An important consequence of our performance models is that they reveal underlying performance bottlenecks in both serial and parallel formulations. All of our results are validated on diverse heterogeneous multiclusters – platforms for which performance prediction is particularly challenging. Finally, we provide predict the scalability of the Spike algorithm using up to 65,536 cores with our model. In this paper we extend the results presented in the Ninth International Symposium on Parallel and Distributed Computing.« less

Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC.

PubMed

Contreras-Torres, Ernesto

2018-06-02

In this study, I introduce novel global and local 0D-protein descriptors based on a statistical quantity named Total Sum of Squares (TSS). This quantity represents the sum of the squares differences of amino acid properties from the arithmetic mean property. As an extension, the amino acid-types and amino acid-groups formalisms are used for describing zones of interest in proteins. To assess the effectiveness of the proposed descriptors, a Nearest Neighbor model for predicting the major four protein structural classes was built. This model has a success rate of 98.53% on the jackknife cross-validation test; this performance being superior to other reported methods despite the simplicity of the predictor. Additionally, this predictor has an average success rate of 98.35% in different cross-validation tests performed. A value of 0.98 for the Kappa statistic clearly discriminates this model from a random predictor. The results obtained by the Nearest Neighbor model demonstrated the ability of the proposed descriptors not only to reflect relevant biochemical information related to the structural classes of proteins but also to allow appropriate interpretability. It can thus be expected that the current method may play a supplementary role to other existing approaches for protein structural class prediction and other protein attributes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences

PubMed Central

Zhang, Long; Jia, Lianyin; Ren, Yazhou

2017-01-01

Protein-protein interactions (PPIs) play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs) and a novel local conjoint triad description (LCTD) feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae, DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC) as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study. PMID:29117139

Changing head model extent affects finite element predictions of transcranial direct current stimulation distributions

NASA Astrophysics Data System (ADS)

Indahlastari, Aprinda; Chauhan, Munish; Schwartz, Benjamin; Sadleir, Rosalind J.

2016-12-01

Objective. In this study, we determined efficient head model sizes relative to predicted current densities in transcranial direct current stimulation (tDCS). Approach. Efficiency measures were defined based on a finite element (FE) simulations performed using nine human head models derived from a single MRI data set, having extents varying from 60%-100% of the original axial range. Eleven tissue types, including anisotropic white matter, and three electrode montages (T7-T8, F3-right supraorbital, Cz-Oz) were used in the models. Main results. Reducing head volume extent from 100% to 60%, that is, varying the model’s axial range from between the apex and C3 vertebra to one encompassing only apex to the superior cerebellum, was found to decrease the total modeling time by up to half. Differences between current density predictions in each model were quantified by using a relative difference measure (RDM). Our simulation results showed that {RDM} was the least affected (a maximum of 10% error) for head volumes modeled from the apex to the base of the skull (60%-75% volume). Significance. This finding suggested that the bone could act as a bioelectricity boundary and thus performing FE simulations of tDCS on the human head with models extending beyond the inferior skull may not be necessary in most cases to obtain reasonable precision in current density results.

Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences.

PubMed

Wang, Jun; Zhang, Long; Jia, Lianyin; Ren, Yazhou; Yu, Guoxian

2017-11-08

Protein-protein interactions (PPIs) play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs) and a novel local conjoint triad description (LCTD) feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae , DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC) as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study.

Contribution of intraoperative neuromonitoring to the identification of the external branch of superior laryngeal nerve

PubMed Central

Aygün, Nurcihan; Uludağ, Mehmet; İşgör, Adnan

2017-01-01

Objective We evaluated the contribution of intraoperative neuromonitoring to the visual and functional identification of the external branch of the superior laryngeal nerve. Material and Methods The prospectively collected data of patients who underwent thyroid surgery with intraoperative neuromonitoring for external branch of the superior laryngeal nerve exploration were assessed retrospectively. The surface endotracheal tube-based Medtronic NIM3 intraoperative neuromonitoring device was used. The external branch of the superior laryngeal nerve function was evaluated by the cricothyroid muscle twitch. In addition, contribution of external branch of the superior laryngeal nerve to the vocal cord adduction was evaluated using electromyographic records. Results The study included data of 126 (female, 103; male, 23) patients undergoing thyroid surgery, with a mean age of 46.2±12.2 years (range, 18–75 years), and 215 neck sides were assessed. Two hundred and one (93.5%) of 215 external branch of the superior laryngeal nerves were identified, of which 60 (27.9%) were identified visually before being stimulated with a monopolar stimulator probe. Eighty-nine (41.4%) external branch of the superior laryngeal nerves were identified visually after being identified with a probe. Although 52 (24.1%) external branch of the superior laryngeal nerves were identified with a probe, they were not visualized. Intraoperative neuromonitoring provided a significant contribution to visual (p<0.001) and functional (p<0.001) identification of external branch of the superior laryngeal nerves. Additionally, positive electromyographic responses were recorded from 160 external branch of the superior laryngeal nerves (74.4%). Conclusion Intraoperative neuromonitoring provides an important contribution to visual and functional identification of external branch of the superior laryngeal nerves. We believe that it can not be predicted whether the external branch of the superior laryngeal nerve is at risk or not and the nerve is often invisible; thus, intraoperative neuromonitoring may routinely be used in superior pole dissection. Glottic electromyography response obtained via external branch of the superior laryngeal nerve stimulation provides quantifiable information in addition to the simple visualization of the cricothyroid muscle twitch. PMID:28944328

A Functional Neuroimaging Study of Sound Localization: Visual Cortex Activity Predicts Performance in Early-Blind Individuals

PubMed Central

Gougoux, Frédéric; Zatorre, Robert J; Lassonde, Maryse; Voss, Patrice

2005-01-01

Blind individuals often demonstrate enhanced nonvisual perceptual abilities. However, the neural substrate that underlies this improved performance remains to be fully understood. An earlier behavioral study demonstrated that some early-blind people localize sounds more accurately than sighted controls using monaural cues. In order to investigate the neural basis of these behavioral differences in humans, we carried out functional imaging studies using positron emission tomography and a speaker array that permitted pseudo-free-field presentations within the scanner. During binaural sound localization, a sighted control group showed decreased cerebral blood flow in the occipital lobe, which was not seen in early-blind individuals. During monaural sound localization (one ear plugged), the subgroup of early-blind subjects who were behaviorally superior at sound localization displayed two activation foci in the occipital cortex. This effect was not seen in blind persons who did not have superior monaural sound localization abilities, nor in sighted individuals. The degree of activation of one of these foci was strongly correlated with sound localization accuracy across the entire group of blind subjects. The results show that those blind persons who perform better than sighted persons recruit occipital areas to carry out auditory localization under monaural conditions. We therefore conclude that computations carried out in the occipital cortex specifically underlie the enhanced capacity to use monaural cues. Our findings shed light not only on intermodal compensatory mechanisms, but also on individual differences in these mechanisms and on inhibitory patterns that differ between sighted individuals and those deprived of vision early in life. PMID:15678166

Bearing performance degradation assessment based on time-frequency code features and SOM network

NASA Astrophysics Data System (ADS)

Zhang, Yan; Tang, Baoping; Han, Yan; Deng, Lei

2017-04-01

Bearing performance degradation assessment and prognostics are extremely important in supporting maintenance decision and guaranteeing the system’s reliability. To achieve this goal, this paper proposes a novel feature extraction method for the degradation assessment and prognostics of bearings. Features of time-frequency codes (TFCs) are extracted from the time-frequency distribution using a hybrid procedure based on short-time Fourier transform (STFT) and non-negative matrix factorization (NMF) theory. An alternative way to design the health indicator is investigated by quantifying the similarity between feature vectors using a self-organizing map (SOM) network. On the basis of this idea, a new health indicator called time-frequency code quantification error (TFCQE) is proposed to assess the performance degradation of the bearing. This indicator is constructed based on the bearing real-time behavior and the SOM model that is previously trained with only the TFC vectors under the normal condition. Vibration signals collected from the bearing run-to-failure tests are used to validate the developed method. The comparison results demonstrate the superiority of the proposed TFCQE indicator over many other traditional features in terms of feature quality metrics, incipient degradation identification and achieving accurate prediction. Highlights • Time-frequency codes are extracted to reflect the signals’ characteristics. • SOM network served as a tool to quantify the similarity between feature vectors. • A new health indicator is proposed to demonstrate the whole stage of degradation development. • The method is useful for extracting the degradation features and detecting the incipient degradation. • The superiority of the proposed method is verified using experimental data.

Identification of New Tools to Predict Surgical Performance of Novices using a Plastic Surgery Simulator.

PubMed

Kazan, Roy; Viezel-Mathieu, Alex; Cyr, Shantale; Hemmerling, Thomas M; Lin, Samuel J; Gilardino, Mirko S

2018-04-09

To identify new tools capable of predicting surgical performance of novices on an augmentation mammoplasty simulator. The pace of technical skills acquisition varies between residents and may necessitate more time than that allotted by residency training before reaching competence. Identifying applicants with superior innate technical abilities might shorten learning curves and the time to reach competence. The objective of this study is to identify new tools that could predict surgical performance of novices on a mammoplasty simulator. We recruited 14 medical students and recorded their performance in 2 skill-games: Mikado and Perplexus Epic, and in 2 video games: Star War Racer (Sony Playstation 3) and Super Monkey Ball 2 (Nintendo Wii). Then, each participant performed an augmentation mammoplasty procedure on a Mammoplasty Part-task Trainer, which allows the simulation of the essential steps of the procedure. The average age of participants was 25.4 years. Correlation studies showed significant association between Perplexus Epic, Star Wars Racer, Super Monkey Ball scores and the modified OSATS score with r s = 0.8491 (p < 0.001), r s = -0.6941 (p = 0.005), and r s = 0.7309 (p < 0.003), but not with the Mikado score r s = -0.0255 (p = 0.9). Linear regressions were strongest for Perplexus Epic and Super Monkey Ball scores with coefficients of determination of 0.59 and 0.55, respectively. A combined score (Perplexus/Super-Monkey-Ball) was computed and showed a significant correlation with the modified OSATS score having an r s = 0.8107 (p < 0.001) and R 2 = 0.75, respectively. This study identified a combination of skill games that correlated to better performance of novices on a surgical simulator. With refinement, such tools could serve to help screen plastic surgery applicants and identify those with higher surgical performance predictors. Copyright © 2018 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.